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Название: Computing Attitude and Affect in Text: Theory and Applications
Авторы: Shanahan J.G., Qu Y., Wiebe J.
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 attitude, affect, and subjective opinion. Various conceptual models and computational methods are presented, including distinguishing attitudes from simple factual assertions; distinguishing between the authors reports from reports of other peoples 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, such as indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups; analyzing client discourse in therapy and counseling; determining relations between scientific texts; generating more appropriate texts; and creating writers aids. In addition to English texts, the collection includes studies of French, Japanese, and Portuguese texts.
The chapters in this book are extended and revised versions of papers presented at the American Association for Artificial Intelligence (AAAI) Spring Symposium on Exploring Attitude and Affect in Text, which took place in March 2004 at Stanford University. The symposium, and the book which grew out it, represents a first foray into this area and a balance among conceptual models, computational methods, and applications.