U.S. patent application number 15/718829 was filed with the patent office on 2018-09-20 for cognitive lexicon learning and predictive text replacement.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Pasquale A. Catalano, Andrew G. Crimmins, Arkadiy O. Tsfasman, John S. Werner.
Application Number | 20180267955 15/718829 |
Document ID | / |
Family ID | 63520069 |
Filed Date | 2018-09-20 |
United States Patent
Application |
20180267955 |
Kind Code |
A1 |
Catalano; Pasquale A. ; et
al. |
September 20, 2018 |
COGNITIVE LEXICON LEARNING AND PREDICTIVE TEXT REPLACEMENT
Abstract
A method comprising of receiving a first communication content
directed to a user. The first communication content includes one or
a combination of the following: content read by the user and
content written by the user. The method also comprises of
generating tokens corresponding to the first communication content
by applying natural language processing and generating a token
frequency index for the user, based on the tokens generated from
the first communication content. The method determines a lexicon
reading level for the user, based on the token frequency index
generated for the user. The lexicon reading level indicates a
reading level of the user. The method adds the lexicon reading
level to a lexicon profile of the user. The method modifies a
second communication content by replacing tokens with synonyms of
the tokens based on comparing the difficulty ratings of the tokens
with the user's lexicon reading level.
Inventors: |
Catalano; Pasquale A.;
(Wallkill, NY) ; Crimmins; Andrew G.; (Montrose,
NY) ; Tsfasman; Arkadiy O.; (Wappingers Falls,
NY) ; Werner; John S.; (Fishkill, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
63520069 |
Appl. No.: |
15/718829 |
Filed: |
September 28, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15461511 |
Mar 17, 2017 |
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15718829 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 51/32 20130101;
G06F 40/242 20200101; G06F 40/247 20200101; H04W 4/14 20130101;
G06F 40/284 20200101 |
International
Class: |
G06F 17/27 20060101
G06F017/27; H04W 4/14 20090101 H04W004/14; H04L 12/58 20060101
H04L012/58 |
Claims
1. A computer-implemented method of making online text align with a
reading level of a user, the computer-implemented method
comprising: receiving a publication content read by the user;
generating tokens corresponding to text of the publication content
by applying natural language processing (NLP); generating a token
frequency index for the user, based on the tokens generated from
the publication content; determining a reading level for the user,
based on the token frequency index generated for the user, a source
of difficulty ratings of specified tokens, and a context of the
source of the publication content; recording the reading level in a
user profile; receiving email message text selected by the user;
receiving the reading level of the user; performing a tokenization
of the email message text by applying NLP to the email message text
and accessing a computer-readable dictionary to determine tokens
from the email message text; generating a plurality of tokens,
based on the tokenization of the email message text; determining a
difficulty rating of a first token of the plurality of tokens
including accessing the source of difficulty ratings for specified
tokens and matching the first token to a specified token;
determining whether the difficulty rating for the first token of
the plurality of tokens differs from the reading level of the user;
responsive to determining that the difficulty rating of the first
token exceeds the reading level of the user, replacing the first
token of the plurality of tokens with a replacement token, the
replacement token identified as being a synonym of the first token
that is consistent with the reading level of the user according to
a word difficulty index and a thesaurus stored in a text
replacement database; modifying the email message text to include
the replacement token for the first token of the plurality of
tokens; and responsive to determining the difficulty rating of the
first token of the plurality of tokens does not exceeds the reading
level of the user, leaving the first token of the plurality of
tokens unchanged.
Description
BACKGROUND OF THE INVENTION
[0001] The present invention relates to the field of natural
language processing, and more specifically, text replacement
utilizing tokenization from natural language processing.
[0002] Communication content consists of text, audio and even
transformation of images to text by object recognition, expressed
in a computer-readable format. This content is user-generated and
consists of both professional and personal written works. Examples
of communication content include websites, books, publications, and
social media posts. Some communication content, such as social
media posts, often contain metadata about the content to help
provide not only content, but context. Metadata often includes
information about location, engagement, and links shared.
Communication content provides some insight on the content creator,
as content parsed from the communication content can be utilized by
a number of applications. For example, social media posts may be
parsed to help identify appropriate targeted advertising.
[0003] Natural language processing is a field concerned with the
interactions between computers and human (natural) languages.
Tokenization is the process of utilizing natural language
processing to break-up a stream of text into words, phrases,
symbols, or other meaningful elements called tokens. Tokenization
typically occurs at the word level and takes into consideration
punctuation, spaces, contractions, hyphens, and emoticons. Tokens
generated from content may become input for further processing.
[0004] Matching readers with appropriate books based on reader
level is done in elementary schools and through online
applications. Users can receive a reading level score based on
reading comprehension tests. Software that examines a document's
reading demand or difficulty level are also available to use by
students and teachers.
