U.S. patent application number 15/054936 was filed with the patent office on 2016-06-16 for inter thread anaphora resolution.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Corville O. Allen, Donna K. Byron, Andrew R. Freed.
Application Number | 20160170957 15/054936 |
Document ID | / |
Family ID | 56079319 |
Filed Date | 2016-06-16 |
United States Patent
Application |
20160170957 |
Kind Code |
A1 |
Allen; Corville O. ; et
al. |
June 16, 2016 |
Inter Thread Anaphora Resolution
Abstract
An approach is provided to resolve anaphors between posts, or
threads, in a threaded discussion, for example an online forum. The
approach analyzes a number of posts that are included in threads of
an online forum. During the analysis, the approach identifies terms
in parent posts, detects anaphors in child posts that reference the
terms in the parent posts, and resolves the anaphor found in the
child post with the term. The parent post with the identified term
and the child post with the resolved anaphor are then stored in the
memory for use by information handling systems, such as question
answering (QA) systems.
Inventors: |
Allen; Corville O.;
(Morrisville, NC) ; Byron; Donna K.; (Petersham,
MA) ; Freed; Andrew R.; (Cary, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
56079319 |
Appl. No.: |
15/054936 |
Filed: |
February 26, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14557637 |
Dec 2, 2014 |
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15054936 |
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Current U.S.
Class: |
704/9 |
Current CPC
Class: |
G06F 40/35 20200101;
G06F 40/211 20200101; G06F 40/284 20200101; G06F 40/295
20200101 |
International
Class: |
G06F 17/27 20060101
G06F017/27 |
Claims
1. A method implemented by an information handling system that
includes a memory and a processor, the method comprising: analyzing
a plurality of posts included in one or more threads of a threaded
discussion, wherein the analyzing further comprises: identifying a
term in a parent post of the threaded discussion; detecting that an
anaphor in a child post of the threaded discussion references the
identified term; and resolving the anaphor found in the child post
with the identified term; and storing the parent post with the
identified term and the child post with the resolved anaphor in the
memory.
2. The method of claim 1 further comprising: ingesting the parent
post with the identified term and the child post with the resolved
anaphor into a corpus utilized by a question answering (QA)
system.
3. The method of claim 2 further comprising: identifying one or
more referential types corresponding to one or more words included
in the parent post; identifying one or more matching anaphora types
corresponding to the identified referential types in one or more
child posts, wherein the child posts include the child post;
detecting the anaphors in the one or more child posts that relate
to one or more of the words included in the parent post based upon
matching the referential types to the anaphora types; resolving
each of the anaphors detected in the child posts with the
corresponding words found in the parent post; and associating each
of the child posts that include one or more anaphors relating to
corresponding words in the parent post to the parent post, wherein
the association is accessible by the QA system.
4. The method of claim 2 further comprising: identifying
referential data in the child post and the parent post, wherein at
least one of the referential data is selected from the group
consisting of domain, question, focus, concept, statements, and a
lexical answer type (LAT); and storing the referential data in the
corpus utilized by the QA system.
5. The method of claim 4 further comprising: receiving a question
at the QA system; identifying, by the QA system, one or more
candidate answers, wherein at least one of the candidate answers is
derived from the child post with one or more anaphors relating to
the corresponding words in the parent post; and responding with at
least one of the candidate answers.
6. The method of claim 1 further comprising: identifying a
referential type of the identified term; identifying a matching
anaphora type corresponding to the anaphor; matching the
referential term to an anaphora type to detect that the anaphor
references the identified term.
7. The method of claim 1 further comprising: analyzing each of a
plurality of posts in the threaded discussion, wherein the
plurality of posts include the child post and the parent post;
identify any referential types corresponding to a plurality of
words included in each of the posts; identify any anaphora types
corresponding to a plurality of words included in each of the
posts; associating each of a plurality of child posts with at least
one parent post as a relationship; resolving the anaphora types
included in the child posts with at least one of the referential
types included in the respective associated parent posts; and
building a thread tree corresponding to the threaded discussion,
wherein the thread tree includes the plurality of posts, the
relationships between posts, and the resolved anaphora types.
8. A method implemented by an information handling system that
includes a memory and a processor, the method comprising:
initializing a forum tree to store data from a thread of a threaded
discussion from an on-line forum; storing, in the forum tree, post
data associated with each post included in the thread, wherein the
storing further comprises: storing any referential types included
in each of the posts; resolving any anaphors included in each of
the posts with referential types included in a parent post; and
storing any resolved anaphor data in each of the posts.
9. The method of claim 8 further comprising: ingesting the forum
tree into a corpus utilized by a question answering (QA)
system.
10. The method of claim 8 further comprising: identifying a
plurality of parent-child relationships between the posts; and
storing a plurality of relationship associations pertaining to the
relationships in the forum tree.
11. The method of claim 8 further comprising: identifying an
anaphora type corresponding to each of the anaphors, wherein the
anaphora types are selected from the anaphora types group
consisting of a pronoun type anaphor, a fragment type anaphor, and
an agreement type anaphor; wherein the referential types are
selected from a referential types group consisting of a noun type,
a lexical answer type (LAT) type, a statement type, a question
type, and a candidate answer type; and matching the anaphora type
found in a child post to the referential type found in a parent
post, wherein the parent post is a parent to the child post in the
forum tree.
Description
BACKGROUND
[0001] In linguistics, anaphora is the use of an expression the
interpretation of which depends upon another expression in context
(its antecedent or postcedent). In the sentence "Sally arrived, but
nobody saw her," the pronoun "her" is anaphoric, referring back to
Sally. The term anaphora denotes the act of referring, whereas the
word that actually does the referring is sometimes called an
anaphor (or cataphor). Usually, an anaphoric expression is a
pro-form or some other kind of deictic expression. Anaphora is an
important concept for different reasons and on different levels:
first, anaphora indicates how discourse is constructed and
maintained; second, anaphora binds different syntactical elements
together at the level of the sentence; third, anaphora presents a
challenge to natural language processing in computational
linguistics, since the identification of the reference can be
difficult; and fourth, anaphora tells us some things about how
language is understood and processed, which is relevant to fields
of linguistics interested in cognitive psychology. An anaphora is
an expression whose reference depends on another referential
elements. Within a Question Answering (QA) system this is
traditionally restricted to intra-paragraph terms such as "it,"
"he," and "where" pronouns and terms are matched or depends on a
statement or term earlier in the paragraph. In addition, many
languages paragraphs usually separates concepts, terms and
statements that may differ. However, in a Forum or a Thread,
concepts and terms are usually discussed in a back and forth set of
comments or a common thread with multiple statements. The forum is
somewhat disconnected where each poster posts their paragraph(s).
However, in a forum or a thread the posts are somewhat
interconnected and to utilize common meanings for some "key" terms
that they depend upon along with other post referential elements.
