U.S. patent application number 16/139832 was filed with the patent office on 2021-11-25 for enhanced knowledge delivery and attainment using a question answering system.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Erik D. Anderson, Howard N. Anglin, Anthony J. Arcuri, James C. Palistrant.
Application Number | 20210365524 16/139832 |
Document ID | / |
Family ID | 1000005955204 |
Filed Date | 2021-11-25 |
United States Patent
Application |
20210365524 |
Kind Code |
A9 |
Anderson; Erik D. ; et
al. |
November 25, 2021 |
Enhanced Knowledge Delivery and Attainment Using a Question
Answering System
Abstract
A mechanism is provided in a data processing system for
presentation delivery. The mechanism delivering a presentation
content to a group of users and receives a plurality of questions
concerning the presentation content from the group of users. The
mechanism stores the plurality of questions in a question history
database and clusters the plurality of questions in the question
history database into one or more question clusters. The mechanism
determines a topic for each of the one or more question clusters to
form one or more question topics and generates feedback for
updating the presentation content based on the one or more question
topics.
Inventors: |
Anderson; Erik D.; (Austin,
TX) ; Anglin; Howard N.; (Austin, TX) ;
Arcuri; Anthony J.; (Austin, TX) ; Palistrant; James
C.; (Cary, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20200097598 A1 |
March 26, 2020 |
|
|
Family ID: |
1000005955204 |
Appl. No.: |
16/139832 |
Filed: |
September 24, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14594270 |
Jan 12, 2015 |
10083219 |
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16139832 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G10L 15/22 20130101;
G10L 2015/225 20130101; G06F 16/9038 20190101; G06F 16/3334
20190101; G06K 9/6223 20130101; G06F 16/3329 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G10L 15/22 20060101 G10L015/22; G06K 9/62 20060101
G06K009/62 |
Claims
1. A method, in a data processing system having a processor and a
memory, wherein the memory comprises instructions which are
executed by the processor to cause the processor to implement a
question answering system for presentation delivery, the method
comprising: delivering a presentation content by a presenter to a
group of users; receiving, by the question answering system, a
plurality of questions concerning the presentation content from the
group of users; storing, by the question answering system, question
information for the plurality of questions in a question history
database, wherein the question information comprise question text,
one or more candidate answers, and supplemental information from
the corpus of information providing evidentiary support for the one
or more candidate answers; clustering, by a clustering component
executing within the question answering system, the plurality of
questions in the question history database into one or more
question clusters based on similarity of question features and
answer features; determining, by the question answering system, a
topic for each of the one or more question clusters to form one or
more question topics; generating, by the feedback generating
component, feedback based on the one or more question topics,
wherein the feedback comprises the supplemental information that is
in the question history database but not contained in the
presentation content; and providing by the question answering
system, the feedback to the presenter.
2. The method of claim 1, wherein generating feedback comprises
generating alerts to the presenter to alter the presentation based
on the one or more question topics.
3. The method of claim 1, wherein clustering the plurality of
questions comprises: extracting a set of features from each
question within the plurality of questions, wherein each feature in
the set of features has a feature value; and performing a
clustering operation on the plurality of questions to group
questions having similar feature values.
4. The method of claim 3, wherein performing the clustering
operation on the plurality of questions comprises performing a
k-means clustering algorithm on the plurality of questions.
5. The method of claim 3, wherein the set of features comprise a
lexical answer type, a focus, and a question classification.
6. The method of claim 1, further comprising: for each received
question, generating, by the question answering system, one or more
candidate answers; and presenting, by the question answering
system, a highest confidence answer within the one or more
candidate answers to a user that submitted the received
question.
7. The method of claim 6, wherein presenting the one or more
candidate answers comprises presenting supplemental information not
contained in the presentation content to the user.
8. The method of claim 6, wherein clustering the plurality of
questions comprises: extracting a set of features from each
question within the plurality of questions and each candidate
answer, wherein each feature in the set of features has a feature
value; and performing a clustering operation on the plurality of
questions, wherein the clustering operation groups questions having
similar feature values.
9. The method of claim 1, wherein generating the feedback
comprises: for each question grouped in a given question topic,
incrementing a topic counter; responsive to the topic counter
exceeding a first threshold, generating the feedback identifying
the supplemental information for the given question topic not
contained in the presentation content.
10. The method of claim 9, wherein the supplemental information
comprises evidentiary support for one or more candidate answers
generated for questions in the given question topic.
11. The method of claim 9, wherein generating the feedback further
comprises: responsive to the topic counter exceeding a second
threshold, generating the feedback identifying customized
presentation content redirection the delivery of the presentation
content to the group of users based on the one or more question
clusters.
12. The method of claim 11, wherein the supplemental information
comprises evidentiary support for one or more candidate answers
generated for questions in the given question topic.
13. The method of claim 1, wherein the presentation content is
customized for the group of users based on one or more user
profiles of the group of users.
14. The method of claim 13, further comprising updating the one or
more user profiles based on the one or more question topics.
15. The method of claim 14, wherein generating the feedback
comprises generating the feedback for the group of users based on
the one or more updated user profiles.
16. A computer program product comprising a computer readable
storage medium having a computer readable program stored therein,
wherein the computer readable program, when executed on a computing
device, causes the computing device to implement a question
answering system for presentation delivery, wherein the computer
readable program causes the computing device to: deliver a
presentation content by a presenter to a group of users; receive,
by the question answering system a plurality of questions
concerning the presentation content from the group of users; store,
by the question answering system, question information for the
plurality of questions in a question history database wherein the
question information comprises question text, one or more candidate
answers, and supplemental information from the corpus of
information providing evidentiary support for the one or more
candidate answers; cluster, by a clustering component executing
within the question answering system, the plurality of questions in
the question history database into one or more question clusters;
determine, by the question answering system, a topic for each of
the one or more question clusters to form one or more question
topics; generate, by the question answering system, feedback based
on the one or more question topics, wherein the feedback comprises
the supplemental information that is in the question history
database but not contained in the presentation content; and
provide, by the question answering system, the feedback to the
presenter.
17. The computer program product of claim 16, wherein clustering
the plurality of questions comprises: extracting a set of features
from each question within the plurality of questions, wherein each
feature in the set of features has a feature value; and performing
a clustering operation on the plurality of questions, wherein the
clustering operation groups questions having similar feature
values.
18. The computer program product of claim 16, wherein generating
feedback for updating the presentation content comprises: for each
question grouped in a given question topic, incrementing a topic
counter; responsive to the topic counter exceeding a first
threshold, generating the feedback identifying supplemental
information for the given question topic not contained in the
presentation content.