SUMMARY
[0005] Embodiments of the present invention disclose a method, a
computer program product, and a system for making online text align
with a reading level of a user. The method comprises of receiving a
publication content read by the user; generating tokens
corresponding to text of the publication content by applying
natural language processing (NLP); generating a token frequency
index for the user, based on the tokens generated from the
publication content; determining a reading level for the user,
based on the token frequency index generated for the user, a source
of difficulty ratings of specified tokens, and a context of the
source of the publication content; recording the reading level in a
user profile; receiving email message text selected by the user;
receiving the reading level of the user; performing a tokenization
of the email message text by applying NLP to the email message text
and accessing a computer-readable dictionary to determine tokens
from the email message text; generating a plurality of tokens,
based on the tokenization of the email message text; determining a
difficulty rating of a first token of the plurality of tokens
including accessing the source of difficulty ratings for specified
tokens and matching the first token to a specified token;
determining whether the difficulty rating for the first token of
the plurality of tokens differs from the reading level of the user;
responsive to determining that the difficulty rating of the first
token exceeds the reading level of the user, replacing the first
token of the plurality of tokens with a replacement token, the
replacement token being a synonym of the first token that is
consistent with the reading level of the user; modifying the second
communication content to include the replacement token for the
first token of the plurality of tokens; and responsive to
determining the difficulty rating of the first token of the
plurality of tokens does not exceeds the lexicon reading level of
the user, replacing the first token of the plurality of tokens with
a replacement token.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 is a functional block diagram illustrating a
distributed data processing environment, in accordance with one
embodiment of the present invention.
[0007] FIG. 2 illustrates operational steps of a lexicon learning
program, on a computer server, within the distributed data
processing environment of FIG. 1, in accordance with an embodiment
of the present invention.
[0008] FIG. 3 illustrates operational steps of a text replacement
program, on the computer server, within the distributed data
processing environment of FIG. 1, in accordance with an embodiment
of the present invention.
[0009] FIG. 4 depicts a block diagram of components of a computing
system, which includes a computing device capable of operating the
lexicon learning program of FIG. 2 and the text replacement program
of FIG. 3, in accordance with an embodiment of the present
invention.
DETAILED DESCRIPTION
[0010] Embodiments of the present invention recognize that
particular words contained in documents read by people are often
not understood. This is because the words are too technical or
foreign to the reader. Often, readers naturally understand meanings
of words foreign to them by using the context of the words in the
document. However, this does not occur all the time. Because of
this, readers are left not fully understanding parts of the
document they are reading. Readers are able to simply skip words,
which risks incorrect understanding of the content, or are able to
look up words in a dictionary but this may be time-consuming or
cumbersome.
[0011] Embodiments of the present invention provide a method to
determine a user's lexicon based on communication content sources,
such as websites, books, publications, and social media posts, and
predictively replace content in communication content that is
determined to be above the consumer's reading level with a synonym,
or a definitional phrase of the word in question that is within the
user's reading level. The difficulty level of the communication
content can be adjusted to a reader's level using a learned lexicon
of the particular user.
[0012] The present invention will now be described in detail with
reference to the Figures. FIG. 1 is a functional block diagram
illustrating a distributed data processing environment, generally
designated 100, in accordance with one embodiment of the present
invention. FIG. 1 provides only an illustration of one
implementation and does not imply any limitations with regard to
the environments in which different embodiments may be implemented.
Many modifications to the depicted environment may be made by those
skilled in the art without departing from the scope of the
invention as recited by the claims.
[0013] Distributed data processing environment 100 includes user
device 104, which further includes user interface 106; computer
server 108, which further includes lexicon learning program 200,
text replacement program 300, and database 114; all of which are
interconnected via network 102.
[0014] Network 102 can be, for example, a telecommunications
network, a local area network (LAN), a wide area network (WAN),
such as the Internet, or a combination of the three, and can
include wired, wireless, or fiber optic connections. Network 102
can include one or more wired and/or wireless networks that are
capable of receiving and transmitting data, voice, and/or video
signals, including multimedia signals that include voice, data, and
video information. In general, network 102 can be any combination
of connections and protocols that will support communications
between user device 104, computer server 108, and other computing
devices (not shown) within distributed data processing environment
100.
[0015] User device 104 allows users access to user interface 106,
which in turn allows users access to lexicon learning program 200
and text replacement program 300. User device 104 can be a can be a
laptop computer, a tablet computer, a netbook computer, a personal
computer (PC), a desktop computer, a personal digital assistant
(PDA), a smart phone, or any other programmable electronic device
capable of communicating with various components and devices within
distributed data processing environment 100, via network 102. In
general, user device 104 represent any programmable electronic
mobile device or combination of programmable electronic mobile
devices capable of executing machine readable program instructions
and communicating with other computing devices (not shown) within
distributed data processing environment 100 via a network, such as
network 102. User device 104 includes user interface 106. In some
embodiments of the present invention, user device 104 may include
internal and external hardware components, as depicted and
described in further detail with respect to FIG. 4.
