Chinese Computational Linguistics and Natural Language by Kaixu Zhang, Jinsong Su, Changle Zhou (auth.), Maosong Sun,

By Kaixu Zhang, Jinsong Su, Changle Zhou (auth.), Maosong Sun, Min Zhang, Dekang Lin, Haifeng Wang (eds.)

This ebook constitutes the refereed complaints of the twelfth China nationwide convention on Computational Linguistics, CCL 2013, and of the 1st overseas Symposium on traditional Language Processing in response to evidently Annotated mammoth info, NLP-NABD 2013, held in Suzhou, China, in October 2013. The 32 papers provided have been conscientiously reviewed and chosen from 252 submissions. The papers are equipped in topical sections on notice segmentation; open-domain query answering; discourse, coreference and pragmatics; statistical and laptop studying equipment in NLP; semantics; textual content mining, open-domain info extraction and computer studying of the internet; sentiment research, opinion mining and textual content class; lexical semantics and ontologies; language assets and annotation; computer translation; speech reputation and synthesis; tagging and chunking; and large-scale wisdom acquisition and reasoning.

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Extra info for Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data: 12th China National Conference, CCL 2013 and First International Symposium, NLP-NABD 2013, Suzhou, China, October 10-12, 2013. Proceedings

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Springer, Heidelberg (2013) 12. : A Statistical Method for Finding Word Boundaries in Chinese Text. In: Computer Processing of Chinese and Oriental Languages, pp. 336–351 (1990) 13. : Pat-Tree-Based Keyword Extraction for Chinese Information Retrieval. ACM SIGIR Forum, 50–58 (1997) 14. : Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus. Computer Linguistics 27(1), 1–30 (2001) 15. : Chinese Word Segmentation without Using Dictionary Based on Unsupervised Learning Strategy.

Hanshi Wang etc, 2011] proposed algorithm of “Evaluation-Segmentation-Adaption” merging boundary entropy, frequency and length, and obtained a segmentation after a convergence iteration process [15]. Natural chunks are flexible in granularity. However within a specific application, a sentence has only one specific and rational natural chunk recognition result, matching the needs of various applications by parameter adaption. Boundary strength is necessary information in natural chunk recognition.

11] to quantify the conformity of the prediction in the model with user intention, BPR[%] = # of correct predictions × 100% . # of all predictions (18) Human-Computer Interactive Word Segmentation for Ancient Chinese Texts 33 Two models were compared in this section. e. our improved statistical model without the human-computer interaction process is abbreviated as Without learning), and the other is the Kalman filter integrated approach discussed in Section 4 (abbreviated as Kalman approach). The result of the experiment is shown in Table 3.

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