U.S. patent application number 12/822991 was filed with the patent office on 2011-05-26 for data classification based on point-of-view dependency.
This patent application is currently assigned to BIZ360, Inc.. Invention is credited to Philip Chan, Daniel Gartung, John Rotherham.
Application Number | 20110125747 12/822991 |
Document ID | / |
Family ID | 42358919 |
Filed Date | 2011-05-26 |
United States Patent
Application |
20110125747 |
Kind Code |
A1 |
Gartung; Daniel ; et
al. |
May 26, 2011 |
DATA CLASSIFICATION BASED ON POINT-OF-VIEW DEPENDENCY
Abstract
Data classification is used to classified input items by
associating the input items with one or more classes from a set of
one or more classes in a data classification system, including
identifying relevant features in an input item to form a feature
vector for the input item, receiving at the data classification
system an indication of a point-of-view, adjusting the feature
vector according to the point-of-view indication or modifying a
pattern discriminator (e.g., trainer and classifier) to
inline-process feature vectors depending on the provided
point-of-view (e.g., SVM custom kernels), and classifying the input
item into the set of classes according to the point-of-view. The
point-of-view data can be introduced either as a pre-process step
prior to passing it off to the pattern discrimination algorithm, or
can be incorporated directly into the pattern discrimination
algorithm if applicable. The pattern discrimination algorithms can
detect arbitrary patterns given a similarly prepared dataset during
both training and subsequent classification of unclassified
documents.
Inventors: |
Gartung; Daniel;
(Hillsborough, CA) ; Chan; Philip; (South San
Francisco, CA) ; Rotherham; John; (Sunnyvale,
CA) |
Assignee: |
BIZ360, Inc.
San Mateo
CA
|
Family ID: |
42358919 |
Appl. No.: |
12/822991 |
Filed: |
June 24, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10931291 |
Aug 30, 2004 |
7769759 |
|
|
12822991 |
|
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60499196 |
Aug 28, 2003 |
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Current U.S.
Class: |
707/737 ;
707/E17.046 |
Current CPC
Class: |
G06F 16/353
20190101 |
Class at
Publication: |
707/737 ;
707/E17.046 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A method of classification, wherein an input item is classified
by associating the input item with one or more classes from a set
of classes in a data classification system, said method comprising
the steps of: receiving the input item to be classified;
identifying relevant features in the input item to form a feature
vector for the input item; receiving an indication of a point of
view at the data classification system; adjusting the feature
vector or modifying a pattern discriminator according to the
point-of-view indication; and classifying the input item into the
set of classes according to the point-of-view.
2. The method of claim 1, wherein the step of adjusting the feature
vector comprises generating custom features.
3. The method of claim 1, wherein the step of adjusting the feature
vector comprises selecting a subset of features.
4. The method of claim 1, wherein the step of modifying a pattern
discrimination algorithm comprises generating a custom kernel.
5. The method of claim 1, wherein the step of adjusting the feature
vector comprises weighting features.
6. The method of claim 5, wherein weighting features uses proximity
weighting.
7. The method of claim 6, wherein proximity weighting calculates
weight of a feature as the maximum of 0.95 raised to the power of
FSP and 0.80 raised to the power of BSP, wherein FSP is the number
of sentences going forward from a nearest alias to the feature and
BSP is the number of sentences going backward from a nearest alias
to the feature, wherein an alias is a representation of a
point-of-view.
8. The method of claim 1, wherein the input item is selected from
the group consisting of a word processing document, an ASCII file,
an XML file, a UTF-8 file, a collection of documents with some
structural organization, an image, a text, a combination of images
and text, media, spreadsheet data, a collection of bytes, an
organization of data and a data stream.
9. A data classification system comprising at least one input item,
at least one feature vector, and at least one data classifier
defined by point-of-view dependency, wherein the data
classification system is configured to perform one or more of
feature generation, feature selection, feature weighting, and
custom kernel generation in order to rate and classify the input
item.
10. The data classification system of claim 9, wherein the input
item is selected from the group consisting of a word processing
document, an ASCII file, an XML file, a UTF-8 file, a collection of
documents with some structural organization, an image, a text, a
combination of images and text, media, spreadsheet data, a
collection of bytes, an organization of data and a data stream.
11. The data classification system of claim 9, wherein the data
classifier classifies one or more data sets based upon patterns
observed during a training process with one or more training data
sets.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Continuation of U.S. application Ser.