19. An apparatus comprising: a processor; and a memory coupled to
the processor, wherein the memory comprises instructions which,
when executed by the processor, cause the processor to implement a
question answering system for presentation delivery, wherein the
instructions cause the processor to: deliver a presentation content
by a presenter to a group of users; receive, by the question
answering system, a plurality of questions concerning the
presentation content from the group of users; store, by the
question answering system, question information for the plurality
of questions in a question history database, wherein the question
information comprises question text, one or more candidate answers,
and supplemental information from the corpus of information
providing evidentiary support for the one or more candidate
answers; cluster, by a clustering component executing within the
question answering system, the plurality of questions in the
question history database into one or more question clusters;
determine, by the question answering system, a topic for each of
the one or more question clusters to form one or more question
topics; generate, by the question answering system, feedback based
on the one or more question topics, wherein the feedback comprises
the supplemental information that is in the question history
database but not contained in the presentation content; and
provide, by the question answering system, the feedback to the
presenter.
20. The apparatus of claim 19, wherein clustering the plurality of
questions comprises: extracting a set of features from each
question within the plurality of questions, wherein each feature in
the set of features has a feature value; and performing a
clustering operation on the plurality of questions, wherein the
clustering operation groups questions having similar feature
values.
Description
BACKGROUND
[0001] The present application relates generally to an improved
data processing apparatus and method and more specifically to
mechanisms for enhanced knowledge delivery and attainment using a
question answering system.
[0002] With the increased usage of computing networks, such as the
Internet, humans are currently inundated and overwhelmed with the
amount of information available to them from various structured and
unstructured sources. However, information gaps abound as users try
to piece together what they can find that they believe to be
relevant during searches for information on various subjects. To
assist with such searches, recent research has been directed to
generating Question and Answer (QA) systems which may take an input
question, analyze it, and return results indicative of the most
probable answer to the input question. QA systems provide automated
mechanisms for searching through large sets of sources of content,
e.g., electronic documents, and analyze them with regard to an
input question to determine an answer to the question and a
confidence measure as to how accurate an answer is for answering
the input question.
[0003] Examples, of QA systems are Siri.RTM. from Apple.RTM.,
Cortana.RTM. from Microsoft.RTM., and the IBM Watson.TM. system
available from International Business Machines (IBM.RTM.)
Corporation of Armonk, N.Y. The IBM Watson.TM. system is an
application of advanced natural language processing, information
retrieval, knowledge representation and reasoning, and machine
learning technologies to the field of open domain question
answering. The IBM Watson.TM. system is built on IBM's DeepQA.TM.
technology used for hypothesis generation, massive evidence
gathering, analysis, and scoring. DeepQA.TM. takes an input
question, analyzes it, decomposes the question into constituent
parts, generates one or more hypothesis based on the decomposed
question and results of a primary search of answer sources,
performs hypothesis and evidence scoring based on a retrieval of
evidence from evidence sources, performs synthesis of the one or
more hypothesis, and based on trained models, performs a final
merging and ranking to output an answer to the input question along
with a confidence measure.
SUMMARY
[0004] In one illustrative embodiment, a method, in a data
processing system, is provided for presentation delivery. The
method comprises delivering presentation content to a group of
users and receiving a plurality of questions concerning the
presentation content from the group of users. The method further
comprises storing the plurality of questions in a question history
database and clustering the plurality of questions in the question
history database into one or more question clusters. The method
further comprises determining a topic for each of the one or more
question clusters to form one or more question topics and
generating feedback for updating the presentation content based on
the one or more question topics.
[0005] In other illustrative embodiments, a computer program
product comprising a computer useable or readable medium having a
computer readable program is provided. The computer readable
program, when executed on a computing device, causes the computing
device to perform various ones of, and combinations of, the
operations outlined above with regard to the method illustrative
embodiment.
[0006] In yet another illustrative embodiment, a system/apparatus
is provided. The system/apparatus may comprise one or more
processors and a memory coupled to the one or more processors. The
memory may comprise instructions which, when executed by the one or
more processors, cause the one or more processors to perform
various ones of, and combinations of, the operations outlined above
with regard to the method illustrative embodiment.
[0007] These and other features and advantages of the present
invention will be described in, or will become apparent to those of
ordinary skill in the art in view of, the following detailed
description of the example embodiments of the present
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The invention, as well as a preferred mode of use and
further objectives and advantages thereof, will best be understood
by reference to the following detailed description of illustrative
embodiments when read in conjunction with the accompanying
drawings, wherein:
[0009] FIG. 1 depicts a schematic diagram of one illustrative
embodiment of a question/answer creation (QA) system in a computer
network;
[0010] FIG. 2 is a block diagram of an example data processing
system in which aspects of the illustrative embodiments are
implemented;
[0011] FIG. 3 illustrates a QA system pipeline for processing an
input question in accordance with one illustrative embodiment;
[0012] FIG. 4 is a block diagram of a question answering system for
enhancing knowledge delivery and attainment for delivery of
presentation content in accordance with an illustrative
embodiment;
[0013] FIG. 5 is a flowchart illustrating operation of a mechanism
for enhancing knowledge delivery and attainment for delivery of
presentation content in accordance with an illustrative embodiment;
and
[0014] FIG. 6 is a flowchart illustrating operation of a question
answering system for enhancing knowledge delivery and attainment
for delivery of presentation content in accordance with an
illustrative embodiment.
DETAILED DESCRIPTION
[0015] The illustrative embodiments provide mechanisms for
enhancing knowledge delivery and attainment using a question
answering system. As companies work to reduce travel and training
costs, the number of cases in which it is practical to have an
instructor teach in front of a group of students have been severely
reduced. Organizations and individuals do not want to spend the
time or money to attend an information session at a physical
location if an equivalent alternative is available. Some training
has shifted to distance learning, the online version of a
classroom. In many cases, however, this approach presents
challenges. For global organizations, the range of time zones
sometimes makes it impractical to conduct live training sessions
over the Internet.
[0016] Due to the above and other factors, recorded training
sessions, whether audio only, audio with slides, or video, have
become increasingly more prevalent. A significant downside to the
recorded event, though, is that the element of interaction is lost
in this process. There are currently some methods for increasing
interaction with the recording itself, such as click-able items and
built-in quizzes; however, while these may help from an attention
perspective, they do not actually allow the interaction to occur as
it would in standard instructor-led training.
[0017] For recorded events, help files are necessarily static. Help
files cannot be updated in real time during delivery nor can they
be easily modified to link to new or updated material. Help files
cannot react to audience queries and cannot dynamically adjust to
audience interest to help direct the audience to relevant content.
Help files cannot do data mining, especially in real time, to help
the presenter react if a large portion of the audience begins
asking questions indicating some confusion or need for
clarification. In a recorded setting, help files focus on the
process of finding information rather than the process of gaining
knowledge.
[0018] In accordance with the illustrative embodiments, a question
answering (QA) system provides enriched content- and
context-related assistance for broadcast style or recorded
deliveries of presentation content. The QA system may provide
feedback to a live presenter or a presentation system delivering a
recorded event. The feedback identifies which topics of the
presentation are well-understood by an audience, as well as which
topics require improved or additional content.
