Download Computational Linguistics and Intelligent Text Processing: by Alexander Gelbukh PDF

By Alexander Gelbukh

This two-volume set, such as LNCS 8403 and LNCS 8404, constitutes the completely refereed court cases of the 14th foreign convention on clever textual content Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The eighty five revised papers provided including four invited papers have been conscientiously reviewed and chosen from three hundred submissions. The papers are prepared within the following topical sections: lexical assets; rfile illustration; morphology, POS-tagging, and named entity attractiveness; syntax and parsing; anaphora solution; spotting textual entailment; semantics and discourse; usual language new release; sentiment research and emotion reputation; opinion mining and social networks; computer translation and multilingualism; info retrieval; textual content category and clustering; textual content summarization; plagiarism detection; sort and spelling checking; speech processing; and applications.

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Extra resources for Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II

Example text

We build some rules to recognize these categories using both words and their POS tag information. Rules we use are detailed below: (a) could(would, should) + past perfect This rule is used to recognize one type of subjunctive. The presence of the tag sequence MD+VB+VBN is regarded as the mark of this type of subjunctive. A sentence matching this rule will be treated as a subjunctive. For example, the sentence below will be regarded as a subjunctive due to the presence of tag sequence MD+VB+VBN.

Our disfluency model also outperformed the PMI Word-Level Emotion Recognition Using High-Level Features 29 model used by Savran et al. [5]. This suggests that learning other high level classes of lexical features may be useful for this task. In the future, we plan to study whether the disfluency features are still highly predictive of emotion when using other corpora. The utility of disfluencies also depends on how well they can be detected. Further work will investigate performance using disfluencies detected from the output of an automatic speech recognizer, rather than manual transcription.

Enriching speech recognition with automatic detection of sentence boundaries and disfluencies. IEEE Transactions on Audio, Speech, and Language Processing 14, 1526–1540 (2006) 28. : Laugh-aware virtual agent and its impact on user amusement. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, pp. 619–626. International Foundation for Autonomous Agents and Multiagent Systems (2013) 29. : Detecting summarization hot spots in meetings using group level involvement and turn-taking features.

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