No. 10/931,291, filed Aug. 30, 2004, entitled DATA CLASSIFICATION
BASED ON POINT-OF-VIEW DEPENDENCY," (Attorney Docket No.
021389-000410US) now allowed, which claims priority from co-pending
U.S. Provisional Patent Application No. 60/499,196 filed Aug. 28,
2003 entitled DATA CLASSIFICATION BASED ON POINT-OF-VIEW
DEPENDENCY, all of which are hereby incorporated by reference, as
if set forth in full in this document, for all purposes.
FIELD OF THE INVENTION
[0002] The present invention relates to automated data
classification in general and data classifiers of documents based
on content in particular.
BACKGROUND OF THE INVENTION
[0003] Data classification systems are useful in many applications.
One application is in filtering data, as might be done as part of a
search over a corpus of data. While many data structures might be
used with a data classification system, a typical example is a
corpus containing many, many data items organized as units such as
records or documents. While a document is used as an example of a
data item, it should be understood that statements might be equally
applicable to data items that are not normally referred to as
documents.
[0004] A data classification system might be used to a filter
documents from a large corpus to flag or otherwise identify
relevant documents distinctly from less relevant documents. As an
example, a company or an analyst might want to review news items
from a large corpus of news items, but only those news items that
relate to a particular company or set of companies. They could use
a data classification system to flag news items that relate to the
companies of interest and provide those relevant documents for
further processing, such as manual review.
[0005] In the general case, a data classification system classifies
documents as being "in" or "not in" a particular class, or
classifies documents as being in one or more of two or more
classes. In an extremely simple data classification system, a class
might be "all documents containing phrase P" and the simple data
classification system classifies each document as either being in
the class or not being in the class (binary decision). In other
simple, but slightly more involved data classification systems, the
class might be "all documents mentioning phrase P or its synonyms"
or the class might be "all documents apparently relating to topic
T".
[0006] A conventional data classification system might first
convert documents into enumerated features though a process of
feature generation. One way that this can be done is to tokenize
text into a distinct dictionary of features with associated
enumerated values. Advanced techniques may pre-process text with
grammatical knowledge to enrich tokens in a way to aid in a
classification task (e.g., part-of-speech POS tagging, negation
prefixing, etc.). "Stop" words ("a", "the", "but", "and", etc.) are
often removed to improve efficiency. With each document distilled
to a set of enumerated features, the data classification system can
then perform feature selection, selecting a subset of features that
either enhance, or at least minimize loss of, the information
content of the document. Arguably, feature selection is primarily
performed for efficiency reasons, as many machine learning
algorithms display non-linear efficiency with respect to the number
of distinct features.
[0007] The selected features can be weighted (which can also be
thought of as a "soft" feature selection, where some features are
selected strongly and other features are selected weakly), to
enhance a machine learning algorithm. An example of feature
weighting is the use of Inverse Document Frequency (IDF), wherein
terms get more weight if they occur more frequently than their
general average in a wider corpus and less weight if they appear
less frequently than their general average.
[0008] The above processes can be done on documents in a training
corpus as well as documents in the corpus that are to be
classified. Training might involve providing the data
classification system with a corpus and classifications for each
document in the training corpus. Thus, for a simple binary
classification process, some of the documents in the training
corpus are tagged as being examples of members of the class while
the others are tagged as being counterexamples.
[0009] The data classification system then operates a training
process wherein discriminating patterns are preferably discovered
in the training corpus between the examples and the
counterexamples. Techniques for pattern discrimination have been
studied in considerable detail. Examples of machine learning
classification techniques include, but are not limited to, Naive
Bayes, Support Vector Machines, Maximum Entropy, and k-nearest
neighbor. Others might be found in use or in literature on the
topic.
[0010] More complex data classification systems have been
developed. For example, instead of simply classifying an input
document as being an example of a member of the class or a
counterexample (a binary classification), the input document might
be classified into one of more than just two possibilities (M-ary
classification into M classes). For example, when evaluating news
stories, a simple data classification system might just make a
binary decision as to whether a particular news story refers to
topic T or not, while a more complex data classification system
might define each class as relating to a particular topic and would
classify the input document into one or more of two or more
classes.
[0011] Data classification systems might make hard decisions as to
how to classify a given input document. Some data classification
systems might make soft decisions, wherein an input document is not
necessarily classified into a class with absolute certainty, but it
is tagged with one or more value(s) indicating the degree(s) to
which the document would be associated with each of one or more
classes.