These referential elements not resolved when the forum or thread is
ingested into a QA system based content that are comments or chat
topics.
BRIEF SUMMARY
[0002] According to one embodiment of the present disclosure, an
approach is provided to resolve anaphors between posts, or threads,
in a threaded discussion, for example an online forum. The approach
analyzes a number of posts that are included in threads of an
online forum. During the analysis, the approach identifies terms in
parent posts, detects anaphors in child posts that reference the
terms in the parent posts, and resolves the anaphor found in the
child post with the term. The parent post with the identified term
and the child post with the resolved anaphor are then stored in the
memory for use by information handling systems, such as question
answering (QA) systems.
[0003] The foregoing is a summary and thus contains, by necessity,
simplifications, generalizations, and omissions of detail;
consequently, those skilled in the art will appreciate that the
summary is illustrative only and is not intended to be in any way
limiting. Other aspects, inventive features, and advantages of the
present disclosure, as defined solely by the claims, will become
apparent in the non-limiting detailed description set forth
below.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0004] The present disclosure may be better understood, and its
numerous objects, features, and advantages made apparent to those
skilled in the art by referencing the accompanying drawings,
wherein:
[0005] FIG. 1 depicts a schematic diagram of one illustrative
embodiment of a question/answer creation (QA) system in a computer
network;
[0006] FIG. 2 illustrates an information handling system, more
particularly, a processor and common components, which is a
simplified example of a computer system capable of performing the
computing operations described herein;
[0007] FIG. 3 is an exemplary diagram depicting the relationship
between posts in a forum with candidate answers derived from the
various posts;
[0008] FIG. 4 is an exemplary diagram depicting various processes
and data stores used to perform inter-thread anaphora
resolution;
[0009] FIG. 5 is an exemplary high level flowchart that performs
steps to process a forum for ingestion to a question answering (QA)
system;
[0010] FIG. 6 is an exemplary flowchart that processes a selected
post from a forum;
[0011] FIG. 7 is an exemplary flowchart depicting anaphora
resolution of terms found in posts of a forum;
[0012] FIG. 8 is an exemplary flowchart depicting steps performed
by the process that ingests forum data with resolved anaphors to a
question answering (QA) system;
[0013] FIG. 9 is an exemplary flowchart depicting steps that
analyze a post relevance;
[0014] FIG. 10 is an exemplary flowchart depicting steps that
analyze a post for leadership qualities;
[0015] FIG. 11 is an exemplary flowchart depicting steps that
perform relevance and sentiment analysis of posts in a forum;
[0016] FIG. 12 is an exemplary flowchart depicting steps that build
a persona-based conversation between a question answering (QA)
system and a user of the system;
[0017] FIG. 13 is an exemplary flowchart depicting steps that score
candidate answers for a persona-conversation between a question
answering (QA) system and a user of the system; and
[0018] FIG. 14 is an exemplary flowchart depicting steps that
selectively ingest post data from a forum into a corpus utilized by
a question answering (QA) system.
DETAILED DESCRIPTION
[0019] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the disclosure. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0020] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below are intended to include any structure, material, or
act for performing the function in combination with other claimed
elements as specifically claimed. The description of the present
disclosure has been presented for purposes of illustration and
description, but is not intended to be exhaustive or limited to the
disclosure 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 disclosure. The
embodiment was chosen and described in order to best explain the
principles of the disclosure and the practical application, and to
enable others of ordinary skill in the art to understand the
disclosure for various embodiments with various modifications as
are suited to the particular use contemplated.
[0021] 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.
[0022] 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.
[0023] 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.
[0024] 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 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.
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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. The
following detailed description will generally follow the summary of
the disclosure, as set forth above, further explaining and
expanding the definitions of the various aspects and embodiments of
the disclosure as necessary.
[0029] FIG. 1 depicts a schematic diagram of one illustrative
embodiment of a question/answer (QA) system 100 in a computer
network 102. QA system 100 may include knowledge manager 104, which
comprises 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. Computer network 102 may include other computing
devices 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 may comprise one or more of
wires, routers, switches, transmitters, receivers, or the like. QA
system 100 and network 102 may enable question/answer (QA)
generation functionality for one or more content users. Other
embodiments may include QA system 100 interacting with components,
systems, sub-systems, and/or devices other than those depicted
herein.
[0030] QA system 100 may receive inputs from various sources. For
example, QA system 100 may receive input from the network 102, a
corpus of electronic documents 107 or other data, semantic data
108, and other possible sources of input. In one embodiment, some
or all of the inputs to QA system 100 route through the network 102
and stored in knowledge base 106. The various computing devices on
the network 102 may include access points for content creators and
content users. Some of the computing devices may include devices
for a database storing the corpus of data. The network 102 may
include local network connections and remote connections in various
embodiments, such that QA system 100 may operate in environments of
any size, including local and global, e.g., the Internet.
Additionally, QA system 100 serves as a front-end system that can
make available a variety of knowledge extracted from or represented
in documents, network-accessible sources and/or structured data
sources. In this manner, some processes populate the knowledge
manager with the knowledge manager also including input interfaces
to receive knowledge requests and respond accordingly.
[0031] In one embodiment, a content creator creates content in a
document 107 for use as part of a corpus of data with QA system
100. The document 107 may include any file, text, article, or
source of data for use in QA system 100. Content users may access
QA system 100 via a network connection or an Internet connection to
the network 102, and may input questions to QA system 100, which QA
system 100 answers according to the content in the corpus of data.
As further described below, when a process evaluates a given
section of a document for semantic content, the process can use a
variety of conventions to query it from knowledge manager 104. One
convention is to send a well-formed question.
[0032] Semantic data 108 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 data 108 is content that interprets an expression, such as
by using Natural Language Processing (NLP). In one embodiment, the
process sends well-formed questions (e.g., natural language
questions, etc.) to QA system 100 and QA system 100 may interpret
the question and provide a response that includes one or more
answers to the question. In some embodiments, QA system 100 may
provide a response to users in a ranked list of answers.
[0033] In some illustrative embodiments, 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. The
IBM Watson.TM. knowledge manager system may receive 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.
[0034] 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. There may be
hundreds or even thousands of reasoning algorithms applied, each of
which performs different analysis, e.g., comparisons, 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.
[0035] 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 IBM Watson.TM. QA system. The statistical model may
then be used to summarize 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 may be repeated for each of the candidate answers until the
IBM Watson.TM. 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.
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.
