By Kaixu Zhang, Jinsong Su, Changle Zhou (auth.), Maosong Sun, Min Zhang, Dekang Lin, Haifeng Wang (eds.)
This e-book constitutes the refereed complaints of the twelfth China nationwide convention on Computational Linguistics, CCL 2013, and of the 1st overseas Symposium on typical Language Processing in response to clearly Annotated large information, NLP-NABD 2013, held in Suzhou, China, in October 2013. The 32 papers offered have been rigorously 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 computing device studying equipment in NLP; semantics; textual content mining, open-domain info extraction and computing device examining of the net; sentiment research, opinion mining and textual content class; lexical semantics and ontologies; language assets and annotation; computing device translation; speech reputation and synthesis; tagging and chunking; and large-scale wisdom acquisition and reasoning.
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Additional 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
Formula (3), (4) and (5). A denotes word boundaries by manual annotated, while NA is set A’s count. B denotes word boundaries tagged by our algorithm, and NB is the count of set B. Why not F1? For the natural chunk recognition’s result – natural chunks might be somehow rough than the words. As a string “AB” is stably and frequently appear with BICs in a massive scale corpora, according to definition of natural chunk, we might take “AB” as a natural chunk, however in segmentation, it might be take in the form of “A” and “B”.
Generally, supervised statistical learning methods are more robust in processing unrestricted texts than the traditional dictionary-based methods. However, in some domain-speciﬁc applications, for example ancient Chinese text processing, there is neither enough annotated homogeneous corpora for training a reliable statistical model, nor a suﬃcient lexicon. Under these circumstances, unsupervised methods are preferred to utilize the linguistic knowledge derived from the raw corpus itself. Many researches also explores human-computer interactive segmentation process, enabling users to add expert knowledge to the system To whom all correspondence should be addressed.
At the end of this procedure a segmentation result that fully matches the demand of the user is returned. This paper devises an improved model for ancient Chinese word segmentation, and uses the Kalman ﬁlter model by Zhu et al. to implement a practical system for human-computer ancient Chinese text processing. The rest of this paper is organized as follows. The next section reviews related work. Our statistical model is introduced in Section 3. Section 4 brieﬂy reviews the Kalman ﬁlter based approach.