[0019] Before beginning the discussion of the various aspects of
the illustrative embodiments in more detail, it should first be
appreciated that throughout this description the term "mechanism"
will be used to refer to elements of the present invention that
perform various operations, functions, and the like. A "mechanism,"
as the term is used herein, may be an implementation of the
functions or aspects of the illustrative embodiments in the form of
an apparatus, a procedure, or a computer program product. In the
case of a procedure, the procedure is implemented by one or more
devices, apparatus, computers, data processing systems, or the
like. In the case of a computer program product, the logic
represented by computer code or instructions embodied in or on the
computer program product is executed by one or more hardware
devices in order to implement the functionality or perform the
operations associated with the specific "mechanism." Thus, the
mechanisms described herein may be implemented as specialized
hardware, software executing on general purpose hardware, software
instructions stored on a medium such that the instructions are
readily executable by specialized or general purpose hardware, a
procedure or method for executing the functions, or a combination
of any of the above.
[0020] The present description and claims may make use of the terms
"a", "at least one of", and "one or more of" with regard to
particular features and elements of the illustrative embodiments.
It should be appreciated that these terms and phrases are intended
to state that there is at least one of the particular feature or
element present in the particular illustrative embodiment, but that
more than one can also be present. That is, these terms/phrases are
not intended to limit the description or claims to a single
feature/element being present or require that a plurality of such
features/elements be present. To the contrary, these terms/phrases
only require at least a single feature/element with the possibility
of a plurality of such features/elements being within the scope of
the description and claims.
[0021] In addition, it should be appreciated that the following
description uses a plurality of various examples for various
elements of the illustrative embodiments to further illustrate
example implementations of the illustrative embodiments and to aid
in the understanding of the mechanisms of the illustrative
embodiments. These examples intended to be non-limiting and are not
exhaustive of the various possibilities for implementing the
mechanisms of the illustrative embodiments. It will be apparent to
those of ordinary skill in the art in view of the present
description that there are many other alternative implementations
for these various elements that may be utilized in addition to, or
in replacement of, the examples provided herein without departing
from the spirit and scope of the present invention.
[0022] The illustrative embodiments may be utilized in many
different types of data processing environments. In order to
provide a context for the description of the specific elements and
functionality of the illustrative embodiments, FIGS. 1-3 are
provided hereafter as example environments in which aspects of the
illustrative embodiments may be implemented. It should be
appreciated that FIGS. 1-3 are only examples and are not intended
to assert or imply any limitation with regard to the environments
in which aspects or embodiments of the present invention may be
implemented. Many modifications to the depicted environments may be
made without departing from the spirit and scope of the present
invention.
[0023] FIGS. 1-3 are directed to describing an example Question
Answering (QA) system (also referred to as a Question/Answer system
or Question and Answer system), methodology, and computer program
product with which the mechanisms of the illustrative embodiments
are implemented. As will be discussed in greater detail hereafter,
the illustrative embodiments are integrated in, augment, and extend
the functionality of these QA mechanisms with regard to enhancing
knowledge delivery and attainment for live or recorded delivery of
presentation content.
[0024] Thus, it is important to first have an understanding of how
question and answer creation in a QA system is implemented before
describing how the mechanisms of the illustrative embodiments are
integrated in and augment such QA systems. It should be appreciated
that the QA mechanisms described in FIGS. 1-3 are only examples and
are not intended to state or imply any limitation with regard to
the type of QA mechanisms with which the illustrative embodiments
are implemented. Many modifications to the example QA system shown
in FIGS. 1-3 may be implemented in various embodiments of the
present invention without departing from the spirit and scope of
the present invention.
[0025] As an overview, a Question Answering system (QA system) is
an artificial intelligence application executing on data processing
hardware that answers questions pertaining to a given
subject-matter domain presented in natural language. The QA system
receives inputs from various sources including input over a
network, a corpus of electronic documents or other data, data from
a content creator, information from one or more content users, and
other such inputs from other possible sources of input. Data
storage devices store the corpus of data. A content creator creates
content in a document for use as part of a corpus of data with the
QA system. The document may include any file, text, article, or
source of data for use in the QA system. For example, a QA system
accesses a body of knowledge about the domain, or subject matter
area, e.g., financial domain, medical domain, legal domain, etc.,
where the body of knowledge (knowledgebase) can be organized in a
variety of configurations, e.g., a structured repository of
domain-specific information, such as ontologies, or unstructured
data related to the domain, or a collection of natural language
documents about the domain.
[0026] Content users input questions to the QA system which then
answers the input questions using the content in the corpus of data
by evaluating documents, sections of documents, portions of data in
the corpus, or the like. When a process evaluates a given section
of a document for semantic content, the process can use a variety
of conventions to query such document from the QA system, e.g.,
sending the query to the QA system as a well-formed question which
are then interpreted by the QA system and a response is provided
containing one or more answers to the question. Semantic content is
content based on the relation between signifiers, such as words,
phrases, signs, and symbols, and what they stand for, their
denotation, or connotation. In other words, semantic content is
content that interprets an expression, such as by using Natural
Language Processing.
[0027] As will be described in greater detail hereafter, the QA
system receives an input question, parses the question to extract
the major features of the question, uses the extracted features to
formulate queries, and then applies those queries to the corpus of
data. Based on the application of the queries to the corpus of
data, the QA system generates a set of hypotheses, or candidate
answers to the input question, by looking across the corpus of data
for portions of the corpus of data that have some potential for
containing a valuable response to the input question. The QA system
then performs deep analysis on the language of the input question
and the language used in each of the portions of the corpus of data
found during the application of the queries using a variety of
reasoning algorithms. There may be hundreds or even thousands of
reasoning algorithms applied, each of which performs different
analysis, e.g., comparisons, natural language analysis, lexical
analysis, or the like, and generates a score. For example, some
reasoning algorithms may look at the matching of terms and synonyms
within the language of the input question and the found portions of
the corpus of data. Other reasoning algorithms may look at temporal
or spatial features in the language, while others may evaluate the
source of the portion of the corpus of data and evaluate its
veracity.
[0028] The scores obtained from the various reasoning algorithms
indicate the extent to which the potential response is inferred by
the input question based on the specific area of focus of that
reasoning algorithm. Each resulting score is then weighted against
a statistical model. The statistical model captures how well the
reasoning algorithm performed at establishing the inference between
two similar passages for a particular domain during the training
period of the QA system. The statistical model is used to summarize
a level of confidence that the QA system has regarding the evidence
that the potential response, i.e. candidate answer, is inferred by
the question. This process is repeated for each of the candidate
answers until the QA system identifies candidate answers that
surface as being significantly stronger than others and thus,
generates a final answer, or ranked set of answers, for the input
question.
[0029] As mentioned above, QA systems and mechanisms operate by
accessing information from a corpus of data or information (also
referred to as a corpus of content), analyzing it, and then
generating answer results based on the analysis of this data.