[0036] Types of information handling systems that can utilize QA
system 100 range from small handheld devices, such as handheld
computer/mobile telephone 110 to large mainframe systems, such as
mainframe computer 170. Examples of handheld computer 110 include
personal digital assistants (PDAs), personal entertainment devices,
such as MP3 players, portable televisions, and compact disc
players. Other examples of information handling systems include
pen, or tablet, computer 120, laptop, or notebook, computer 130,
personal computer system 150, and server 160. As shown, the various
information handling systems can be networked together using
computer network 102. Types of computer network 102 that can be
used to interconnect the various information handling systems
include Local Area Networks (LANs), Wireless Local Area Networks
(WLANs), the Internet, the Public Switched Telephone Network
(PSTN), other wireless networks, and any other network topology
that can be used to interconnect the information handling systems.
Many of the information handling systems include nonvolatile data
stores, such as hard drives and/or nonvolatile memory. Some of the
information handling systems shown in FIG. 1 depicts separate
nonvolatile data stores (server 160 utilizes nonvolatile data store
165, and mainframe computer 170 utilizes nonvolatile data store
175. The nonvolatile data store can be a component that is external
to the various information handling systems or can be internal to
one of the information handling systems. An illustrative example of
an information handling system showing an exemplary processor and
various components commonly accessed by the processor is shown in
FIG. 2.
[0037] FIG. 2 illustrates information handling system 200, more
particularly, a processor and common components, which is a
simplified example of a computer system capable of performing the
computing operations described herein. Information handling system
200 includes one or more processors 210 coupled to processor
interface bus 212. Processor interface bus 212 connects processors
210 to Northbridge 215, which is also known as the Memory
Controller Hub (MCH). Northbridge 215 connects to system memory 220
and provides a means for processor(s) 210 to access the system
memory. Graphics controller 225 also connects to Northbridge 215.
In one embodiment, PCI Express bus 218 connects Northbridge 215 to
graphics controller 225. Graphics controller 225 connects to
display device 230, such as a computer monitor.
[0038] Northbridge 215 and Southbridge 235 connect to each other
using bus 219. In one embodiment, the bus is a Direct Media
Interface (DMI) bus that transfers data at high speeds in each
direction between Northbridge 215 and Southbridge 235. In another
embodiment, a Peripheral Component Interconnect (PCI) bus connects
the Northbridge and the Southbridge. Southbridge 235, also known as
the I/O Controller Hub (ICH) is a chip that generally implements
capabilities that operate at slower speeds than the capabilities
provided by the Northbridge. Southbridge 235 typically provides
various busses used to connect various components. These busses
include, for example, PCI and PCI Express busses, an ISA bus, a
System Management Bus (SMBus or SMB), and/or a Low Pin Count (LPC)
bus. The LPC bus often connects low-bandwidth devices, such as boot
ROM 296 and "legacy" I/O devices (using a "super I/O" chip). The
"legacy" I/O devices (298) can include, for example, serial and
parallel ports, keyboard, mouse, and/or a floppy disk controller.
The LPC bus also connects Southbridge 235 to Trusted Platform
Module (TPM) 295. Other components often included in Southbridge
235 include a Direct Memory Access (DMA) controller, a Programmable
Interrupt Controller (PIC), and a storage device controller, which
connects Southbridge 235 to nonvolatile storage device 285, such as
a hard disk drive, using bus 284.
[0039] ExpressCard 255 is a slot that connects hot-pluggable
devices to the information handling system. ExpressCard 255
supports both PCI Express and USB connectivity as it connects to
Southbridge 235 using both the Universal Serial Bus (USB) the PCI
Express bus. Southbridge 235 includes USB Controller 240 that
provides USB connectivity to devices that connect to the USB. These
devices include webcam (camera) 250, infrared (IR) receiver 248,
keyboard and trackpad 244, and Bluetooth device 246, which provides
for wireless personal area networks (PANs). USB Controller 240 also
provides USB connectivity to other miscellaneous USB connected
devices 242, such as a mouse, removable nonvolatile storage device
245, modems, network cards, ISDN connectors, fax, printers, USB
hubs, and many other types of USB connected devices. While
removable nonvolatile storage device 245 is shown as a
USB-connected device, removable nonvolatile storage device 245
could be connected using a different interface, such as a Firewire
interface, etcetera.
[0040] Wireless Local Area Network (LAN) device 275 connects to
Southbridge 235 via the PCI or PCI Express bus 272. LAN device 275
typically implements one of the IEEE .802.11 standards of
over-the-air modulation techniques that all use the same protocol
to wireless communicate between information handling system 200 and
another computer system or device. Optical storage device 290
connects to Southbridge 235 using Serial ATA (SATA) bus 288. Serial
ATA adapters and devices communicate over a high-speed serial link.
The Serial ATA bus also connects Southbridge 235 to other forms of
storage devices, such as hard disk drives. Audio circuitry 260,
such as a sound card, connects to Southbridge 235 via bus 258.
Audio circuitry 260 also provides functionality such as audio
line-in and optical digital audio in port 262, optical digital
output and headphone jack 264, internal speakers 266, and internal
microphone 268. Ethernet controller 270 connects to Southbridge 235
using a bus, such as the PCI or PCI Express bus. Ethernet
controller 270 connects information handling system 200 to a
computer network, such as a Local Area Network (LAN), the Internet,
and other public and private computer networks.
[0041] While FIG. 2 shows one information handling system, an
information handling system may take many forms, some of which are
shown in FIG. 1. For example, an information handling system may
take the form of a desktop, server, portable, laptop, notebook, or
other form factor computer or data processing system. In addition,
an information handling system may take other form factors such as
a personal digital assistant (PDA), a gaming device, ATM machine, a
portable telephone device, a communication device or other devices
that include a processor and memory.
[0042] FIGS. 3-14 depict an approach that resolves anaphors between
posts in an online forum. The approach described herein capitalizes
on the nature and implicit association with forums, thread, chat
content, and email chains and perform anaphora resolution on terms
to provide their explicit meaning and their referential elements.
When terms that require anaphora resolution are noted, they are
associated with the most likely referential term and or topic in a
thread, to complete the meaning and the statement, where ambiguity
is based on the term type. These include anaphora words and
concepts: Anaphora words: pronouns, too, same, true, yeah
(agreement), disagreement A result of this approach is that forum,
thread and chat content is better able to be ingested in a question
answering (QA) system because the terms are better defined and
associated with anaphors of child posts.
[0043] Sometimes another co-referential phrase cannot be
identified, in which case a system depends on dependent phrases and
their sponsors. This occurs frequently forums in the form of
agreements and disagreements. The approach here utilizes the
implicit nature of an online forum to resolve these agreement and
disagreement anaphors. New anaphoric elements of agreement and
disagreement are found within posts of a forum. Such agreements and
disagreements may be encoded by the user in different fashions
(e.g., "+1," "second that," etc). Typically in forums there are
propositions and statements that do not constitute an entire
co-reference. These elements are treated as abstract concepts
(propositions/statements) and lead to agreements/disagreements.
Co-references in general are primarily noun, pronoun resolutions.
The approach provided herein extends beyond simple co-references
and improves anaphor resolution where prevalent in posts to have
propositions and statements, then agreements and disagreements.