[0012] One problem with existing data classification systems is
that real world examples might be more involved and items would be
classified differently depending on other considerations. Hence,
there is a considerable need in the art for a more sophisticated
classification system capable of classifying items based on
multiple inputs into multidimensional categories.
BRIEF SUMMARY OF THE INVENTION
[0013] One aspect of the present invention provides a method of
data classification, wherein an input item is classified by
associating the input item with one or more classes from a set of
one or more classes in a data classification system, including
identifying relevant features in an input item to form a feature
vector for the input item, receiving at the data classification
system an indication of a point-of-view, adjusting the feature
vector according to the point-of-view indication or modifying a
pattern discriminator (e.g., trainer and classifier) to
inline-process feature vectors depending on the provided
point-of-view (e.g., SVM custom kernels), and classifying the input
item into the set of classes according to the point-of-view. The
point-of-view data can be introduced either as a pre-process step
prior to passing it off to the pattern discriminator, or can be
incorporated directly into the pattern discriminator if it is
applicable (e.g., custom kernels in a support vector machine could
be enhanced with point-of-view data). The pattern discriminator can
detect arbitrary patterns given a similarly prepared dataset during
both training and subsequent classification of unclassified
documents.
[0014] Some advantages of such a system include improved accuracy
within a given classification problem, as it focuses the pattern
discrimination engine on the correct context given a point-of-view
to operate from. Another advantage is improvements over
applications of a given trained model to new points-of-view not
incorporated in the original training. This can be the result of
methodologies focusing on the features related to the point-of-view
while having the effect of abstracting the point-of-view
itself.
[0015] Another aspect of the present invention provides a data
classification system, wherein the system includes at least one
input item, at least one feature vector, and at least one data
classifier defined by point-of-view dependency, wherein the system
uses feature weighting in order to rate and classify input items.
The data classifier classifies one or more data sets based upon
patterns observed during a training process with one or more
training data sets. In addition, the data classification system may
rely on a mathematical engine, such as a support vector machine, to
engage in feature weighting.
[0016] As used herein, each item to be classified is described as a
document, but it should be understood that items that are
classified are not limited to items that are considered documents
in all contexts. For example, an input item may include, but is not
limited to, a word processing document, a file of a particular
format (e.g., ASCII file, XML file, UTF-8 file, etc.), a collection
of documents with some structural organization, an image, text, a
combination of images and text, media, spreadsheet data, a
collection of bytes, or other organizations of data or data
streams. A data classification system is provided with access to
one or more of the items of the corpus and based upon the analysis
of the one or more items, the data classification system arrives at
a determination about each of the one or more items, where an
example determination is whether or not a given item belongs into a
particular class. In some cases, the data classification system is
"trained" using examples, wherein the data classification system is
provided with several example items and an indication, for each of
the example items, of the classification for those example
items.
[0017] The invention further encompasses data classifiers that
classify received data sets based upon specific patterns. These
patterns are observed during a training process with training data
sets.
[0018] A further understanding of the nature and advantages of the
inventions herein may be realized by reference to the remaining
portions of the specification and the attached drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The present invention is best understood when read in
conjunction with the accompanying figures which serve to illustrate
the preferred embodiments. It is understood, however, that the
invention is not limited to the specific embodiments disclosed in
the figures.
[0020] FIG. 1 illustrates a classifier with point-of-view
dependencies according to aspects of the present invention.
[0021] FIG. 2 illustrates a data classification system according to
aspects of the present invention.
[0022] FIG. 3 illustrates a trainer in a data classification system
according to aspects of the present invention.
[0023] FIG. 4 illustrates a classifier in a data classification
system according to aspects of the present invention.
[0024] FIG. 5 illustrates a system wizard usable for accepting user
input of a point of view for a training session.
[0025] FIG. 6 illustrates a system wizard usable for accepting user
input on variances of the predicted sentiment for an automatically
selected set of articles.
DETAILED DESCRIPTION OF THE INVENTION
(a) Definitions and General Parameters
[0026] The following definitions are set forth to illustrate and
define the meaning and scope of the various terms used herein.
[0027] The terms "input item" and "document" are interchangeably
used herein and refer to any item that can be used in conjunction
with the present classification method. For example, an input item
may include, but is not limited to, a word processing document, a
file of a particular format (e.g., ASCII file, XML file, UTF-8
file, etc.), a collection of documents with some structural
organization, an image, text, a combination of images and text,
media, spreadsheet data, a collection of bytes, or other
organizations of data or data streams.