Accessing information from a corpus of data typically includes: a
database query that answers questions about what is in a collection
of structured records, and a search that delivers a collection of
document links in response to a query against a collection of
unstructured data (text, markup language, etc.). Conventional
question answering systems are capable of generating answers based
on the corpus of data and the input question, verifying answers to
a collection of questions for the corpus of data, correcting errors
in digital text using a corpus of data, and selecting answers to
questions from a pool of potential answers, i.e. candidate
answers.
[0030] Content creators, such as article authors, electronic
document creators, web page authors, document database creators,
and the like, determine use cases for products, solutions, and
services described in such content before writing their content.
Consequently, the content creators know what questions the content
is intended to answer in a particular topic addressed by the
content. Categorizing the questions, such as in terms of roles,
type of information, tasks, or the like, associated with the
question, in each document of a corpus of data allows the QA system
to more quickly and efficiently identify documents containing
content related to a specific query. The content may also answer
other questions that the content creator did not contemplate that
may be useful to content users. The questions and answers may be
verified by the content creator to be contained in the content for
a given document. These capabilities contribute to improved
accuracy, system performance, machine learning, and confidence of
the QA system. Content creators, automated tools, or the like,
annotate or otherwise generate metadata for providing information
useable by the QA system to identify these question and answer
attributes of the content.
[0031] Operating on such content, the QA system generates answers
for input questions using a plurality of intensive analysis
mechanisms which evaluate the content to identify the most probable
answers, i.e. candidate answers, for the input question. The most
probable answers are output as a ranked listing of candidate
answers ranked according to their relative scores or confidence
measures calculated during evaluation of the candidate answers, as
a single final answer having a highest ranking score or confidence
measure, or which is a best match to the input question, or a
combination of ranked listing and final answer.
[0032] FIG. 1 depicts a schematic diagram of one illustrative
embodiment of a question/answer creation (QA) system 100 in a
computer network 102. One example of a question/answer generation
which may be used in conjunction with the principles described
herein is described in U.S. Patent Application Publication No.
2011/0125734, which is herein incorporated by reference in its
entirety. The QA system 100 is implemented on one or more computing
devices 104 (comprising one or more processors and one or more
memories, and potentially any other computing device elements
generally known in the art including buses, storage devices,
communication interfaces, and the like) connected to the computer
network 102. The network 102 includes multiple computing devices
104 in communication with each other and with other devices or
components via one or more wired and/or wireless data communication
links, where each communication link comprises one or more of
wires, routers, switches, transmitters, receivers, or the like. The
QA system 100 and network 102 enables question/answer (QA)
generation functionality for one or more QA system users via their
respective computing devices 110-112. Other embodiments of the QA
system 100 may be used with components, systems, sub-systems,
and/or devices other than those that are depicted herein.
[0033] The QA system 100 is configured to implement a QA system
pipeline 108 that receive inputs from various sources. For example,
the QA system 100 receives input from the network 102, a corpus of
electronic documents 106, QA system users, and/or other data and
other possible sources of input. In one embodiment, some or all of
the inputs to the QA system 100 are routed through the network 102.
The various computing devices 104 on the network 102 include access
points for content creators and QA system users. Some of the
computing devices 104 include devices for a database storing the
corpus of data 106 (which is shown as a separate entity in FIG. 1
for illustrative purposes only). Portions of the corpus of data 106
may also be provided on one or more other network attached storage
devices, in one or more databases, or other computing devices not
explicitly shown in FIG. 1. The network 102 includes local network
connections and remote connections in various embodiments, such
that the QA system 100 may operate in environments of any size,
including local and global, e.g., the Internet.
[0034] In one embodiment, the content creator creates content in a
document of the corpus of data 106 for use as part of a corpus of
data with the QA system 100. The document includes any file, text,
article, or source of data for use in the QA system 100. QA system
users access the QA system 100 via a network connection or an
Internet connection to the network 102, and input questions to the
QA system 100 that are answered by the content in the corpus of
data 106. In one embodiment, the questions are formed using natural
language. The QA system 100 parses and interprets the question, and
provides a response to the QA system user, e.g., QA system user
110, containing one or more answers to the question. In some
embodiments, the QA system 100 provides a response to users in a
ranked list of candidate answers while in other illustrative
embodiments, the QA system 100 provides a single final answer or a
combination of a final answer and ranked listing of other candidate
answers.
[0035] The QA system 100 implements a QA system pipeline 108 which
comprises a plurality of stages for processing an input question
and the corpus of data 106. The QA system pipeline 108 generates
answers for the input question based on the processing of the input
question and the corpus of data 106. The QA system pipeline 108
will be described in greater detail hereafter with regard to FIG.
3.
[0036] In some illustrative embodiments, the QA system 100 may be
the IBM Watson.TM. QA system available from International Business
Machines Corporation of Armonk, N.Y., which is augmented with the
mechanisms of the illustrative embodiments described hereafter. As
outlined previously, the IBM Watson.TM. QA system receives an input
question which it then parses to extract the major features of the
question, that in turn are then used to formulate queries that are
applied to the corpus of data. Based on the application of the
queries to the corpus of data, a set of hypotheses, or candidate
answers to the input question, are generated by looking across the
corpus of data for portions of the corpus of data that have some
potential for containing a valuable response to the input question.
The IBM Watson.TM. QA system then performs deep analysis on the
language of the input question and the language used in each of the
portions of the corpus of data found during the application of the
queries using a variety of reasoning algorithms. The scores
obtained from the various reasoning algorithms are then weighted
against a statistical model that summarizes a level of confidence
that the IBM Watson.TM. QA system has regarding the evidence that
the potential response, i.e. candidate answer, is inferred by the
question. This process is be repeated for each of the candidate
answers to generate ranked listing of candidate answers which may
then be presented to the user that submitted the input question, or
from which a final answer is selected and presented to the user.
More information about the IBM Watson.TM. QA system may be
obtained, for example, from the IBM Corporation website, IBM
Redbooks, and the like. For example, information about the IBM
Watson.TM. QA system can be found in Yuan et al., "Watson and
Healthcare," IBM developerWorks, 2011 and "The Era of Cognitive
Systems: An Inside Look at IBM Watson and How it Works" by Rob
High, IBM Redbooks, 2012.
[0037] In accordance with an illustrative embodiment, a
presentation system delivers live or recorded presentation content
from a server 104 to users at clients 110, 112. QA system users at
clients 110,112 interact with QA system 100 during the presentation
to ask questions, or simply make comments, about the presentation
to enhance the presentation content without necessarily disrupting
the presentation flow. Users may interact with QA system 100 in a
manner similar to a chat session. In alternative embodiments, QA
system 100 may receive questions by voice or video messages. QA
system 100 provides answers to questions and/or supplemental
content not included in the presentation content within the scope
of the presented topic.