Dangling references pose a challenge when solving co-references.
When resolving co-referencing across paragraphs, an analysis
sometimes results in dangling references and the parsing tree has
siblings that are not referenced. In this approach, the implicit
nature of a forum structure and metadata contained therein are
utilized to resolve the anaphors by traversing the parent/child
structure of the forum. The result of the approach's analysis of
forums is greater and more accurate resolution of anaphors in posts
found in a forum. Additional anaphoric elements such as "in-line
quotes", short fragments and opinionated statements are detected
with the approach. The approach utilizes the forum metadata where
there is an implicit reference to resolve anaphors based on the
natural language found in the posts and it's associated "in-line"
metadata.
[0044] FIG. 3 is an exemplary diagram depicting the relationship
between posts in a forum with candidate answers derived from the
various posts. Posts included in a forum include main thread post
300 and child posts (320 and 340). Posts are interrelated both
through the common main thread post (300) but also outside the main
post where some posts, while being a child post of the main post,
also serve as "parent" posts to other child posts. FIG. 3 depicts
an instance or embodiment of a threaded discussion, where there is
a main post and child posts to a main post. In this embodiment, any
posts can be a parent post of subsequent child posts, where text,
images and information can be placed. There is an implicit
relationship in a threaded discussion between the parent post and
all child posts, and even grandchildren posts, etc. The threaded
discussion can take place online, off-line, in direct communication
in a chat engine, or in comments to messages in text messages.
These threaded discussions can be built based on phone
conversations, such as text messaging, where there is a back and
forth discussion between multiple parties. Comments made to topics
on the web can be treated as threaded discussions with a main post
or topic and several child posts.
[0045] In the example shown, child post 320 is a child of main post
300 and child post 340 is a child of post 320, so that post 320 is
a child to main post 300 and a parent post to child post 340.
Relationships are established between child posts and their parent
posts.
[0046] During analysis of the online forum, various types of data,
or attributes, are gathered or deduced from the various posts. Each
of the posts depicted (posts 300, 320, and 340) are each shown as
having each of the attributes. These attributes include the domain
to which the post belongs (domain 302 for main post 300, domain 322
for child post 322, and domain 342 for child post 342), questions
included in the post (questions 304 for main post 300, questions
324 for child post 320, and questions 344 for child post 340), the
focus of the post (focus 306 for main post 300, focus 326 for child
post 320, and focus 346 for child post 340), the concept of the
post (concept 308 for main post 300, concept 328 for child post
320, and concept 348 for child post 340), the statements made in
the post (statements 310 for main post 300, statements 330 for
child post 320, and statements 350 for child post 340), and the
Lexical Answer Types ("LATs" which are the type of answer that will
be required for the question, such as a person, place, film name,
etc.). The LATs for the posts shown include LATs 312 for main post
300, LATs 332 for child post 320, and LATs 352 for child post
340.
[0047] Sources of candidate answers 350 include any posts in the
forum based on the QA analysis performed by the QA system. The QA
system utilizes resolved anaphors when analyzing child posts that
refer to items that were included in parent posts. In the example,
sources of candidate answers 350 include a candidate answer derived
from main post 300 (candidate answer 360), a candidate answer
derived from child post 320 (candidate answer 370), and a candidate
answer derived from child post 340 (candidate answer 380).
[0048] The following is an example of anaphor resolution between
posts. First, a main thread is posted to an online forum with a
title of "Product outage next Friday from noon to midnight" with
post text as "Hi everyone--the area I work in uses the product a
lot--any chance the upgrade outage can be pushed until the
weekend?" A child post is received that says "Hi John--our
infrastructure support is M-F, so pushing stuff to the weekends is
not usually possible. Especially in this case with technicians
installing memory, they only work during the week. Keep in mind,
you can still develop, just not deliver changes to each other."
Here, the word "stuff" is an anaphor that refers to the term
"upgrade" in the main post. Another post is received saying "Still
down . . . any updates on the timeline?" Here, the words "still
down" are an anaphor referring to the term "product outage" in the
main post and the word "timeline" is an anaphor referring to the
date and time ("Friday from noon to midnight") as the original
timeline when the outage was scheduled. As used herein, a "term" in
one post, such as a parent post, is any term, phrase, passage, or
expression that provides a referential term to which an anaphor in
a child post refers.
[0049] FIG. 4 is an exemplary diagram depicting various processes
and data stores used to perform inter-thread anaphora resolution.
Forum tree 400 is a collection of data pertaining to an online
forum that is being analyzed. Post data 410 shows data elements, or
attributes, that are gathered or deduced from the various posts
including the domain of the post, questions posed by the post, the
focus of the post, any concepts included in the post, statements
made in the post and the Lexical Answer Type (LAT) of the post. In
addition, anaphors that are found and resolved for the post are
also stored for the post.
[0050] In forum tree 400, post data includes a main post 420 and
relationships between posts, signified as related posts 425.
Relationships include parent child relationships where one post (a
child post) is posted after and references another post (the parent
post). Main post 420 serves as a parent post to one or more other
(child) posts in the forum tree.
[0051] Anaphora detection process 430 detects anaphors in
identified child posts and uses referential data found in parent
posts to resolve such anaphors. Anaphora detection can be broken
down into different types of anaphora detection. These different
types of anaphora detection include pronoun type 435 where a
pronoun found in a child post refers to a noun found in a parent
post. For example, the pronoun "he" found in a child post might
refer to a person that was referenced in a parent post. Pronoun
anaphors are stored in data store 440.
[0052] Another type of anaphora detection is fragment type 445
where a subject fragment that is found in a child post refers to a
subject found in a parent post. Using the example introduced above
for a software product outage, a fragment (anaphor) found in a
child post of "still down" was detected and found to refer back to
the product outage term that was referenced in the main post.
Fragment anaphors are stored in data store 450.
[0053] Another type of anaphora detection is agreement type 455
where a statement of agreement that is found in a child post refers
to an opinion or answer that was found in a parent post. For
example, in a forum discussing a movie, a main post could opine
that a particular movie was "fantastic." A child post could have a
statement of agreement, such as "me too," or "+1," or "correct you
are!" with such agreements referring back to the opinion that the
movie was fantastic. Disagreements are also detected as agreement
type anaphors where, instead of agreeing, the child post includes a
statement of disagreement, such as "no way," "I don't think so," or
"are you crazy?" with such agreements referring back to the opinion
that the movie was fantastic. Agreement type anaphors are stored in
data store 460.
[0054] Another type of anaphora detection is statement/question
type 455 where a statement that is found in a child post refers to
a question that was found in a parent post. For example, in a forum
discussing the movie, a main post could pose a question of "who is
the main actor in the movie?" A child post could provide an answer,
such as "John Doe is the leading man in the film" with such answer
referring back to the question posed in the parent post.
