Download Computational Linguistics and Intelligent Text Processing: by Srinivas Bangalore (auth.), Alexander Gelbukh (eds.) PDF

By Srinivas Bangalore (auth.), Alexander Gelbukh (eds.)

This two-volume set, along with LNCS 7181 and LNCS 7182, constitutes the completely refereed lawsuits of the thirteenth foreign convention on laptop Linguistics and clever Processing, held in New Delhi, India, in March 2012. the entire of ninety two complete papers have been rigorously reviewed and chosen for inclusion within the lawsuits. The contents were ordered based on the subsequent topical sections: NLP procedure structure; lexical assets; morphology and syntax; note experience disambiguation and named entity popularity; semantics and discourse; sentiment research, opinion mining, and feelings; usual language iteration; computer translation and multilingualism; textual content categorization and clustering; info extraction and textual content mining; info retrieval and query answering; record summarization; and applications.

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Additional info for Computational Linguistics and Intelligent Text Processing: 13th International Conference, CICLing 2012, New Delhi, India, March 11-17, 2012, Proceedings, Part I

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Bangalore Acknowledgments. The work presented in this paper is a result of several collaborations with colleagues at AT&T Labs-Research, especially, Giuseppe diFabbrizio, Amanda Stent, and Taniya Mishra. I wish to thank each of my collaborators for our joint work. References 1. : Live search for mobile: Web services by voice on the cellphone. In: Proceedings of ICASSP 2008, Las Vegas (2008) 2. : Learning dialogue structures from a corpus. In: Proceedings of Eurospeech (1997) 3. : A generalized construction of speech recognition transducers.

Then, the input utterance is assigned a subtask label (either a current open subtask, or a new one), and incorporated into the subtask tree. We will use GDAC to refer to grounding-level dialog act assignment/classification, TDAC to refer to task-level dialog act classification and STC to refer to subtask label classification. – Maintenance of the dialog state – in this case, maintenance of the dialog state amounts to maintenance of the subtask tree. In a dialog system, it can be helpful to have an idea of what the user is likely to say next (for example, to set the language model in the speech recognizer [20]).

761–764 (2004) 4. : Generalized algorithms for constructing statistical language models. In: Proceedings of ACL (2003) 5. : Deploying GOOG-411: Early lesstons in data, measurement and testing. In: Proceedings of ICASSP 2008, Las Vegas (2008) 6. : Learning the structure of task-driven human-human dialog. IEEE Transactions on Audio, Speech, and Language Processing Special Issue on New Approaches to Statistical Speech and Text Processing 16(7), 1249–1259 (2008) 7. : Learning the structure of task-driven human-human dialogs.

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