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Human Language Technology (HLT) and Natural Language Processing
(NLP) systems have typically focused on the "factual" aspect of
content analysis. Other aspects, including pragmatics, opinion, and
style, have received much less attention. However, to achieve an
adequate understanding of a text, these aspects cannot be ignored.
The chapters in this book address the aspect of subjective opinion,
which includes identifying different points of view, identifying
different emotive dimensions, and classifying text by opinion.
Various conceptual models and computational methods are presented.
The models explored in this book include the following:
distinguishing attitudes from simple factual assertions;
distinguishing between the author's reports from reports of other
people's opinions; and distinguishing between explicitly and
implicitly stated attitudes. In addition, many applications are
described that promise to benefit from the ability to understand
attitudes and affect, including indexing and retrieval of documents
by opinion; automatic question answering about opinions; analysis
of sentiment in the media and in discussion groups about consumer
products, political issues, etc. ; brand and reputation management;
discovering and predicting consumer and voting trends; analyzing
client discourse in therapy and counseling; determining relations
between scientific texts by finding reasons for citations;
generating more appropriate texts and making agents more
believable; and creating writers' aids. The studies reported here
are carried out on different languages such as English, French,
Japanese, and Portuguese. Difficult challenges remain, however. It
can be argued that analyzing attitude and affect in text is an
"NLP"-complete problem.
Knowledge discovery is an area of computer science that attempts to
uncover interesting and useful patterns in data that permit a
computer to perform a task autonomously or assist a human in
performing a task more efficiently. Soft Computing for Knowledge
Discovery provides a self-contained and systematic exposition of
the key theory and algorithms that form the core of knowledge
discovery from a soft computing perspective. It focuses on
knowledge representation, machine learning, and the key
methodologies that make up the fabric of soft computing - fuzzy set
theory, fuzzy logic, evolutionary computing, and various theories
of probability (e.g. naive Bayes and Bayesian networks,
Dempster-Shafer theory, mass assignment theory, and others). In
addition to describing many state-of-the-art soft computing
approaches to knowledge discovery, the author introduces Cartesian
granule features and their corresponding learning algorithms as an
intuitive approach to knowledge discovery. This new approach
embraces the synergistic spirit of soft computing and exploits
uncertainty in order to achieve tractability, transparency and
generalization. Parallels are drawn between this approach and other
well known approaches (such as naive Bayes and decision trees)
leading to equivalences under certain conditions. The approaches
presented are further illustrated in a battery of both artificial
and real-world problems. Knowledge discovery in real-world
problems, such as object recognition in outdoor scenes, medical
diagnosis and control, is described in detail. These case studies
provide further examples of how to apply the presented concepts and
algorithms to practical problems. The author provides web page
access to an online bibliography, datasets, source codes for
several algorithms described in the book, and other information.
Soft Computing for Knowledge Discovery is for advanced
undergraduates, professionals and researchers in computer science,
engineering and business information systems who work or have an
interest in the dynamic fields of knowledge discovery and soft
computing.
Human Language Technology (HLT) and Natural Language Processing
(NLP) systems have typically focused on the "factual" aspect of
content analysis. Other aspects, including pragmatics, opinion, and
style, have received much less attention. However, to achieve an
adequate understanding of a text, these aspects cannot be ignored.
The chapters in this book address the aspect of subjective opinion,
which includes identifying different points of view, identifying
different emotive dimensions, and classifying text by opinion.
Various conceptual models and computational methods are presented.
The models explored in this book include the following:
distinguishing attitudes from simple factual assertions;
distinguishing between the author's reports from reports of other
people's opinions; and distinguishing between explicitly and
implicitly stated attitudes. In addition, many applications are
described that promise to benefit from the ability to understand
attitudes and affect, including indexing and retrieval of documents
by opinion; automatic question answering about opinions; analysis
of sentiment in the media and in discussion groups about consumer
products, political issues, etc. ; brand and reputation management;
discovering and predicting consumer and voting trends; analyzing
client discourse in therapy and counseling; determining relations
between scientific texts by finding reasons for citations;
generating more appropriate texts and making agents more
believable; and creating writers' aids. The studies reported here
are carried out on different languages such as English, French,
Japanese, and Portuguese. Difficult challenges remain, however. It
can be argued that analyzing attitude and affect in text is an
"NLP"-complete problem.
Knowledge discovery is an area of computer science that attempts to
uncover interesting and useful patterns in data that permit a
computer to perform a task autonomously or assist a human in
performing a task more efficiently. Soft Computing for Knowledge
Discovery provides a self-contained and systematic exposition of
the key theory and algorithms that form the core of knowledge
discovery from a soft computing perspective. It focuses on
knowledge representation, machine learning, and the key
methodologies that make up the fabric of soft computing - fuzzy set
theory, fuzzy logic, evolutionary computing, and various theories
of probability (e.g. naive Bayes and Bayesian networks,
Dempster-Shafer theory, mass assignment theory, and others). In
addition to describing many state-of-the-art soft computing
approaches to knowledge discovery, the author introduces Cartesian
granule features and their corresponding learning algorithms as an
intuitive approach to knowledge discovery. This new approach
embraces the synergistic spirit of soft computing and exploits
uncertainty in order to achieve tractability, transparency and
generalization. Parallels are drawn between this approach and other
well known approaches (such as naive Bayes and decision trees)
leading to equivalences under certain conditions. The approaches
presented are further illustrated in a battery of both artificial
and real-world problems. Knowledge discovery in real-world
problems, such as object recognition in outdoor scenes, medical
diagnosis and control, is described in detail. These case studies
provide further examples of how to apply the presented concepts and
algorithms to practical problems. The author provides web page
access to an online bibliography, datasets, source codes for
several algorithms described in the book, and other information.
Soft Computing for Knowledge Discovery is for advanced
undergraduates, professionals and researchers in computer science,
engineering and business information systems who work or have an
interest in the dynamic fields of knowledge discovery and soft
computing.
This volume constitutes the thoroughly refereed post-workshop
proceedings of an international workshop on fuzzy logic in
Artificial Intelligence held in Negoya, Japan during IJCAI
'97.
The 17 revised full papers presented have gone through two rounds
of reviewing and revision. Three papers by leading authorities in
the area are devoted to the general relevance of fuzzy logic and
fuzzy sets to AI. The remaining papers address various relevant
issues ranging from theory to application in areas like knowledge
representation, induction, logic programming, robotics, pattern
recognition, etc.
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