Statement/question type anaphors are stored in data store 470.
[0055] Process 475 associates the anaphors found in the child posts
to their respective terms found in their parent posts. The resolved
anaphor (e.g., the pronoun "he" resolved to a particular person's
name, etc.) is stored in the post's data in data store 410. To
associate anaphors to parent posts, the parent posts with the
relevant terms that is referenced by the anaphor found in the child
post needs to be detected. This detection is performed by checking
for referential terms in different types of posts. At 480, the main
post in the thread or forum is checked for referential terms. At
485, the parent post of the child post is checked for referential
terms. The referential terms might not be in the main or parent
post, but might be in an intervening "ancestor" post between the
main post and the parent post. At 490, these ancestor posts are
checked for referential terms. When referential terms are found in
a parent post (either the main post, the immediate parent post, or
an ancestor post), the relationship is noted in forum tree 400.
[0056] FIG. 5 is an exemplary high level flowchart that performs
steps to process a forum for ingestion to a question answering (QA)
system. FIG. 5 processing commences at 500 and shows the steps
taken by a process that performs a routine that processes online
forums. At step 510, the process selects the first online forum
that is being processed. At step 520, the process selects the first
thread from the selected forum. At step 525, the process selects
the main post of selected thread.
[0057] At predefined process 530, the main post is processed (see
FIG. 6 and corresponding text for processing details). The data
gathered from processing the main post is stored as post data in
data store 410. The process determines as to whether there are
child posts to process in the selected thread (decision 540). If
there are more child posts to process, then decision 540 branches
to the `yes` branch to process additional child posts. At step 550,
the process selects the next post from selected thread. At
predefined process 560, the process performs the process selected
post routine (see FIG. 6 and corresponding text for processing
details). The data gathered from the child post is stored as post
data in data store 410. Processing then loops back to decision
540.
[0058] Once all of the child posts are processed, decision 540
branches to the `no` branch whereupon the process determines as to
whether there are more threads in the selected forum to process
(decision 570). If there are more threads in the selected forum to
process, then decision 570 branches to the `yes` branch which loops
back to step 520 to select the next thread from the selected forum.
This looping continues until there are no more threads in the
selected forum to process, at which point decision 570 branches to
the `no` branch for anaphora resolution.
[0059] At predefined process 575, the process performs the anaphora
resolution routine (see FIG. 7 and corresponding text for
processing details). The anaphora resolution routine detects
anaphors found in posts from post data store 410, resolves the
anaphors with terms found in referential data from other posts
stored in post data store 410, and resolves the anaphor by storing
the identified terms referenced by the anaphors in the post data
410.
[0060] The process determines as to whether the end of forums being
processed has been reached (decision 580). If the end of forums
being processed has not yet been reached, then decision 580
branches to the `no` branch which loops back to step 510 to select
the next forum and process the posts in the forum as described
above. This looping continues until the end of the forums being
processed has been reached, at which point decision 580 branches to
the `yes` branch for further processing.
[0061] At predefined process 585, the process performs the Ingest
Forum Data with
[0062] Resolved Anaphors routine (see FIG. 8 and corresponding text
for processing details).At predefined process 590, the process
performs the Relevance & Sentiment routine (see FIG. 11 and
corresponding text for processing details). At predefined process
595, the process performs the Build Persona-Based Conversation
routine (see FIG. 12 and corresponding text for processing
details). FIG. 5 processing thereafter ends at 599.
[0063] FIG. 6 is an exemplary flowchart that processes a selected
post from a forum. FIG. 6 processing commences at 600 and shows the
steps that perform a routine that processes data found in a post.
The process determines as to whether the post being processed is
the main post of the forum thread (decision 610). If the post being
processed is the main post of the forum thread, then decision 610
branches to the `yes` branch whereupon, at step 620, the process
initializes forum tree 400 used to store the post data associated
with this forum thread. On the other hand, if the post being
processed is not the main post of the forum thread, then decision
610 branches to the `no` branch bypassing step 620.
[0064] At step 630, the process generates a unique post identifier
for this post and adds a record used to store this post data in
forum tree 400 with new post data 410. At step 640, the process
identifies referential types based on words, terms, and phrases
found in the post that is being processed. Referential data can
include the domain of the post, questions posed by the post, the
focus of the post, any concepts included in the post, statements
made in the post and the Lexical Answer Type (LAT) of the post.
[0065] At step 650, the process identifies anaphora types based on
the words, terms, and phrases found in post that is being
processed. Types of anaphors include pronoun type anaphors,
fragment type anaphors, agreement type anaphors, and statement type
anaphors.
[0066] At step 660, the process identifies any parent(s) to this
post that are already included in forum tree 400. Parent posts
include the main post to the thread, the direct parent post of the
thread, and any intervening parent (ancestor) posts between the
main post and the direct parent post. At step 670, the process adds
links from this (child) post to any identified parent posts that
were found in step 660. At step 675, the relationships between this
post and parent posts are added to post data included in data store
410. Links are added to this post as links to the parent posts, and
in the respective parent post data (425) as links to this child
post with data store 425 being a subset of data store 410 and shown
as a separate data store for illustrative purposes.
[0067] At predefined process 680, the process performs the Analyze
Post Relevance routine (see FIG. 9 and corresponding text for
processing details).At predefined process 690, the process performs
the Analyze Post for Leadership routine (see FIG. 10 and
corresponding text for processing details). FIG. 6 processing
thereafter returns to the calling routine (see FIG. 5) at 695.
[0068] FIG. 7 is an exemplary flowchart depicting anaphora
resolution of terms found in posts of a forum. FIG. 7 processing
commences at 700 and shows the steps taken by a process that
performs a routine that resolves anaphors found in a child post. At
step 710, the process selects the first post from forum tree 400.
At step 720, the process selects the first anaphor from the
selected post (if an anaphor exists in the post). At step 725, the
process selects the first related post (immediate parent post, then
main post, then ancestor posts) from forum tree 400. At step 730,
the process selects the first referential term/type from the
selected related post.
[0069] Table 750 depicts the relationship between anaphora types
(755) and their respective referential types (760). Pronoun type
anaphors are resolved with referential types found in a parent post
of a noun or subject. Fragment type anaphors are resolved with
referential types found in a parent post of a statement, a Lexical
Answer Type (LAT), or focus. Agreement type anaphors are resolved
with referential types found in a parent post of a statement or
opinion, a question, or a candidate answer. At step 740, the
process identifies anaphora type(s) for the selected anaphor based
on the referential type as shown in table 750.
[0070] The process determines as to whether the identified anaphora
type(s) were found in the selected child post (decision 765). If
the identified anaphora type(s) were found in the selected child
post, then decision 765 branches to the `yes` branch for continued
processing. On the other hand, if the identified anaphora type(s)
were not found in the selected child post, then decision 765
branches to the `no` branch bypassing decision 770 and step 775.
