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in automatic summarization. This book provides a systematic introduction to the field, explaining basic definitions, the strategies used by human;
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perfunctory task, mostly due to a lack of standards. It has thus become extremely difficult to implement automatic text analysis tasks. Automatic Text;
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Text summarization has been studied for over a half century, but traditional methods process texts empirically and neglect the;
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of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text;
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summarization. Daniel Marcu develops a semantics-free theoretical framework that is both general enough to be applicable to naturally occurring texts;
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retrieval; - text classification and clustering; - opinion mining; - comprehension aids (automatic summarization, machine translation;
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Summarization. Just when we thought we knew everything about it, the doors to divergent thinking open and summarization-no longer something;
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Multimodal Video Characterization and Summarization is a valuable research tool for both professionals and academicians working in the;
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This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation;
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summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the;
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understanding, text summarization and many other research domains. It turns out that a number of significant parallels do exist. Structuring the methods;
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, text summarization, sentiment analysis, information retrieval, and many more applications of NLP. The book begins with text data collection;
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dictionaries, lexicons, mood scoring, and summarization of text. This is followed by the analytics of text classification in Section 4. The performance;
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selected from 70 submissions. The papers are organized in topical sections on text summarization; relation and word embedding; machine translation;
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modeling, text summarization, text generation, entity extraction, and sentiment analysis. Natural Language Processing Recipes starts by offering;
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machines. Automatic text simplification is the process of transforming a text into another text which, ideally conveying the same message, will;
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models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have;
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models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have;
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analysis and emotion detection; opinion mining and social network analysis; natural language generation and text summarization; information;
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analysis and emotion detection; opinion mining and social network analysis; natural language generation and text summarization; information;
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: sentiment analysis; opinion mining; author profiling and authorship attribution; social network analysis; machine translation; text summarization;
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optimization, we propose SECO-SEVA as an automatic evaluation metric available for research purposes. Finally, the text finishes with a consideration;
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The second edition of Flight Stability and Automatic Control presents an organized introduction to the useful and relevant topics necessary;
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; information extraction and text mining; information retrieval and question answering; document summarization; and applications.;
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In-depth coverage of popular Ford transmissions for the seroius do-it-yourselfer. Each manual offers easy-to-follow text plus hundreds;
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retrieval; text categorization and classification; summarization and recognizing textual entailment; authoring aid, error correction, and style;
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