[0028] The term "relevant feature" refers to a uniquely
identifiable attribute that could affect the detection of patterns
within a corpus. Relevant features might be domain specific, for
example, in the case of English text classification, a relevant
feature might be the presence of a unique word within a document,
regardless of position.
[0029] A "feature vector", as used herein, refers to a list of
features describing an instance, wherein a feature is the
specification of an attribute and its value. For example, in the
case of English text classification, the attribute of a feature
might be a unique word within a document and the value of the
feature might be the number of occurrences of the unique word
within the document.
[0030] The term "classifier" refers to a system, apparatus or code
for mapping from unlabeled instances to discrete classes.
Classifiers may use a mapping form (e.g., decision tree) and an
interpretation procedure, including rules for how to handle
unknowns. Some classifiers might also provide probability estimates
or scores. These scores can be evaluated to yield a discrete
decision.
[0031] The term "trainer" refers to a system, apparatus or code for
examining a set of known labeled instances to detect implicit
patterns and create models. Classifiers can then apply these models
to future unlabelled instances to generate discrete classes (see
"classifier").
[0032] The terms "point-of-view" and "POV" are interchangeably used
herein and refer to a variable frame of reference when examining or
processing a current document. The same instance may be placed in
different classes given different "point-of-views".
(b) Data Classifiers with Point-of-View Dependencies
[0033] FIG. 1 is a block diagram of a classifier with point-of-view
dependencies according to embodiments of the present invention.
Using the novel data classification systems and methods described
herein, input documents can be classified into classes for a given
point of view (point-of-view dependency). In many cases, a corpus
might be divided into classes one way for one point-of-view and
would be divided into those classes differently for a different
point-of-view. As shown in FIG. 1, classifier 40 might classify an
input document 15 into class 301 or 302 depending on a given point
of view 201 or 202. This allows for improved data assessment over a
conventional data classification system that might always classify
a document into an example or a counterexample (or one or more of a
plurality of classes in M-ary classification).
[0034] An example illustrating the instant system might be the
collection of documents regarding a lawsuit. The documents will
likely contain references to a defendant (e.g., company A) and
several side references to other companies (e.g., companies B-D).
From the point-of-view of company A, the documents should be
classified as belonging to a lawsuit class that company A may track
and analyze daily. From the point of view of companies B-D, the
very same documents would not be considered lawsuit documents
(i.e., classified under a lawsuit class) because, from their
perspective, the documents are not about a lawsuit concerning
companies B-D.
[0035] In another example, an analyst might be searching a news
report corpus for articles about layoffs and the data
classification system might classify incoming articles as being
about layoffs, or not about layoffs. Given a document with two
threads of discussion, one regarding layoffs at Company A and one
regarding a merger at Company B, a traditional data classification
system would only recognize that the article is about layoffs
regardless of whether it concerns Company A or Company B. However,
in embodiments of a novel data classification system as described
herein, this distinction is easily made. As such, the system is
trained against the difference and would be able to, when given the
Company A point-of-view, correctly classify as Layoffs and when
given the Company B point-of-view, correctly classify as
NOT-Layoffs within the same document. Hence, the classification of
"sentiment" or "favorability" applies perfectly herein, i.e., given
a certain document, it may easily be defined as "favorable" for one
company and defined as "unfavorable" for another company.
[0036] FIG. 2 is a high-level block diagram illustrating a data
classification system 10 according to the present invention. System
10 accepts a training corpus, such as training corpus 12, for
training the data classification system. In one embodiment, the
training corpus 12 contains labeled documents. For a simple binary
data classification system in this embodiment, some of the
documents in the training corpus are tagged as being examples of
members of the class while the others are tagged as being
counterexamples. The data classification system 10 then operates a
trainer 20 wherein discriminating patterns are discovered in the
training corpus 12 between the examples and counterexamples to
generate a model 30. Examples of pattern discrimination techniques
include, but are not limited to, Naive Bayes, Support Vector
Machines, Maximum Entropy, and k-nearest neighbor.
[0037] When a new input document 15 is presented, system 10
operates a classifier 40, which classifies document 15 using model
30 and generates a predicted class 50. For example, in a simple
binary classification system, document 15 is classified as either
being in the class or not being in the class.
[0038] Point of view (POV) 60 includes context sensitive
information to enable trainer 20 to discriminate a single
classification between multiple points-of-view. Similarly, POV 70
is an input to classifier 40 to enable it to generate a single
classification between multiple points-of-view.