The process determines as to whether the anaphora term found in the
child post matches the referential term found in the parent post
(decision 770). If the anaphora term found in the child post
matches the referential term found in the parent post, then
decision 770 branches to the `yes` branch, whereupon, at step 775,
the process annotates the anaphora relationship with related post
referential term. In addition, at step 775, the anaphor found in
the child post is resolved using the referential term found in the
parent post. The annotated anaphora relationship data and the
resolved anaphor data is stored in post data 410. On the other
hand, if the anaphora term found in the child post does not match
the referential term found in the parent post, then decision 770
branches to the `no` branch bypassing step 775.
[0071] The process determines as to whether there are more
referential terms that need to be processed (decision 780). If
there are more referential terms that need to be processed, then
decision 780 branches to the `yes` branch which loops back to step
730 to select and process the next referential term. This looping
continues until all referential terms have been processed, at which
point decision 780 branches to the `no` branch.
[0072] The process determines as to whether there are more related
posts that need to be processed (decision 785). If there are more
related posts that need to be processed, then decision 785 branches
to the `yes` branch which loops back to step 725 to select and
process the next related post. This looping continues until all
related posts have been processed, at which point decision 785
branches to the `no` branch.
[0073] The process determines as to whether there are more anaphors
included in the selected post that need to be processed (decision
790). If there are more anaphors included in the selected post that
need to be processed, then decision 790 branches to the `yes`
branch whereupon processing loops back to step 720 to select and
process the next anaphor from the selected post. This looping
continues until all anaphors in the selected post have been
processed, at which point decision 790 branches to the `no`
branch.
[0074] The process determines as to whether there are more posts in
the forum tree that need to be processed (decision 795). If there
are more posts in the forum tree that need to be processed, then
decision 795 branches to the `yes` branch which loops back to
select and process the next post from the forum tree. This looping
continues until all of the posts have been processed, at which
point decision 795 branches to the `no` branch and processing
returns to the calling routine (see FIG. 5) at 799.
[0075] FIG. 8 is an exemplary flowchart depicting steps performed
by the process that ingests forum data with resolved anaphors to a
question answering (QA) system. At predefined process 800, the
process performs the Selectively Ingest Forum Posts with Resolved
Anaphors into QA System Knowledge Base Based on Relevance to Parent
Post and/or Main Post routine (see FIG. 14 and corresponding text
for processing details). Predefined process 800 reads post data 410
from forum tree 400 and ingests the post data to knowledge base 106
that is utilized by question answering (QA) system 100. When
requestor 810, such as a user of the QA system, poses a question to
the QA system, the QA system may provide candidate answers that
utilize the ingested forum data with such ingested data including
resolved anaphors found in child post data.
[0076] FIG. 9 is an exemplary flowchart depicting steps that
analyze the relevance of a post. FIG. 9 processing commences at 900
and shows the steps taken by a process that performs a routine that
analyzes a post for relevance. At step 905, the process retrieves
data from forum tree pertaining to a post, the post's parent(s)
post(s), and the main post. The process determines as to whether
the selected post is the main post (decision 910). If the selected
post is the main post, then decision 910 branches to the `yes`
branch and processing returns to the calling routine (see FIG. 5)
at 915. On the other hand, if the selected post is not the main
post, then decision 910 branches to the `no` branch and processing
continues.
[0077] At step 920, the process compares and scores this child
post's Lexical
[0078] Answer Type (LAT) to its parent's LAT utilizing inter-thread
anaphora data. Step 920 stores the Parent-LAT relationship score in
memory area 925. At step 930, the process compares and score this
child post's LAT to the LAT of the main post utilizing inter-thread
anaphora data. Step 930 stores the Main-LAT relationship score in
memory area 935.
[0079] At step 940, the process compares and score this child
post's focus to its parent(s) focus utilizing inter-thread anaphora
data. Step 940 stores the parent-focus relationship score in memory
area 945. At step 950, the process compares and scores this child
post's focus to the focus of the main post utilizing inter-thread
anaphora data. Step 950 stores the main-focus relationship score in
memory area 955.
[0080] At step 960, the process scores the sentiment of this child
post to the content of parent post utilizing inter-thread anaphora
data. Step 960 stores the parent sentiment score in memory area
965. At step 970, the process scores the sentiment of this child
post to the content of the main post utilizing inter-thread
anaphora data. Step 970 stores the main sentiment score in memory
area 975. At step 980, the process stores the relevance scores (LAT
& focus for the parent and main) and the sentiment scores
related to post in post data 410. FIG. 9 processing thereafter
returns to the calling routine (see FIG. 5) at 995.
[0081] FIG. 10 is an exemplary flowchart depicting steps that
analyze a post for leadership qualities. FIG. 10 processing
commences at 1000 and shows the steps taken by a process that
performs a routine that analyzes a post for leadership or follower
qualities. At step 1010, the process analyzes the selected post
text from forum tree 400 for assertions or action verbs where the
poster (writer of the post) makes statements to perform a function
with the analysis of such leadership persona utilizing inter-thread
anaphora data previously identified for the post.
[0082] The process determines as to whether leadership persona was
identified in the post by step 1010 (decision 1020). If leadership
persona was identified in the post, then decision 1020 branches to
the `yes` branch whereupon, at step 1030 the process calculates a
leader persona score based on the assertions and/or statements made
in post with surety. The leader persona score is stored as
leadership data in the data pertaining to the post (data store
410). FIG. 10 processing thereafter returns to the calling routine
(see FIG. 5) at 1040.
[0083] On the other hand, if leadership persona was not identified
in the post, then decision 1020 branches to the `no` branch
bypassing step 1030 and branching to steps that analyze the post
for follower persona traits. At step 1050, the process analyzes the
post text for questions posed by the poster or agreement made by
the poster with little or no actions relevant to the post's parent
post or to the main post. The analysis utilizes inter-thread
anaphora data previously identified for the post. The process
determines as to whether the analysis performed at step 1050
identified a follower persona in the post (decision 1160). If a
follower persona is identified in the post, then decision 1160
branches to the `yes` branch, whereupon at step 1070 the process
calculates the follower persona score for the post based on the
extent of questions or agreement in the post with little or no
actions that are relevant to the post's parent post or to the main
post. The follower persona score is stored as follower data in the
leadership data pertaining to the post (data store 410). On the
other hand, if a follower persona is not identified in the post,
then decision 1160 branches to the `no` branch bypassing step 1070.
FIG. 11 processing thereafter returns to the calling routine (see
FIG. 5) at 1195.
[0084] FIG. 11 is an exemplary flowchart depicting steps that
perform relevance and sentiment analysis of posts in a forum. FIG.
11 processing commences at 1100 and shows the steps taken by a
process that performs a relevance and sentiment analysis routine.