[0016] User interface 106 provides an interface to lexicon learning
program 200 and text replacement program 300 on computer server 108
for a user of user device 104. In one embodiment of the present
invention, user interface 106 may be a graphical user interface
(GUI) or a web user interface (WUI) and can display text,
documents, web browser windows, user options, application
interfaces, and instructions for operation, and include the
information (such as graphic, text, and sound) that a program
presents to a user and the control sequences the user employs to
control the program. In another embodiment, user interface 106 may
also be mobile application software that provides an interface
between a user of user device 104 and computer server 108.
Application software, or an "app," is a computer program designed
to run on computing devices, smart phones, tablet computers and
other mobile devices. User interface 106 enables the user of user
device 104 to create a user lexicon profile on lexicon learning
program 200, which determines a lexicon reading level for the user.
User interface 106 may also enable the user of user device 104 to
input communication content to text replacement program 300.
[0017] Computer server 108 can be a standalone computing device, a
management server, a web server, a mobile computing device, or any
other electronic device or computing system capable of receiving,
sending, and processing data. In other embodiments, computer server
108 can represent a server computing system utilizing multiple
computers as a server system, such as in a cloud computing
environment. In another embodiment, computer server 108 can be a
laptop computer, a tablet computer, a netbook computer, a personal
computer (PC), a desktop computer, a personal digital assistant
(PDA), a smart phone, or any other programmable electronic device
capable of communicating with user device 104 and other computing
devices (not shown) within distributed data processing environment
100 via network 102. In another embodiment, computer server 108
represents a computing system utilizing clustered computers and
components (e.g., database server computers, application server
computers, etc.) that act as a single pool of seamless resources
when accessed within distributed data processing environment 100.
Computer server 108 includes lexicon learning program 200, text
replacement program 300, and database 114. Lexicon learning program
200 and text replacement program 300 are interconnected with
database 114 by communication bus 110 and communication bus 112
respectively. In some embodiments of the present invention, lexicon
learning program 200 and text replacement program 300 are
communicatively connected to database 114. In other embodiments,
lexicon learning program 200, text replacement program 300, and
database 114 are accessible to computer server 108 via network 102
(not shown). Computer server 108 may include internal and external
hardware components, as depicted and described in further detail
with respect to FIG. 4.
[0018] Lexicon learning program 200 determines a lexicon reading
level of a user, based on communication content users of user
interface 106 have read, written and received as input. Other media
types, such as video or audio content the user has viewed, listened
too, or orated many also be received as inputs. A user of user
interface 106 uses lexicon learning program 200 to register and
create a unique user lexicon profile. Lexicon learning program 200
receives input of communication content that the user has read and
written. Communication content utilized in lexicon learning program
200 are written to computer-readable media. Lexicon learning
program 200 uses natural language processing tokenization to
tokenize the communication content. Lexicon learning program 200
generates a lexicon frequency index using tokens established from
tokenization of communication content. The lexicon frequency index
is based on how often particular tokens are used within the
communication content sources received as input. In some
embodiments of the present invention, lexicon learning program 200
updates the lexicon frequency index based on receipt of new
communication content. Lexicon learning program 200 determines a
lexicon reading level for a specific user based on the user's
lexicon frequency index. Lexicon learning program 200 is depicted
and described in further detail with respect to FIG. 2.
[0019] Text replacement program 300 replaces tokens in
communication content, which is determined to have a higher
difficulty rating than a user's lexicon reading level, with tokens
that have a difficulty rating within the user's lexicon reading
level. The text replacement program 300 may also replace tokens
that are at the user's lexicon reading level, or lower, but do not
appear in the user's lexicon frequency index. Text replacement
program 300 receives unconsumed communication content for natural
language processing by a user of user interface 106. Text
replacement program 300 uses natural language processing to
tokenize the entirety of the communication content. Text
replacement program 300 determines a difficulty rating of a token,
and determines if the token difficulty rating is greater than the
user's lexicon reading level. If the token's difficulty rating is
greater than the user's lexicon reading level, then text
replacement program 300 replaces the token with a lower difficulty
rated like-meaning synonym token similar to the user's lexicon
reading level. If the token's difficulty rating is less than or
equal to the user's lexicon reading level, then text replacement
program 300 leaves the token unchanged. Text replacement program
300 replaces the higher difficulty token within the communication
content with a lower difficulty token if necessary. Text
replacement program 300 determines if there are additional tokens
in the communication content that have yet to be analyzed. If there
are additional tokens in the communication content that have not
been analyzed, text replacement program 300 proceeds to the next
token to identify, determine difficulty rating, and replace, if
appropriate. If there are no additional tokens in the communication
content that need to be analyzed, text replacement program 300
outputs the communication content updated with replaced tokens.
Text replacement program 300 is depicted and described in further
detail with respect to FIG. 3.
[0020] Database 114 is a repository for data used by lexicon
learning program 200 and text replacement program 300. In the
depicted embodiment, database 114 resides on computer server 108.
