By Srinivas Bangalore (auth.), Alexander Gelbukh (eds.)
This two-volume set, which include 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 full of ninety two complete papers have been conscientiously reviewed and chosen for inclusion within the court cases. The contents were ordered in line with the subsequent topical sections: NLP method structure; lexical assets; morphology and syntax; notice feel disambiguation and named entity acceptance; semantics and discourse; sentiment research, opinion mining, and feelings; ordinary language new release; desktop translation and multilingualism; textual content categorization and clustering; info extraction and textual content mining; info retrieval and query answering; rfile 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
We have also discussed a model for human-machine dialog, that in contrast with traditional multi-component models of dialog, tightly couples speech understanding and language generation tasks of a dialog manager with the aim of better exploiting constraints from each component and minimize the ill-eﬀects of error propagation. 16 S. 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.
We encode the problem of parsing as a weighted ﬁnite-state transducer (FST). This encoding allows us to apply the parser on ASR 1-best as well as ASR WCNs using the composition operation of FSTs. We formulate the parsing problem as associating with each token of the input a label indicating whether that token belongs to one of a search term (st), location term (lt) or neither (null). Thus, given a word sequence (W = w1 , . . , wn ) output from ASR, we search of the most likely label sequence (T = t1 , .
As shown in Table 2, a signiﬁcant increasing in precision values is produced by applying elimination at each step. For instance, the list in Step1 for Fruit hypernym includes some incorrect relevant items such as vitamin/vitamin or porsiyon/portion. In next step, portion remains but vitamin is eliminated. Corpus-Driven Hyponym Acquisition for Turkish Language 37 Finally, portion is eliminated in Step3. The second important result is that the decrease in recall is very small. Such a decrease is inevitable because we apply statistical elimination rather than statistical expansion.