At step 1110, the process selects the first post from bottom (last)
post in tree as the forum tree is processed in reverse order from
the bottom of the tree to the top (main post) of the tree. The
process determines as to whether the selected post has child posts
that reference the selected post (decision 1120). If the selected
post has child posts that reference the selected post, then
decision 1120 branches to the `yes` branch to process the selected
post using steps 1130 through 1180. On the other hand, if the
selected post does not have any child posts that reference the
selected post, then decision 1120 branches to the `no` branch
bypassing steps 1130 through 1180.
[0085] At step 1130, the process retrieves and combines the
relevance scores (LAT/focus) of the child posts that refer to this
selected (parent) post. At step 1140, the process analyzes the
combined relevance scores of the child posts. For example, the
relevance scores can be compared to thresholds that identify
whether the post is somewhat more relevant than other posts. In one
embodiment, the relevance scores are combined beforehand so that
the tree's relevance scores can be used to obtain thresholds (e.g.,
above average relevance score, etc.). At step 1150, the process
boosts or suppresses the selected post's relevance score based on
analysis of the child posts' relevance scores that was performed in
step 1140. For example, if the combined relevance scores are above
average, then the selected post's relevance score might be boosted
and if the combined relevance score is below average, then the
selected post's relevance score might be reduced or otherwise
suppressed. Additionally, posts with relevance scores in the top
quartile or top ten percent might be further boosted and those
posts with relevance scores in the bottom quartile or bottom ten
percent might be further suppressed or reduced.
[0086] At step 1160, the process retrieves and combine the
sentiment scores of the selected post's child posts. At step 1170,
the process analyzes the combined sentiment scores of the selected
post's child posts. For example, the sentiment scores can be
compared to thresholds that identify whether the post is somewhat
more relevant than other posts. In one embodiment, the sentiment
scores are combined beforehand so that the tree's sentiment scores
can be used to obtain thresholds (e.g., above average sentiment
score, etc.). At step 1180, the process boosts or suppresses the
selected post's sentiment score based on the analysis of the
selected post's child posts' sentiment scores that was performed in
step 1170. For example, if the combined sentiment scores are above
average, then the selected post's sentiment score might be boosted
and if the combined sentiment score is below average, then the
selected post's sentiment score might be reduced or otherwise
suppressed. Additionally, posts with sentiment scores in the top
quartile or top ten percent might be further boosted and those
posts with sentiment scores in the bottom quartile or bottom ten
percent might be further suppressed or reduced.
[0087] The process determines as to whether the process has reached
the top of forum tree with the last post selected having been the
main post (decision 1190). If the process has not yet reached the
top of forum tree, then decision 1190 branches to the `no` branch
which loops back to select and process the next post up the forum
tree. This looping continues until all of the posts have been
processed, at which point decision 1190 branches to the `yes`
branch and processing returns to the calling routine (see FIG. 5)
at 1195.
[0088] FIG. 12 is an exemplary flowchart depicting steps that build
a persona-based conversation between a question answering (QA)
system and a user of the system. FIG. 12 processing commences at
1200 and shows the steps taken by a process that performs a
persona-based conversation routine that allows a user to have a
natural language conversation with a question answering (QA)
system.
[0089] At step 1205, the process configures the QA System to engage
in conversation with a user. At step 1210, the process selects a
configuration profile from a set of available profiles. The
profiles include a preference to weigh candidate answers based on
relevance, to weigh candidate answers based on sentiment, to weigh
candidate answers based on both relevance and sentiment, and
whether a leader or follower persona desired in the candidate
answer. Step 1210 retrieves persona based profiles from data store
1215 and stores the selected persona-based profile in memory area
1220.
[0090] At step 1225, the process receives a question from a user.
In one embodiment, the user is the entity that selected the
persona-based profile by interacting with the QA system so that the
selected persona-based profile was selected in step 1210. At step
1230, the process employed by the QA system identifies candidate
answers from traditional knowledge base 106 with answers matching
the focus and Lexical Answer Type (LAT) of the question that was
posed by the user. The candidate answers are stored as potential
candidate answers in memory area 1235. At step 1240, the process
employed by the QA System identifies a set of conversational
candidate answers from a forum-based corpus (forum trees 400) with
these candidate answers also matching the focus and the LAT of the
question posed by the user. These conversational candidate answers
are stored in memory area 1245.
[0091] At predefined process 1250, the process performs the Score
Candidate Answers routine (see FIG. 13 and corresponding text for
processing details). Predefined process 1250 takes the traditional
candidate answers from memory area 1235 and the conversational
candidate answers from memory area 1245 to result in one or more
final conversational answers that are stored in memory area 1255.
At step 1260, the process replies to the user in with the final
conversational answer(s) that were stored in memory area 1255. In
one embodiment, a conversational tone is used in the reply so that
the answer has a conversational feel not unlike the tone used in
the user's original question posed to the user in step 1225. FIG.
12 processing thereafter ends at 1295.
[0092] In one embodiment, when ingesting corpora that is in the
forum or thread form, the main topic is treated as the main
context. We identify the series of focus and LAT for the main
topic. A focus and LAT are deduced, but maintain the most confident
focus and LAT. The Inter-Thread Anaphora Resolution data is
utilized to diagnose associations across focuses in the responses
to then in combination determine a relevance score for each child
response. The child responses are analyzed for similarity based on
the focus, LAT and word vocabulary. The sentiment levels for each
individual post in relation to the main terms are recorded with
each conversation. The positive, neutral, or negative phrase in the
statement is determined and a sentiment level is associated with
that part of the statement. In addition, the embodiment utilizes
prismatic frames with a sentiment level attached to identify
categorize those phrases as positive or negative in their relation
to the main topic or parent topic.
[0093] Example parsing with SVO and sentiment
[0094] Overall question (main post): "How is the weather?"
[0095] Response post: "The weather is beautiful. It's seventy
degrees and clear. A sunny day like today is great for surfing. I'm
the best surfer ever, just ask my awesome friends!"
[0096] S1: The weather is beautiful. Subject="weather", overall
relevance=80%. "beautiful"=subject complement adjective,
sentiment=70%
[0097] S2: "It's seventy degrees and clear." Anaphora resolution
resolves "It's" as the subject="weather", overall relevance=100%.
as "seventy degrees" and "clear" are highly correlated to the
weather type. sentiment=neutral.
[0098] S3: "A sunny day like today is great for surfing."
Subject="sunny day", an ontology tells us this is type=weather, so
overall relevance=80%. Subject complements "sunny" and "great" lead
to sentiment=90%
[0099] S4a: "I'm the best surfer ever." Subject=I, I is
type=person, no relevance to question, this subject-verb-object
(SVO) is not considered.
[0100] S4b: "Just ask my awesome friends!" In this command
structure, the implied subject is "you", type=person, no relevance
to this question, this SVO is not considered.