[0038] FIG. 2 is a block diagram of an example data processing
system in which aspects of the illustrative embodiments are
implemented. Data processing system 200 is an example of a
computer, such as server 104 or client 110 in FIG. 1, in which
computer usable code or instructions implementing the processes for
illustrative embodiments of the present invention are located. In
one illustrative embodiment, FIG. 2 represents a server computing
device, such as a server 104, which, which implements a QA system
100 and QA system pipeline 108 augmented to include the additional
mechanisms of the illustrative embodiments described hereafter.
[0039] In the depicted example, data processing system 200 employs
a hub architecture including north bridge and memory controller hub
(NB/MCH) 202 and south bridge and input/output (I/O) controller hub
(SB/ICH) 204. Processing unit 206, main memory 208, and graphics
processor 210 are connected to NB/MCH 202. Graphics processor 210
is connected to NB/MCH 202 through an accelerated graphics port
(AGP).
[0040] In the depicted example, local area network (LAN) adapter
212 connects to SB/ICH 204. Audio adapter 216, keyboard and mouse
adapter 220, modem 222, read only memory (ROM) 224, hard disk drive
(HDD) 226, CD-ROM drive 230, universal serial bus (USB) ports and
other communication ports 232, and PCI/PCIe devices 234 connect to
SB/ICH 204 through bus 238 and bus 240. PCI/PCIe devices may
include, for example, Ethernet adapters, add-in cards, and PC cards
for notebook computers. PCI uses a card bus controller, while PCIe
does not. ROM 224 may be, for example, a flash basic input/output
system (BIOS).
[0041] HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through
bus 240. HDD 226 and CD-ROM drive 230 may use, for example, an
integrated drive electronics (IDE) or serial advanced technology
attachment (SATA) interface. Super I/O (SIO) device 236 is
connected to SB/ICH 204.
[0042] An operating system runs on processing unit 206. The
operating system coordinates and provides control of various
components within the data processing system 200 in FIG. 2. As a
client, the operating system is a commercially available operating
system such as Microsoft.RTM. Windows 8.RTM.. An object-oriented
programming system, such as the Java.TM. programming system, may
run in conjunction with the operating system and provides calls to
the operating system from Java.TM. programs or applications
executing on data processing system 200.
[0043] As a server, data processing system 200 may be, for example,
an IBM.RTM. eServer.TM. System P.RTM. computer system, running the
Advanced Interactive Executive (AIX.RTM.) operating system or the
LINUX.RTM. operating system. Data processing system 200 may be a
symmetric multiprocessor (SMP) system including a plurality of
processors in processing unit 206. Alternatively, a single
processor system may be employed.
[0044] Instructions for the operating system, the object-oriented
programming system, and applications or programs are located on
storage devices, such as HDD 226, and are loaded into main memory
208 for execution by processing unit 206. The processes for
illustrative embodiments of the present invention are performed by
processing unit 206 using computer usable program code, which is
located in a memory such as, for example, main memory 208, ROM 224,
or in one or more peripheral devices 226 and 230, for example.
[0045] A bus system, such as bus 238 or bus 240 as shown in FIG. 2,
is comprised of one or more buses. Of course, the bus system may be
implemented using any type of communication fabric or architecture
that provides for a transfer of data between different components
or devices attached to the fabric or architecture. A communication
unit, such as modem 222 or network adapter 212 of FIG. 2, includes
one or more devices used to transmit and receive data. A memory may
be, for example, main memory 208, ROM 224, or a cache such as found
in NB/MCH 202 in FIG. 2.
[0046] Those of ordinary skill in the art will appreciate that the
hardware depicted in FIGS. 1 and 2 may vary depending on the
implementation. Other internal hardware or peripheral devices, such
as flash memory, equivalent non-volatile memory, or optical disk
drives and the like, may be used in addition to or in place of the
hardware depicted in FIGS. 1 and 2. Also, the processes of the
illustrative embodiments may be applied to a multiprocessor data
processing system, other than the SMP system mentioned previously,
without departing from the spirit and scope of the present
invention.
[0047] Moreover, the data processing system 200 may take the form
of any of a number of different data processing systems including
client computing devices, server computing devices, a tablet
computer, laptop computer, telephone or other communication device,
a personal digital assistant (PDA), or the like. In some
illustrative examples, data processing system 200 may be a portable
computing device that is configured with flash memory to provide
non-volatile memory for storing operating system files and/or
user-generated data, for example. Essentially, data processing
system 200 may be any known or later developed data processing
system without architectural limitation.
[0048] FIG. 3 illustrates a QA system pipeline for processing an
input question in accordance with one illustrative embodiment. The
QA system pipeline of FIG. 3 may be implemented, for example, as QA
system pipeline 108 of QA system 100 in FIG. 1. It should be
appreciated that the stages of the QA system pipeline shown in FIG.
3 are implemented as one or more software engines, components, or
the like, which are configured with logic for implementing the
functionality attributed to the particular stage. Each stage is
implemented using one or more of such software engines, components
or the like. The software engines, components, etc. are executed on
one or more processors of one or more data processing systems or
devices and utilize or operate on data stored in one or more data
storage devices, memories, or the like, on one or more of the data
processing systems. The QA system pipeline of FIG. 3 is augmented,
for example, in one or more of the stages to implement the improved
mechanism of the illustrative embodiments described hereafter,
additional stages may be provided to implement the improved
mechanism, or separate logic from the pipeline 300 may be provided
for interfacing with the pipeline 300 and implementing the improved
functionality and operations of the illustrative embodiments.
[0049] As shown in FIG. 3, the QA system pipeline 300 comprises a
plurality of stages 310-380 through which the QA system operates to
analyze an input question and generate a final response. In an
initial question input stage 310, the QA system receives an input
question that is presented in a natural language format. That is, a
user inputs, via a user interface, an input question for which the
user wishes to obtain an answer, e.g., "Who are Washington's
closest advisors?" In response to receiving the input question, the
next stage of the QA system pipeline 300, i.e. the question and
topic analysis stage 320, parses the input question using natural
language processing (NLP) techniques to extract major features from
the input question, and classify the major features according to
types, e.g., names, dates, or any of a plethora of other defined
topics. For example, in the example question above, the term "who"
may be associated with a topic for "persons" indicating that the
identity of a person is being sought, "Washington" may be
identified as a proper name of a person with which the question is
associated, "closest" may be identified as a word indicative of
proximity or relationship, and "advisors" may be indicative of a
noun or other language topic.
[0050] In addition, the extracted major features include key words
and phrases classified into question characteristics, such as the
focus of the question, the lexical answer type (LAT) of the
question, and the like. As referred to herein, a lexical answer
type (LAT) is a word in, or a word inferred from, the input
question that indicates the type of the answer, independent of
assigning semantics to that word. For example, in the question
"What maneuver was invented in the 1500s to speed up the game and
involves two pieces of the same color?," the LAT is the string
"maneuver." The focus of a question is the part of the question
that, if replaced by the answer, makes the question a standalone
statement. For example, in the question "What drug has been shown
to relieve the symptoms of ADD with relatively few side effects?,"
the focus is "drug" since if this word were replaced with the
answer, e.g., the answer "Adderall" can be used to replace the term
"drug" to generate the sentence "Adderall has been shown to relieve
the symptoms of ADD with relatively few side effects." The focus
often, but not always, contains the LAT. On the other hand, in many
cases it is not possible to infer a meaningful LAT from the
focus.