In another embodiment of the present invention, database 114 may
reside elsewhere within distributed data processing environment 100
provided lexicon learning program 200 and text replacement program
300 have access to database 114, for example, via network 102. A
database is an organized collection of data and the data relative
to embodiments of the present invention that are included in
database 114 are associated with functions of lexicon learning
program 200 and text replacement program 300. Database 114 stores
communication content associated with user's lexicon profile and
lexicon reading level data associated with users of user interface
106. Database 114 may also store metadata regarding communication
content sources and a lexicon frequency index to lexicon reading
level algorithms. For example, a user of lexicon learning program
200 creates a lexicon profile and links several communication
content sources. A document is uploaded for text replacement. The
user uploads a document for text replacement. Database 114 would
store the lexicon profile and the lexicon reading level outputted
from lexicon learning program 200 as well as the communication
content sources received as input by the user. The user would not
have to re-upload communication content sources for future text
replacement needs, however, the user may add additional
communication content sources or remove previously linked
communication content sources through time. Database 114 would also
store metadata regarding the communication content sources linked
to the user's lexicon profile in order to create the user's lexicon
frequency index. Database 114 may also store the outputted document
with text replacements for the user to access and may store
received documents and text-updated documents of text replacement
program 300 for future text replacement needs. Database 114 may
also store a dictionary, thesaurus, and word difficulty indexes
that can be referenced by lexicon learning program 200 and text
replacement program 300. Information on database 114 may be
transferred or stored over network 102. Database 114 can be
implemented with various types of storage devices capable of
storing data and configuration files accessed and utilized by
computer server 108, such as a database server, a hard disk drive,
or a flash memory.
[0021] FIG. 2 illustrates operational steps of lexicon learning
program 200, on computer server 108, within distributed data
processing environment 100 of FIG. 1, for generating a lexicon
reading level based on communication content users have received as
input, in accordance with an embodiment of the present
invention.
[0022] Lexicon learning program 200 creates a lexicon profile (step
210). In an embodiment of the present invention, a user registers a
lexicon profile with lexicon learning program 200 and links
communication content sources. A user accesses user device 104 of
FIG. 1 via user interface 106 of FIG. 1. Lexicon learning program
200 accesses the linked communication content sources.
Communication content consists of content expressed in a
computer-readable format. Communication content sources include
sources users have read or written. Communication content sources
read by a user that can be linked to a user's lexicon profile may
include websites, books, and online journals. Communication content
sources written by a user that can be linked to a user's lexicon
profile may include publications, social media posts, emails, SMS
text messages, and locally stored documents. Once registered, the
lexicon profile may be accessed by the user and the user may link
or unlink communication content sources. For example, a user of
lexicon learning program 200 creates a lexicon profile with lexicon
learning program 200. The user links communication content sources
to the lexicon profile. The user links the user's typed text
messages, the user's typed emails, the user's social media profiles
and the user's authored short story to the user's lexicon profile.
Lexicon profiles may be stored on database 114 of FIG. 1.
[0023] Lexicon learning program 200 receives communication content
from communication content sources (step 220). In an embodiment of
the present invention, lexicon learning program 200 accesses and
retrieves computer-readable communication content sources to which
a user has linked to the user's lexicon profile. For example, a
user of lexicon learning program 200 can link the user's lexicon
profile to an account on a social cataloging application like
"Goodreads" so that lexicon learning program 200 may access books
the user has read. ("Goodreads" may be subject to trademark rights
in various jurisdictions throughout the world and is used here only
in reference to the products or services properly denominated by
the marks to the extent that such trademark rights may exist). In
another example, a user of lexicon learning program 200 can link
the user's lexicon profile to a personal Twitter.RTM. account so
that lexicon learning program 200 may access social media posts the
user has written or read (Twitter is a registered trademark of
Twitter Inc. in the U.S., and other countries worldwide). In
another embodiment, lexicon learning program 200 accesses audio
communication content linked to a lexicon profile. Lexicon learning
program 200 utilizes speech-to-text recognition software to
transform audio communication into a computer-readable format. For
example, a user links an audio recording of an oral presentation to
the user's lexicon profile. Lexicon learning program 200 utilizes
speech-to-text software to transform the audio recording into a
computer-readable format in order to utilize it as a communication
content input.
[0024] Lexicon learning program 200 uses natural language
processing tokenization of the entirety of the communication
content sources received (step 230). Lexicon learning program 200
receives the series of characters (alpha characters, numeric
characters and punctuation marks or emoticons) that make up the
content belonging to communication content sources and generates
tokens from the content. Tokenization is the process of utilizing
natural language processing to break-up a stream of text into
words, phrases, symbols, or other meaningful elements. Tokenization
takes into consideration punctuation, spaces, contractions,
hyphens, and emoticons. For example, the text phrase "Friends,
Romans, Countrymen, lend me your ears;" would likely generate the
following tokens: "Friends", "Romans", "Countrymen", "lend", "me",
"your" and "ears". In an embodiment of the present invention,
lexicon learning program 200 accesses lexicons such as a computer
readable dictionary, or simply a word list, to determine tokens
within communication content sources. In some embodiments, lexicons
such as computer readable dictionaries or word lists may be stored
in database 114 of FIG. 1.