[0101] Thus, only the first three sentences are considered in
scoring the response (S1, S2, and S3). This response will be scored
with high relevance and medium-high sentiment.
[0102] Note that a generic sentiment scoring algorithm would have
given this response a very high sentiment, as the fourth sentence
was overwhelmingly high sentiment. However, the high sentiment was
not related to the topic at hand, so our algorithm disregards
it.
[0103] Below is a second example forum thread ingestion. A forum
thread appears as follows. (Responses are marked with Rn, nested
responses with RnRm.). Inter-Thread Anaphor Resolution (ITAR) is
used to resolve anaphors.
[0104] TOPIC: "What's the weather like where you are?"
(LAT=WeatherConditions)
[0105] R1: "It's sunny and 70 degrees with a slight breeze" (ITAR
to "The weather is sunny and 70 degrees with a slight breeze",
Persona="Leader"), (relevant=100% from three weather terms of
sunny/70 degrees/slight breeze, sentiment=5)
[0106] *R2: "It's a beautiful 70 degrees and I'm going to the
beach!" (ITAR to "The weather is a beautiful 70 degrees and I'm
going to the beach!" Persona="Leader") (relevant=90% from two
weather terms of beautiful/70 degrees, overall sentiment=8,
beautiful 70 degrees sentiment =9, I'm going to the beach
sentiment=7)
[0107] **R2R1 "Here too! I can't wait to get some surfing in!"
(ITAR to `The weather is beautiful here too and I'm going to the
beach! I'm going to the beach!`) (ITAR Here also, I cannot wait to
get some surfing in! "Persona="Leader") (relevant=20%, sentiment=9)
(relevant="20%, sentiment=5)
[0108] ***R2R1 R1 "I love surfing!" (relevance to Topic=0%,
sentiment=7, relevance to Parent=90%, Persona="Follower")
[0109] *R3: "Who cares, I never go outside" (relevant=10%,
sentiment=1, Persona="Leader")
[0110] *R4: "I love it!!!" (relevant=10%, sentiment=10)
[0111] FIG. 13 is an exemplary flowchart depicting steps that score
candidate answers for a persona-conversation between a question
answering (QA) system and a user of the system. FIG. 13 processing
commences at 1300 and shows the steps taken by a process that
performs a routine that scores candidate answers for a
persona-based conversation with a question answering (QA) system.
At step 1310, the process selects the first candidate answer from a
set of weighted candidate answers stored in data store 1320.
Weighted candidate answers 1320 include both the candidate answers
stored in memory area 1235 in FIG. 12 as well as the conversational
candidate answers stored in memory area 1245 in FIG. 12.
[0112] At step 1330, the process compares the selected candidate
answer to the selected persona-based profile that was previously
stored in memory area 1220. At step 1340, the process increases the
weight of the selected candidate answer the more it matches the
selected persona-based profile (sentiment/relevance) and decreases
the weight the more it does not match the selected persona-based
profile. The process determines as to whether the end of the set of
candidate answers has been reached (decision 1350). If the end of
the set of candidate answers has not yet been reached, then
decision 1350 branches to the `no` branch which loops back to step
1310 to select and process the next candidate answer from data
store 1320. This looping continues until all of the candidate
answers have been processed, at which point decision 1350 branches
to the `yes` branch for further processing. At step 1360, the
process selects the final conversational answer, or answers, as the
candidate answer(s) with the highest weight after taking into
account the persona-based profile that has been selected. The final
conversational candidate answer(s) are stored in memory area 1255.
FIG. 13 processing thereafter returns to the calling routine (see
FIG. 12) at 1395.
[0113] FIG. 14 is an exemplary flowchart depicting steps that
selectively ingest post data from a forum into a corpus utilized by
a question answering (QA) system. FIG. 14 processing commences at
1400 and shows the steps taken by a process that performs a routine
that selectively ingests post data to a knowledge base utilized by
a question answering (QA) system. At step 1410, the process selects
the first set of post data from forum tree 400.
[0114] At step 1420, the process compares the selected post data
(e.g., relevance data, sentiment data, etc.) to ingestion
thresholds (e.g., minimum relevance score, minimum sentiment score,
etc.). The ingestion thresholds are retrieved from data store 1430.
The process determines as to whether to ingest the selected post
data based on comparison performed in step 1420 (decision 1440). If
the determination is to ingest the selected post data based on
comparison, then decision 1440 branches to the `yes` branch
whereupon, at step 1450, the process ingests the selected post data
into knowledge base 106. The post data includes the text of the
post, the referential data of the post (the domain of the post,
questions posed in the post, the focus of the post, the concept of
the post, statements made in the post, and the Lexical Answer Type
(LAT) of the post). The post data also includes resolved anaphor
data such as resolved pronoun type anaphors, resolved fragment type
anaphors, and resolved agreement or disagreement type anaphors. The
post data further includes child and parent links to the selected
post, and relevance data such as relevance and sentiment scores of
the post. In one embodiment, the post data also includes leadership
data found for the post, relevance data found for the post, and
sentiment data found for the post.
[0115] On the other hand, if the determination is to avoid
ingestion of the selected post data based on the comparison
performed in step 1420, then decision 1440 branches to the `no`
branch. At step 1460, the process discards the selected post data
in inhibits ingestion of the post data into knowledge base 106.
[0116] The process determines as to whether there are more posts in
the forum or thread that need to be processed (decision 1470). If
there are more posts in the forum or thread that need to be
processed, then decision 1470 branches to the `yes` branch which
loops back to select and process the next post from post data 410
as described above. This looping continues until all of the posts
from post data 410 have been processed, at which point decision
1470 branches to the `no` branch and the selective ingestion
process ends at 1495.
[0117] While particular embodiments of the present disclosure have
been shown and described, it will be obvious to those skilled in
the art that, based upon the teachings herein, that changes and
modifications may be made without departing from this disclosure
and its broader aspects. Therefore, the appended claims are to
encompass within their scope all such changes and modifications as
are within the true spirit and scope of this disclosure.
Furthermore, it is to be understood that the disclosure is solely
defined by the appended claims. It will be understood by those with
skill in the art that if a specific number of an introduced claim
element is intended, such intent will be explicitly recited in the
claim, and in the absence of such recitation no such limitation is
present. For non-limiting example, as an aid to understanding, the
following appended claims contain usage of the introductory phrases
"at least one" and "one or more" to introduce claim elements.
However, the use of such phrases should not be construed to imply
that the introduction of a claim element by the indefinite articles
"a" or "an" limits any particular claim containing such introduced
claim element to disclosures containing only one such element, even
when the same claim includes the introductory phrases "one or more"
or "at least one" and indefinite articles such as "a" or "an"; the
same holds true for the use in the claims of definite articles.
* * * * *