[0051] Referring again to FIG. 3, the identified major features are
then used during the question decomposition stage 330 to decompose
the question into one or more queries that are applied to the
corpora of data/information 345 in order to generate one or more
hypotheses. The queries are generated in any known or later
developed query language, such as the Structure Query Language
(SQL), or the like. The queries are applied to one or more
databases storing information about the electronic texts,
documents, articles, websites, and the like, that make up the
corpora of data/information 345. That is, these various sources
themselves, different collections of sources, and the like,
represent a different corpus 347 within the corpora 345. There may
be different corpora 347 defined for different collections of
documents based on various criteria depending upon the particular
implementation. For example, different corpora may be established
for different topics, subject matter categories, sources of
information, or the like. As one example, a first corpus may be
associated with healthcare documents while a second corpus may be
associated with financial documents. Alternatively, one corpus may
be documents published by the U.S. Department of Energy while
another corpus may be IBM Redbooks documents. Any collection of
content having some similar attribute may be considered to be a
corpus 347 within the corpora 345.
[0052] The queries are applied to one or more databases storing
information about the electronic texts, documents, articles,
websites, and the like, that make up the corpus of
data/information, e.g., the corpus of data 106 in FIG. 1. The
queries are applied to the corpus of data/information at the
hypothesis generation stage 340 to generate results identifying
potential hypotheses for answering the input question, which can
then be evaluated. That is, the application of the queries results
in the extraction of portions of the corpus of data/information
matching the criteria of the particular query. These portions of
the corpus are then analyzed and used, during the hypothesis
generation stage 340, to generate hypotheses for answering the
input question. These hypotheses are also referred to herein as
"candidate answers" for the input question. For any input question,
at this stage 340, there may be hundreds of hypotheses or candidate
answers generated that may need to be evaluated.
[0053] The QA system pipeline 300, in stage 350, then performs a
deep analysis and comparison of the language of the input question
and the language of each hypothesis or "candidate answer," as well
as performs evidence scoring to evaluate the likelihood that the
particular hypothesis is a correct answer for the input question.
As mentioned above, this involves using a plurality of reasoning
algorithms, each performing a separate type of analysis of the
language of the input question and/or content of the corpus that
provides evidence in support of, or not in support of, the
hypothesis. Each reasoning algorithm generates a score based on the
analysis it performs which indicates a measure of relevance of the
individual portions of the corpus of data/information extracted by
application of the queries as well as a measure of the correctness
of the corresponding hypothesis, i.e. a measure of confidence in
the hypothesis. There are various ways of generating such scores
depending upon the particular analysis being performed. In
generally, however, these algorithms look for particular terms,
phrases, or patterns of text that are indicative of terms, phrases,
or patterns of interest and determine a degree of matching with
higher degrees of matching being given relatively higher scores
than lower degrees of matching.
[0054] Thus, for example, an algorithm may be configured to look
for the exact term from an input question or synonyms to that term
in the input question, e.g., the exact term or synonyms for the
term "movie," and generate a score based on a frequency of use of
these exact terms or synonyms. In such a case, exact matches will
be given the highest scores, while synonyms may be given lower
scores based on a relative ranking of the synonyms as may be
specified by a subject matter expert (person with knowledge of the
particular domain and terminology used) or automatically determined
from frequency of use of the synonym in the corpus corresponding to
the domain. Thus, for example, an exact match of the term "movie"
in content of the corpus (also referred to as evidence, or evidence
passages) is given a highest score. A synonym of movie, such as
"motion picture" may be given a lower score but still higher than a
synonym of the type "film" or "moving picture show." Instances of
the exact matches and synonyms for each evidence passage may be
compiled and used in a quantitative function to generate a score
for the degree of matching of the evidence passage to the input
question.
[0055] Thus, for example, a hypothesis or candidate answer to the
input question of "What was the first movie?" is "The Horse in
Motion." If the evidence passage contains the statements "The first
motion picture ever made was `The Horse in Motion` in 1878 by
Eadweard Muybridge. It was a movie of a horse running," and the
algorithm is looking for exact matches or synonyms to the focus of
the input question, i.e. "movie," then an exact match of "movie" is
found in the second sentence of the evidence passage and a highly
scored synonym to "movie," i.e. "motion picture," is found in the
first sentence of the evidence passage. This may be combined with
further analysis of the evidence passage to identify that the text
of the candidate answer is present in the evidence passage as well,
i.e. "The Horse in Motion." These factors may be combined to give
this evidence passage a relatively high score as supporting
evidence for the candidate answer "The Horse in Motion" being a
correct answer.
[0056] It should be appreciated that this is just one simple
example of how scoring can be performed. Many other algorithms of
various complexity may be used to generate scores for candidate
answers and evidence without departing from the spirit and scope of
the present invention.
[0057] In the synthesis stage 360, the large number of scores
generated by the various reasoning algorithms are synthesized into
confidence scores or confidence measures for the various
hypotheses. This process involves applying weights to the various
scores, where the weights have been determined through training of
the statistical model employed by the QA system and/or dynamically
updated. For example, the weights for scores generated by
algorithms that identify exactly matching terms and synonym may be
set relatively higher than other algorithms that are evaluating
publication dates for evidence passages. The weights themselves may
be specified by subject matter experts or learned through machine
learning processes that evaluate the significance of
characteristics evidence passages and their relative importance to
overall candidate answer generation.
[0058] The weighted scores are processed in accordance with a
statistical model generated through training of the QA system that
identifies a manner by which these scores may be combined to
generate a confidence score or measure for the individual
hypotheses or candidate answers. This confidence score or measure
summarizes the level of confidence that the QA system has about the
evidence that the candidate answer is inferred by the input
question, i.e. that the candidate answer is the correct answer for
the input question.
[0059] The resulting confidence scores or measures are processed by
a final confidence merging and ranking stage 370 which compares the
confidence scores and measures to each other, compares them against
predetermined thresholds, or performs any other analysis on the
confidence scores to determine which hypotheses/candidate answers
are the most likely to be the correct answer to the input question.
The hypotheses/candidate answers are ranked according to these
comparisons to generate a ranked listing of hypotheses/candidate
answers (hereafter simply referred to as "candidate answers"). From
the ranked listing of candidate answers, at stage 380, a final
answer and confidence score, or final set of candidate answers and
confidence scores, are generated and output to the submitter of the
original input question via a graphical user interface or other
mechanism for outputting information.
[0060] FIG. 4 is a block diagram of a question answering system for
enhancing knowledge delivery and attainment for delivery of
presentation content in accordance with an illustrative embodiment.
