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Detecting Emotive Sentences with Pattern-based Language Modelling

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Title: Detecting Emotive Sentences with Pattern-based Language Modelling
Authors: Ptaszynski, Michal Browse this author
Masui, Fumito Browse this author →KAKEN DB
Rzepka, Rafal Browse this author →KAKEN DB
Araki, Kenji Browse this author →KAKEN DB
Keywords: Brute Force Search Algorithms
Pattern Extraction
Language Modelling
Emotive expressions
Language Combinatorics
Issue Date: Sep-2014
Publisher: Elsevier
Journal Title: Procedia Computer Science
Volume: 35
Start Page: 484
End Page: 493
Publisher DOI: 10.1016/j.procs.2014.08.129
Abstract: This paper presents our research in detection of emotive (emotionally loaded) sentences. The task is defined as a text classification problem with an assumption that emotive sentences stand out both lexically and grammatically. The assumption is verified exper- imentally. The experiment is based on n-grams as well as more sophisticated patterns with disjointed elements. To deal with the sophisticated patterns a novel language modelling algorithm based on the idea of language combinatorics is applied. The results of experiments are explained with the standard means of Precision, Recall and balanced F-score. The algorithm also provides a refined list of most frequent sophisticated patterns typical for both emotive and non-emotive context.
Rights: © 2014, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 3.0 license
Type: article
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: Rzepka, Rafal

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