[0025] Lexicon learning program 200 generates a lexicon frequency
index for the user based on tokens obtained from communication
content sources (step 240). The lexicon frequency index may be
generated and updated as more tokens are obtained from
communication content sources. The lexicon frequency index is based
on how often particular tokens are used within the communication
content sources received. The lexicon frequency index identifies
tokens that a user reads or writes and keeps track of the frequency
of which the tokens appear in the written or read content. For
example, the text phrase "That's one small step for man, one giant
leap for mankind" would generate tokens for each word of the phrase
through natural language processing tokenization as described
above. The lexicon frequency index would tabulate the tokens "one"
and "for" as being used twice and the tokens "That's", "small",
"step", "man", "giant", "leap" and "mankind" as being used once. In
an embodiment of the present invention, tokens generated from
communication content sources written by a user are weighted higher
on the lexicon frequency index than tokens generated from
communication content sources read by a user. In another
embodiment, tokens generated from communication content sources
written by a user are weighted lower on the lexicon frequency index
than tokens generated from communication content sources read by a
user. In yet another embodiment, tokens generated from
communication content sources written by a user and tokens
generated from communication content sources read by a user are
equally weighted. Correct use of the token, by the user, may also
impact weighting. In an embodiment, an aging algorithm is utilized
in generating the lexicon frequency index that determines if tokens
have not been used for a period of time to account for a user's
current vocabulary. If a token has not been used for a period of
time, the token is removed from the user's lexicon frequency index.
In another embodiment, lexicon program 200 ignores pronouns,
articles, and conjunctions, and does not include them in the
lexicon frequency index to not overpopulate the lexicon frequency
index with commonly used words such as "the" and "a". Referencing
the above example, lexicon program 200 would ignore the tokens
"That's" and "for" as they are an article and a conjunction
respectively.
[0026] Having built the lexicon frequency index for the user,
lexicon learning program 200 determines whether there is additional
communication content to analyze (decision step 250). If additional
communication content sources are received, lexicon learning
program 200 updates the lexicon frequency index. Additional
communication content sources results in additional tokens being
generated, which may be added to the lexicon frequency index. For
the case in which lexicon learning program 200 recognizes
additional communication content sources were linked to the user's
lexicon profile (step 250, "YES" branch), lexicon learning program
200 returns to step 220 to receive additional communication content
sources and proceeds as described above. In this case, lexicon
learning program 200 receives the additional communication content
source and proceeds to utilize tokenization to update the lexicon
frequency index for the specific user.
[0027] In the case in which lexicon learning program 200 does not
recognize additional communication content sources were linked to
the user's lexicon profile, lexicon learning program 200 does not
receive additional communication content (step 250, "NO" branch).
In this case, lexicon learning program 200 may determine that
received input is disregarded with respect to updating the lexicon
frequency index, based on content volume, source, or other
attributes of the input. In such cases, and subsequent to updating
the lexicon frequency index, lexicon learning program 200 proceeds
to determine a lexicon reading level for the user (step 260). A
lexicon reading level is determined based on a particular user's
lexicon profile. In some embodiments of the present invention, the
lexicon reading level is a number that signifies the reading
proficiency of the user. The higher the lexicon reading level
number, the more proficient the user is at reading and vocabulary.
For example, a user reading at a 12.sup.th grade reading level will
likely have a higher lexicon reading level than a user reading at a
6.sup.th grade reading level. In other embodiments, other
indicators of lexicon reading level may be used, for example,
letters, symbols, or descriptive words.
[0028] The lexicon reading level is calculated based on the lexicon
frequency index and is determined by detecting patterns of sentence
structures, vocabulary, and frequency of use from the communication
content sources via machine learning and pattern recognition
techniques, as would be appreciated by one with skill in the art.
The lexicon reading level of a user implies the same reading level
in user written communications. Machine learning explores the study
and construction of algorithms that can learn from and make
predictions based on data. Such algorithms operate by building a
model from example inputs in order to make data-driven predictions
or decisions expressed as outputs, rather than following strictly
static program instructions. Within the field of data analytics,
machine learning is a method used to devise complex models and
algorithms that lend themselves to decisions, and probability
related prediction. These analytical models enable researchers,
data scientists, engineers, and analysts to produce reliable,
repeatable decisions and results and to uncover hidden insights
through learning from historical relationships and trends in the
data. Pattern recognition is a branch of machine learning that
focuses on the recognition of patterns and regularities in data.
Pattern recognition systems may be trained from labeled "training"
data (supervised learning), but when no labeled data are available,
other algorithms can be used to discover previously unknown
patterns (unsupervised learning). Lexicon learning program 200
utilizes the lexicon frequency indexes of individuals to determine
comparable lexicon reading levels. As additional users utilize
lexicon learning program 200, lexicon learning program 200 analyzes
generated lexicon frequency indexes to determine patterns of
context and frequency of tokens by comparing with other user's
lexicon frequency indexes. Lexicon learning program 200 develops
algorithms based on the patterns and trends found in users'
communication content sources to determine an approximate lexicon
reading level. All tokens are assigned a token difficulty rating as
described in step 330 of FIG. 3. In an embodiment of the present
invention, once a word is assigned a token difficulty rating, a
lexicon reading level may be determined by taking the lexicon
frequency index multiplied by the difficulty ratings of the tokens
(a token difficulty index) all divided by the total number of
tokens. Other formats for determining reading levels for users
utilizing the lexicon frequency index may be implemented in other
embodiments. Lexicon reading levels linked to lexicon profiles may
be stored on database 114 of FIG. 1.