A presenter or presentation system delivers presentation content
403 to a group of users. Question answering (QA) system 410
receives questions 401 from the group of users who are viewing or
listening to a live or recorded presentation. QA system 410
provides answers to questions 401 in the form of supplemental
information 416 based on information in corpus 404 and, in one
embodiment, presentation content 403. QA system 410 may return
supplemental information 416 to the users asking the question.
[0061] QA system 410 may access presentation content 403 by
interfacing with a presentation system. Corpus 404 may also include
information from training oriented material from internal and
possibly external sites depending on the presentation topic. The
training oriented material may include whitepapers, presentations,
etc. QA system 410 may actively listen to live or recorded
presentations. QA system 410 may be connected in the background
with context to any video or audio prompts that may be relevant
shortly before a question is asked.
[0062] QA system 410 includes reasoning algorithm (RA) pipeline
411, which generates question features 412 from questions 401 and
answer features 413 from candidate answers generated from corpus
404 and/or presentation content 403. Question features 412 may
include, for example, Lexical Answer Type (LAT), focus, question
classification, etc. Answer features 413 may include, for example,
sources of evidentiary support for answers, answer confidence, etc.
QA system 410 stores question information in question history
database (DB) 405. The question information may include the
question text, question features, high confidence answers, sources
of evidentiary support, identification of the user and/or user
group, etc.
[0063] Clustering component 414 performs clustering on received
questions 401, as well as questions in question history DB 405.
Cluster analysis or clustering is the task of grouping a set of
objects in such a way that objects in the same group (i.e., a
cluster) are more similar in some sense or another to each other
than to those in other groups (clusters). Clustering is a main task
of exploratory data mining, and a common technique for statistical
data analysis, used in many fields, including machine learning,
pattern recognition, image analysis, information retrieval, and
bioinformatics.
[0064] In one example embodiment, clustering component 414 uses
k-means clustering. The k-means clustering algorithm is a technique
of vector quantization, originally from signal processing, that is
popular for cluster analysis in data mining. The k-means clustering
algorithm aims to partition n observations into k clusters in which
each observation belongs to the cluster with the nearest mean,
serving as a prototype of the cluster.
[0065] Clustering component 414 groups questions into question
clusters based on similarity of question features 412 and answer
features 413. In one example embodiment, clustering component 414
stores information about question clusters in question history DB
405. In one embodiment, QA system 410 uses capabilities of RA
pipeline 411 to identify a topic of the questions. For example,
consider a presentation about changes to health care law. Users
viewing the presentation may ask questions about the subject matter
being presented. One or two users may ask about a particular
state's insurance exchange, but many users may ask about children's
dental coverage. In this example, clustering component 414 clusters
the questions about the topic of "state-run insurance exchange" in
one cluster and the questions about "children's dental coverage" in
another cluster.
[0066] Feedback generating component 415 generates presentation
feedback 417 based on the question clusters determined by
clustering component 414. Feedback generating component 415
provides presentation feedback 417 to the presenter or presentation
system.
[0067] In one embodiment, feedback generating component 415
generates presentation feedback 417 according to a number of
questions in each cluster. That is for questions that have been
asked only a few times, feedback generating component 415 may
simply provide supplemental information 416 to the users asking the
questions without interrupting or modifying the presentation.
However, in response to a number of questions in a cluster
exceeding a first threshold, feedback generating component 415
generates feedback for updating future presentation content to
include supplemental information 416 for the given question topic
not contained in the presentation content 403. In response to the
number of questions in the cluster exceeding a second threshold,
feedback generating component 415 generates feedback for updating
current presentation content 403 to include supplemental
information 416 for the given question topic not contained in the
presentation content 403.
[0068] In an example embodiment, feedback generating component 415
accesses presentation content 403 to determine whether supplemental
information 416 is present in presentation content 403. If feedback
generating component 415 determines that a cluster of questions
will be answered later in the presentation, then feedback
generating component 415 does not update current or future
presentation content. However, if feedback generating component 415
determines that users are asking questions for which presentation
content 403 does not provide support, then feedback generating
component 415 generates presentation feedback 417 to update
presentation content 403.
[0069] In an illustrative embodiment, users or groups of users
provide user profiles 402. The creator of the presentation may
customize presentation content 403 for a particular group of users
based on information in user profiles 402. That is, the creator of
presentation content 403 may determine that a group of users have
sufficient understanding of some topics and need deeper knowledge
for other topics based on user profiles 402.
[0070] As QA system 410 receives questions 401 and clustering
component 414 groups questions 401 into clusters, feedback
generating component 415 may update user profiles 402. That is, if
users begin asking a high number of questions about a particular
topic, feedback generating component 415 may determine that the
group of users require more information about that topic. Thus,
feedback generating component 415 updates user profiles 402 to
indicate that the group of users require more information about the
identified topic. In an alternative embodiment, QA system 410
ingests profile information from user profiles 402 and stores the
information in question history DB 405.
[0071] The features of QA system 410, as described above with
respect to FIG. 4, may be used in many ways. In most live broadcast
presentations, there is a concurrent chat session for the audience
to post questions and for the conference moderator to post
information. Leveraging this interface, QA system 410 would act in
the moderator role providing faster answers to questions and
optionally providing reference links for additional related
content, such as whitepapers, presentations, videos, or other
content on a relevant topic. This reference information could be
provided in a verbose chat session or accumulated and later posted
with the content of the live presentation.
[0072] QA system 410 could discretely provide feedback to the
presenter to assist with positioning or delivery of the
presentation content. Chat comments may provide insight that the
audience needs more than explanation or that there is a strong
interest in subject matter that was not planned to receive
significant attention. QA system 410 could provide cues to the
presenter along with recommended content to redirect the
presentation accordingly. The presenter may still elect to stay the
course of the presentation or may lightly or heavily leverage the
recommended content to better satisfy the audience based on the
level of interest, based on similar questions or a quick poll.
[0073] QA system 410 may redirect the flow of a presentation based
on a weighted average of the expertise of the current audience or
from the types of questions being asked. QA system 410 receives
input from the audience of a replay of a presentation and may
control the presentation itself.
[0074] During replay of a presentation, QA system 410 may harvest
questions from viewers and provide answers and references as
appropriate that would enhance the value of the answer. As with a
live presentation with a chat session, a context sensitive chat
environment with QA system 410 can be leveraged to enable better
relevancy for the presumably smaller audience.
[0075] QA system 410 may allow the replay of presentation content
to flow differently based on the types of questions asked in a chat
session. QA system 410 would answer new chat questions even for a
replayed session. An individual or smaller group of users watching
a replay may have different needs than the larger audience that
attended the live session. For example, someone who participated in
the live session may want to build deeper insights on a specific
set of topics. Providing keywords or tags to the QS system 410 may
trigger a customized abbreviated view of the material and cause new
recommended content to be suggested for further study.