FIG. 3 shows trainer 20 in greater detail. Trainer 20 accepts a
training corpus 12 as its input and outputs a model 30 with
discerned patterns in training corpus 12. In the figure, trainer 20
is shown with a feature generator 1, feature selector 2, feature
weighter 3, and a pattern discriminator 4. Feature generator 1
generates, from input corpus 12, features to be considered for
discrimination. With each document in training corpus 12 distilled
to a set of enumerated features, trainer 20 can then operate
feature selector 2 to select a subset of features that either
enhance, or at least minimize loss of, the information content of
the document. Feature selection is primarily performed for
efficiency reasons, as many pattern discrimination techniques
display non-linear efficiency with respect to the number of
distinct features. Feature weighter 3 then weights the selected
features to enhance a pattern discrimination algorithm. Finally, a
pattern discriminator 4 is run to discriminate patterns within the
training corpus, where the pattern discriminator is optionally
provided with a custom kernel when the pattern discriminator
supports it. Point-of-view information 60 can be introduced in
various components of trainer 20 to discriminate a single
classification between multiple points-of-view. FIG. 4 shows
classifier 40 in greater detail. Classifier 40 accepts an input
document 15 as its input and outputs a predicted class 50 for input
document 15. In the figure, classifier 40 is shown with a feature
generator 5, a feature selector 6, a feature weighter 7, and a
model applier 8. Feature generator 5 generates features from input
document 15 to be considered for discrimination. After input
document 15 is distilled into a set of enumerated features,
classifier 40 can then operate a feature selector 6 to select a
subset of features that either enhance, or at least minimize loss
of, the information content of the document. Feature weighter 7
then weights the selected features. Finally, a model applier 8
applies model 30 to input document 15 to predict the class of input
document 15, where a custom kernel is optionally provided to model
30. Predicted class 50 is produced optionally with confidence
values for input document 15. Point-of-view information 70 can be
introduced in various components of classifier 40 to enable it to
generate a single classification between multiple
points-of-view.
[0039] POV information uses include, but are not limited to, custom
feature generation, feature selection, feature weighting and custom
kernel generation. Custom feature generation based on POV could
generate additional features not normally generated in traditional
classification systems where the additional features may be
indicative of a relationship between a given POV and a conventional
feature. Feature selection might be based on POV, wherein features
that appear unrelated are stripped out from the vector. Feature
weighting might also be based on POV, wherein features are weighted
based on relationship (i.e., the value associated with the
attribute is modified in cases where the pattern discrimination
engine supports it). Similarly, a custom kernel might be created
when a pattern discriminator supports it (e.g., Support Vector
Machine (SVM)). The custom kernel can apply POV weighting of
features dynamically during training and classification.
(c) Examples
[0040] The following specific examples are intended to illustrate
embodiments of data classification systems according to aspects of
the invention and should not be construed as limiting the scope of
the claims.
[0041] (i) Point-of-View (POV) Sentiment Ratings
[0042] A novel data classification system might employ POV
sentiment ratings. These ratings capture a person's or
organization's point-of-view on any sentiment (e.g., article
sentiment) using a positive, neutral, and negative (3-point) scale.
In this system, documents can be classified as positive, neutral or
negative documents, with respect to a particular POV. This can be
used to provide an automated point-of-view sentiment classification
service. Where a document must be classified as positive, neutral
or negative, the data classification system performs ternary
classification. In other variations, gradations of positive and
negative are possible, yielding the more than three classes to
choose from, e.g., "strongly positive", "positive", "slightly
positive", neutral, etc. In order to achieve the ternary
classification, the data classification system is trained with a
training corpus wherein each document in the corpus is labeled with
its class. The data classification system can then create three
models: a positive model, a neutral model, and a negative model.
This process can be expanded to classification into more than three
classes.
[0043] The ratings extend a particular point-of-view to all
articles for a specific subject such as a company, competitor, or
the like. The user can gain business insights from enhanced
sentiment reports as well as sentiment report filters in all other
reports. There are many examples of how the user may gain important
business insights by using the systems described herein. For
example, a user employed by company X may need to know who the top
authors are that are currently writing negative articles (i.e.,
negative from company X's perspective) about a competitive lawsuit,
wherein the user is particularly interested in all articles written
during the last week (i.e., top authors/negative articles/lawsuit
company topic/seven days rating). Alternatively, the user may need
to find out how editorial coverage opinion is changing with respect
to company X's handling of a specific crisis (i.e., sentiment over
time/crisis company topic rating). Yet in another scenario, the
user may need to investigate what types of publications contain
positive articles about company X's recent product launch (top
publications/positive articles/product launch company topic
rating).