[0029] FIG. 3 illustrates operational steps of text replacement
program 300, on computer server 108, within distributed data
processing environment 100 of FIG. 1, for predictively replacing
tokens in communication content with tokens that have a difficulty
rating within a user's lexicon reading level, in accordance with an
embodiment of the present invention.
[0030] Text replacement program 300 receives communication content
that is not previously consumed by the user (step 310). Unconsumed
communication content may be any media that can be translated into
text and utilized in text replacement, and for simplicity is
referred to as a document. A document containing written text is
uploaded to be processed by text replacement program 300. Users of
user interface 106 of FIG. 1 may upload a document, provide a link
to a document, or run text replacement program 300 as an applet in
a web-browser that constantly analyzes new pages as they are
loaded. The document contains text that the user intends to read
and not a document that the user has written. In some embodiments
of the present invention, the user has created a lexicon profile.
In other embodiments, the user has linked communication content
sources to the lexicon profile and has a lexicon reading level
determined by lexicon learning program 200, in connection to the
lexicon profile. For example, a user has created a lexicon profile
and has a determined lexicon reading level. The user inputs a
work-related document containing highly technical content that the
user is not familiar with. The user does not understand particular
words in the work-related document and would prefer to replace the
unfamiliar content with words the user can understand. In yet other
embodiments, text replacement program 300 utilizes speech-to-text
recognition software to transform audio communication into a
computer-readable format. For example, text replacement program 300
receives an audio recording of an oral presentation. Text
replacement program 300 utilizes speech-to-text software to
transform the audio recording into a computer-readable format in
order to utilize it as an uploaded communication content.
[0031] Text replacement program 300 uses natural language
processing tokenization of the document received (step 320). Text
replacement program 300 receives the series of characters (alpha
characters, numeric characters and punctuation marks or emoticons)
that make up the content of the document and generates tokens from
the text. Tokenization is processed, as described in step 230 of
FIG. 2, to break-up a stream of text into words, phrases, symbols,
or other meaningful elements. Tokenization takes into consideration
punctuation, spaces, contractions, hyphens, and emoticons. For
example, the text phrase "brevity is the soul of wit" would likely
generate the following tokens: "brevity", "is", "the", "soul",
"of", and "wit". In an embodiment of the present invention, text
replacement program 300 accesses a source of words, such as a
computer readable dictionary, or simply a word list, to determine
tokens within communication content sources. A source of words,
such as computer readable dictionaries or word lists, may be stored
on database 114 of FIG. 1.
[0032] After tokenizing the entire document, text replacement
program 300 determines a difficulty rating for a token (step 330)
utilizing natural language processing. Each token is assigned a
specific token difficulty rating. A token difficulty rating for a
token reflects how likely a language speaker would know the token.
In some embodiments of the present invention, a difficulty rating
is a number signifying the reading level required to understand the
token. In other embodiments, the difficulty rating can be indicated
with a term, a character or a symbol. In yet other embodiments, a
higher difficulty rating indicates the word or term is more
advanced, and more difficult to understand. In still other
embodiments, different progressions of token difficulty ratings are
utilized. Token-difficulty rating assignments may be stored on
database 114 of FIG. 1.
[0033] In an embodiment of the present invention, text replacement
program 300 utilizes natural language processing to determine a
difficulty rating of sentence structures. Each sentence is paired
with a specific difficulty rating based on sentence structure
aspects such as word contexts and uses of complicated linguistic
phrases such as double negatives, eggcorns, portmanteaus, and
colloquialisms. This embodiment is reflected in the following steps
of text replacement program 300 where difficulty ratings of
sentence structures are used in comparison with a user's lexicon
reading level.
[0034] Upon determining the difficulty rating of the token, text
replacement program 300 determines whether the token difficulty
rating is greater than the user's lexicon reading level (decision
step 340). The user's lexicon reading level may be retrieved from
database 114 of FIG. 1. Token difficulty ratings and lexicon
reading levels are of the same magnitude and can be compared to
each other. For the case in which text replacement program 300
determines that the token difficulty rating is greater than the
user's lexicon reading level and the token does not appear in the
user's lexicon frequency index, (step 340, "YES" branch), text
replacement program 300 proceeds to replace the token with a lower
difficulty rated token (step 350). In this case, the lower
difficulty rated token is of equal or lesser value than the user's
lexicon reading level and is a like-meaning synonym of the original
token. For example, a token "ameliorate" may be replaced with the
token "improve." In this example, the token "ameliorate" has a
higher token difficulty rating than a user's lexicon reading level.