[0076] QA system 410 may allow viewers of a replay to choose one's
own custom session based on current knowledge level. An individual
could leverage the replay session as part of a broader knowledge
need. QA system 410 would utilize assets and integrate them to
provide a set of information and recommended approach to study,
serving up information as a high level overview, building block
approach, lecture series, or another appropriate structured
format.
[0077] QA system 410 may conduct a pre-presentation survey of
invitees to better determine the content or how the presentation
should flow. Typically, presentation material is a small subset of
the available presentation content in order that the presentation
material fits into a certain time format, for example in a one-hour
time slot. At least a few days prior to the live presentation, QA
system 410 would collect brief survey information from the
prospective audience to help the presenter tailor the presentation
or be better prepared for some deeper questions that may arise.
[0078] QA system 410 may provide auto-generated content (e.g.,
presentation, help file, video) of information based on initial
topic and questions asked during replay. In the absence of known
existing presentations, a user can initiate an education advisor
interface providing a topic set or short curriculum and receive a
structured reference to assets to methodically leverage content to
build knowledge. Building a worthwhile recommendation would likely
require a conversational interface that collects details about
scope, depth, time focus, and calendar time, as well as other
parameters to support the objective.
[0079] A product delivered with a presentation and QA system 410
can replace an installation or configuration guide. Such a
presentation could be interactive based on metadata or learned
experience of a user. Instead of step-by-step instructions on
paper, QA system 410 can generate a set of dynamic content
delivered to a smartphone, tablet, or similar device that would
include up-to-date detailed instructions utilizing demonstration
videos, pictures and diagrams, audio delivered content, and helpful
references for more information. This content would be device and
data bandwidth appropriate.
[0080] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0081] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0082] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0083] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0084] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0085] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0086] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0087] FIG. 5 is a flowchart illustrating operation of a mechanism
for enhancing knowledge delivery and attainment for delivery of
presentation content in accordance with an illustrative embodiment.
Operation begins (block 500), and the mechanism presents content
(block 501). The mechanism may present the content as a live
content broadcast (block 502) or a replay of a content broadcast
(block 503). Thereafter, the mechanism obtains user profiles, which
specify user interests (block 504).
[0088] Then, the mechanism determines whether a question is
received (block 505). If the mechanism does not receive a question,
the mechanism determines whether the presentation is finished
(block 506). If the presentation is not finished, operation returns
to block 501 to present content.
[0089] If the mechanism receives a question in block 505, the
mechanism processes the question using a question answering system
(block 507). Operation of a question answering system is described
in detail with reference to FIGS. 1-3. Next, the mechanism updates
a knowledge base (block 508). The knowledge base may comprise
question history DB 405 and cluster information generated by
clustering component 414 in FIG. 4. In one embodiment, updating the
knowledge base may comprise updating user profiles in block
504.
[0090] The mechanism then provides feedback to the presenter or
presentation system (block 509). In one embodiment, the feedback
includes recommendations to update presentation content for the
current or future presentations. The mechanism also provides
supplemental information to the user asking the question (block
510).
[0091] Thereafter, the mechanism determines whether the
presentation is finished (block 506). If the mechanism determines
the presentation is not finished, operation returns to block 501 to
present content. If the mechanism determines the presentation is
finished in block 506, then operation ends (block 511).
[0092] FIG. 6 is a flowchart illustrating operation of a question
answering system for enhancing knowledge delivery and attainment
for delivery of presentation content in accordance with an
illustrative embodiment. Operation begins (block 600), and the QA
system determines whether a question is received (block 601). If
the QA system determines that a question is not received, the QA
system determines whether the presentation is finished (block 602).
If the presentation is not finished, operation returns to block 601
to determine whether a question is received. If the QA system
determines the presentation is finished in block 602, then
operation ends (block 603).
[0093] If the QA system receives a question in block 601, then the
QA system extracts features from the question (block 604). The QA
system determines candidate answers to the question (block 605) and
extracts features from the answers and source material (block 606).
The QA system clusters the question with a question history (block
607) and determines a topic of the question cluster (block 608).
Then, the QA system increments a counter for the topic associated
with the question cluster (block 609).
[0094] The QA system determines whether the counter is greater than
a first threshold (THRESHOLD 1) (block 610). If the counter is not
greater than the first threshold, then the QA system presents the
answer and/or supplemental information to the user (block 611).
Then, the QA system updates a user profile associated with the user
or the group of users viewing the presentation (block 612).
Thereafter, operation proceeds to block 602 to determine whether
the presentation is finished.
[0095] If the counter is greater than the first threshold in block
610, the QA system determines whether the counter is greater than a
second threshold (THRESHOLD2) (block 613). If the counter is not
greater than the second threshold, then the QA system generates
feedback to update future presentation content (block 614). Then,
the QA system presents the answer and/or supplemental information
to the user (block 611) and updates a user profile associated with
the user or the group of users viewing the presentation (block
612). Thereafter, operation proceeds to block 602 to determine
whether the presentation is finished.
[0096] If the counter is greater than the second threshold in block
613, then the QA system generates feedback to modify the current
presentation (block 615). Then, the QA system generates feedback to
update future presentation content (block 614), presents the answer
and/or supplemental information to the user (block 611), and
updates a user profile associated with the user or the group of
users viewing the presentation (block 612). Thereafter, operation
proceeds to block 602 to determine whether the presentation is
finished.
[0097] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0098] As noted above, it should be appreciated that the
illustrative embodiments may take the form of an entirely hardware
embodiment, an entirely software embodiment or an embodiment
containing both hardware and software elements. In one example
embodiment, the mechanisms of the illustrative embodiments are
implemented in software or program code, which includes but is not
limited to firmware, resident software, microcode, etc.
[0099] A data processing system suitable for storing and/or
executing program code will include at least one processor coupled
directly or indirectly to memory elements through a system bus. The
memory elements can include local memory employed during actual
execution of the program code, bulk storage, and cache memories
which provide temporary storage of at least some program code in
order to reduce the number of times code must be retrieved from
bulk storage during execution.
[0100] Input/output or I/O devices (including but not limited to
keyboards, displays, pointing devices, etc.) can be coupled to the
system either directly or through intervening I/O controllers.
Network adapters may also be coupled to the system to enable the
data processing system to become coupled to other data processing
systems or remote printers or storage devices through intervening
private or public networks. Modems, cable modems and Ethernet cards
are just a few of the currently available types of network
adapters.
[0101] The description of the present invention has been presented
for purposes of illustration and description, and is not intended
to be exhaustive or limited to the invention in the form disclosed.
Many modifications and variations will be apparent to those of
ordinary skill in the art without departing from the scope and
spirit of the described embodiments. The embodiment was chosen and
described in order to best explain the principles of the invention,
the practical application, and to enable others of ordinary skill
in the art to understand the invention for various embodiments with
various modifications as are suited to the particular use
contemplated. The terminology used herein was chosen to best
explain the principles of the embodiments, the practical
application or technical improvement over technologies found in the
marketplace, or to enable others of ordinary skill in the art to
understand the embodiments disclosed herein.
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