[0044] Sentiment ratings can be automatically applied as articles
enter the system, i.e., without human intervention. More
specifically, the sentiment ratings might work through a system
wizard that captures the point-of-view for a subject while a person
reviews and validates ratings such as ratings on articles. FIG. 5
illustrates a step of a system wizard, which asks an user to choose
a point of view for a training session. For example, the user is
asked to choose among Microsoft, Sun Microsystems, Hewlett-Packard,
Dell, Gateway, Apple, and Sony Electronics as the point of view for
the training session. FIG. 6 shows another step of the system
wizard, which asks an user to confirm, correct or ignore the
predicted sentiment for an automatically selected set of articles.
For example, the user is asked to review positive predictions for a
set of articles for Microsoft. In a practical setting, new or
revised ratings can be applied overnight to all articles for a
subject within the system. New articles receive ratings as they
enter the system.
[0045] In one embodiment, the data classification systems and
methods of the instant invention employ event-based machine
learning, including an advanced patterns recognition engine,
point-of-view capture algorithms, pre-population with a large
corpus of rated events, and closed-loop learning for continued
point-of-view learning. Generally, the more the system is taught
the more the system knows. Hence, the wizard can be run multiple
times (i.e., trained) which improves rating consistency.
[0046] The wizard can be trained any time the user desires to tune
sentiment ratings and a new point-of-view may be applied to an
entire user account history in batch mode. Moreover, manual ratings
and individual article manual overrides can be incorporated into
new ratings going forward. Under specific circumstances, it may be
necessary to rerun the wizard, particularly when there is a
dramatic change in the article profiles. For example, if company X
is confronted with a new crisis or company X changes from being a
private company to being a public company, the wizard may have to
be rerun and thus retrained.
[0047] After training, the data classification system can be used
to predict classification of an unlabeled instance. For example, in
a ternary classification system, each of the three models is
applied in order to predict classification, and a confidence number
is returned from each model's classification. Since the three
models may disagree (two or more models could claim that the
instance is in that model's class), a weighting scheme is applied
amongst the three models to break disagreement and produce the
single predicted class.
[0048] In tests, one implementation was 77%-97% accurate depending
on the scenario. This was generated by training on 2/3 of a labeled
corpus and testing classification against the remaining 1/3 of a
labeled corpus. When a conventional process stack was applied
against the same problem, in-corpus accuracy dropped significantly
(10-30%), and cross-corpus accuracy (application of trained model
to a new corpus in different domain) fell to statistically
insignificant levels (i.e., the results were no more accurate than
random guesses).
[0049] (ii) SVM Pattern Discrimination
[0050] In one example, a Support Vector Machine (SVM) pattern
discrimination algorithm was chosen for classification. SVMs are
capable of operating efficiently on large feature spaces, which
reduces the need to modify feature vectors for efficiency reasons.
In addition, SVMs support the concept of "weighting" feature
vectors which was initially used.
[0051] In the feature weighting scheme that was examined, one can
apply a relatively unsophisticated algorithm of weighting. Given a
bag of aliases representing a simplistic Point-of-View (e.g.,
"IBM", "International Business Machines", "Big-Blue"), one can
weight all features against sentence-level proximity to an alias
within the bag. In the test-case, a feature was weighted according
to the number of sentences away from the nearest alias, using the
formula shown in Equation 1, where FSP is feature-to-sentence
proximity going forward from the alias and BSP is
feature-to-sentence proximity going backward from the alias.
FeatureWeight=Max(0.95.sup.FSP,0.80.sup.BSP) (Equ. 1)
[0052] Using the formula of Equation 1 had the effect of giving
more weight to features closer to the point-of-view, with more
weight given for a proximity forward of the POV and less weight
given for a proximity prior to the POV. Using that equation,
documents were distinguishable on the basis of POV.
[0053] Thus, adding context sensitive information to the feature
vector enables the mathematical engine to discriminate a single
classification between different POVs. This information can be
added in any of the components in FIG. 3 and FIG. 4. In this
example, it is done within "feature weighter" however it is equally
applicable to all components.
[0054] Various modifications and variations of the present
invention will be apparent to those skilled in the art without
departing from the scope and spirit of the invention. Although the
invention has been described in connection with specific preferred
embodiments, it should be understood that the invention as claimed
should not be unduly limited to to such specific embodiments.
Indeed, various modifications of the described modes for carrying
out the invention which are obvious to those skilled in the art are
intended to be within the scope of the claims.
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