Text replacement program 300 replaces the token "ameliorate" with a
lower difficulty rated token "improve." In an embodiment of the
present invention, like-meaning synonyms that appear in the user's
lexicon frequency index may be favored as replacement tokens over a
like synonym token that does not appear in the list. In another
embodiment, like-meaning synonyms that do not appear in the user's
lexicon frequency index may be favored as replacement tokens.
[0035] In other embodiments of the present invention, text
replacement program 300 proceeds to replace the token with the
definition of the token. For example, a token "ameliorate" may be
replaced with the definition phrase "to make or become better, more
bearable, or more satisfactory." In this example, the token
"ameliorate" has a higher token difficulty rating than a user's
lexicon reading level. Text replacement program 300 replaces the
token "ameliorate" with its lower difficulty rated dictionary
definition. The token's dictionary definition is retrieved from
database 114 of FIG. 1.
[0036] For the case in which text replacement program 300
determines that the token difficulty rating is not greater than the
user's lexicon reading level, (step 340, "NO" branch), text
replacement program 300 proceeds to leave the token unchanged (step
360). In this case, the token is less than or equal to the user's
lexicon reading level and is unaffected by text replacement program
300.
[0037] After replacing the token with a lower difficulty rated
token (step 350), text replacement program 300 proceeds to update
the original document (step 370). If one or more tokens are
replaced with a lower difficulty rated token, the document is
modified to replace the original token with the lower difficulty
rated token, for each token determined to be replaced. In an
embodiment of the present invention, text replacement program 300
changes the font of the replaced token to highlight a replaced
token for the user. In another embodiment, text replacement program
300 italicizes the replaced token. In yet another embodiment, text
replacement program 300 changes the text color of the replaced
token. If a token of the document is not replaced, the modified
document includes the original token.
[0038] After regenerating and updating the document (step 370) or
leaving the token unchanged (step 360), text replacement program
300 determines if there are additional tokens to analyze (decision
step 380). For the case in which text replacement program 300
determines that there are additional tokens' difficulty ratings
that are present in the document that have not been compared to the
user's lexicon reading level, (step 380, "YES" branch), text
replacement program 300 returns to step 330 to determine the
difficulty rating of the next token. For example, text replacement
program 300 replaces the first token of a document with a lower
difficulty rated token. Text replacement program 300 updates the
document with the replaced token. Text replacement program 300
determines that there are additional tokens present in the document
that have not been compared with the user's lexicon reading level.
Therefore, text replacement program 300 proceeds to the second
token and determines the difficulty rating of the second token.
[0039] For the case in which text replacement program 300
determines all tokens present in the document have been compared to
the user's lexicon reading level, (step 380, "NO" branch), text
replacement program 300 proceeds to output the updated document
(step 390). The outputted updated document may contain tokens
modified by text replacement program 300 to have a difficulty
rating equal to or lower than the user's lexicon reading level.
[0040] In an embodiment of the present invention, the user may
hover over the replaced text of the outputted updated document of
text replacement program 300 and view the original replaced tokens
using user interface 106 of FIG. 1. For example, the token
"ameliorate" is replaced with the token "improve" by text
replacement program 300 in a document. Text replacement program 300
outputs the document. The user may hover over the text reading
"improve" and the document would display the original replaced
token "ameliorate" in the place of the text reading "improve."
[0041] In another embodiment of the present invention, text
replacement program 300 replaces a lower difficulty token with a
higher difficulty rated token for education use to promote reading
skills and introduce new vocabulary to a user. Upon determining the
difficulty rating of the token, text replacement program 300
determines whether the token difficulty rating is less than the
user's lexicon reading level. For the case in which text
replacement program 300 determines that the token difficulty rating
is less than the user's lexicon reading level, text replacement
program 300 proceeds to replace the token with a higher difficulty
rated token. In this case, the higher difficulty rated token is of
equal or greater value than the user's lexicon reading level and is
a like-meaning synonym of the original token. For example, a token
"improve" may be replaced with the token "ameliorate." In this
example, the token "improve" has a lower token difficulty rating
than a user's lexicon reading level. Text replacement program 300
replaces the token "improve" with the higher difficulty rated
synonym "ameliorate." For the case in which text replacement
program 300 determines that the token difficulty rating is not
lower than the user's lexicon reading level, text replacement
program 300 proceeds to leave the token unchanged. In this case,
the token is greater than or equal to the user's lexicon reading
level and is unaffected by text replacement program 300.
Embodiments of the present invention in which text replacement
program 300 replaces tokens of a document in a "reverse mode", for
educational purposes, replace tokens of a document with higher
difficulty tokens. In an embodiment, text replacement program 300
determines a limit on how many tokens get replaced in a document so
that a user would not be overwhelmed by the number of new replaced
words. The user may modify this limit of text replacement program
300.
[0042] In yet another embodiment of the present invention, text
replacement program 300 may recommend a user, who has a lexicon
reading level greater than or equal to a particular document's
difficulty rating, to provide assistance to a user attempting to
read the particular document. Natural language processing can
obtain a difficulty rating for an entire document. Upon determining
the difficulty rating of a document, text replacement program 300
determines whether the document's difficulty rating is less than or
equal to a user's lexicon reading level. If the document's
difficulty rating is less than or equal to the user's lexicon
reading level, text replacement program 300 makes no
recommendation. If the document's difficulty rating is greater than
a first user's lexicon reading level, text replacement program 300
may recommend to the first user, a second user who has a lexicon
reading level greater than or equal to a document's difficulty
rating. For example, user A wants to read and understand a
document. User A's lexicon reading level is below the document's
difficulty rating and user B's lexicon reading level is above the
document's difficulty rating. Text replacement program 300
recommends user B to user A to assist in reading and understanding
the document. User B may be a "friend" or "follower" of user A in
one of user A's linked social media accounts. Text replacement
program 300 proceeds with text replacement as described above. In
still another embodiment, text replacement program 300 may
recommend a user, who has a large number of matching tokens in
their lexicon frequency index that overlap a particular document's
tokens, to provide assistance to a user attempting to read the
particular document. For example, user A wants to read and
understand a document and user A's lexicon reading level is below
the document's difficulty rating. User B's lexicon frequency index
contains many of the same tokens as the tokens in the document.
Text replacement program 300 recommends user B to user A to assist
in reading and understanding the document.
[0043] FIG. 4 depicts a block diagram of components of computing
system 400, which includes computing device 405, which is capable
of operating lexicon learning program 200 of FIG. 2 and text
replacement program 300 of FIG. 3, in accordance with an embodiment
of the present invention. It should be appreciated that FIG. 4
provides only an illustration of one implementation and does not
imply any limitations with regard to the environments in which
different embodiments may be implemented. Many modifications to the
depicted environment may be made.
[0044] Computing device 405, includes components and functional
capability similar to computer server 108 and user device 104, in
accordance with an illustrative embodiment of the present
invention. Computing device 405 includes communications fabric 402,
which provides communications between computer processor(s) 404,
memory 406, persistent storage 408, communications unit 410, and
input/output (I/O) interface(s) 412. Communications fabric 402 can
be implemented with any architecture designed for passing data
and/or control information between processors (such as
microprocessors, communications and network processors, etc.),
system memory, peripheral devices, and any other hardware
components within a system. For example, communications fabric 402
can be implemented with one or more buses.
[0045] Memory 406, cache memory 416, and persistent storage 408 are
computer readable storage media. In this embodiment, memory 406
includes random access memory (RAM) 414. In general, memory 406 can
include any suitable volatile or non-volatile computer readable
storage media.
[0046] In some embodiments of the present invention, lexicon
learning program 200 and text replacement program 300 are stored in
persistent storage 408 for execution by one or more of the
respective computer processors 404 via one or more memories of
memory 406. In these embodiments, persistent storage 408 includes a
magnetic hard disk drive. Alternatively, or in addition to a
magnetic hard disk drive, persistent storage 408 can include a
solid state hard drive, a semiconductor storage device, read-only
memory (ROM), erasable programmable read-only memory (EPROM), flash
memory, or any other computer readable storage media that is
capable of storing program instructions or digital information.
[0047] The media used by persistent storage 408 may also be
removable. For example, a removable hard drive may be used for
persistent storage 408. Other examples include optical and magnetic
disks, thumb drives, and smart cards that are inserted into a drive
for transfer onto another computer readable storage medium that is
also part of persistent storage 408.
[0048] Communications unit 410, in these examples, provides for
communications with other data processing systems or devices,
including resources of distributed data processing environment 100,
user device 104, and computer server 108. In these examples,
communications unit 410 includes one or more network interface
cards. Communications unit 410 may provide communications through
the use of either or both physical and wireless communications
links. Lexicon learning program 200 and text replacement program
300 may be downloaded to persistent storage 408 through
communications unit 410.
[0049] I/O interface(s) 412 allows for input and output of data
with other devices that may be connected to computing system 400.
For example, I/O interface 412 may provide a connection to external
devices 418 such as a keyboard, keypad, a touch screen, and/or some
other suitable input device. External devices 418 can also include
portable computer readable storage media such as, for example,
thumb drives, portable optical or magnetic disks, and memory cards.
Software and data used to practice embodiments of the present
invention, e.g., lexicon learning program 200 and text replacement
program 300 can be stored on such portable computer readable
storage media and can be loaded onto persistent storage 408 via I/O
interface(s) 412. I/O interface(s) 412 also connect to a display
420.
[0050] Display 420 provides a mechanism to display data to a user
and may be, for example, a computer monitor.
[0051] The programs described herein are identified based upon the
application for which they are implemented in a specific embodiment
of the invention. However, it should be appreciated that any
particular program nomenclature herein is used merely for
convenience, and thus the invention should not be limited to use
solely in any specific application identified and/or implied by
such nomenclature.
[0052] 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.
[0053] 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.
[0054] 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.
[0055] 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.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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.
[0060] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
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 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.
* * * * *