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By Jingbo Zhu, Huizhen Wang, Benjamin K. Tsou (auth.), Wenjie Li, Diego Mollá-Aliod (eds.)

This ebook constitutes the completely refereed complaints of the twenty second foreign convention on laptop Processing of Oriental Languages, ICCPOL 2009, held in Hong Kong, in March 2009.

The 25 revised complete papers and 15 revised poster papers offered have been rigorously reviewed and chosen from sixty three submissions. The papers tackle various issues in common language processing and its functions, together with observe segmentation, word and time period extraction, chunking and parsing, semantic labelling, opinion mining, ontology development, computer translation, info extraction, rfile summarization, and so on.

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Additional resources for Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy: 22nd International Conference, ICCPOL 2009, Hong Kong, March 26-27, 2009. Proceedings

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Synonymous expressions among predicate-noun based phrases and simple-verb based phrases should be collected and grouped together. Policy #5 shows one important aspect to note. In Korean, some classes of nouns can be combined with general do-verb(“하다”), or become-verb(”되다”) to form a new verb. Such nouns are called predicate nouns. Examples of predicate-noun based verbs are, “분리되다 (become-separation)”, “합병되다 (become-union)”, “개발하다 (dodevelopment)”. In a given domain, one can also find simple verbs with similar meaning.

Future works are including: translating query patterns into actual SPARQL queries and evaluate coverages over the ontology, processing sentences as a combination of more than one LGG patterns, and expanding of query patterns to cover more issues of the domain. It is easy to think only keywords (and respectively nouns and nouns phrases) are trackable and can be used as a query while sentences are not. However, local grammarbased applications like our research show that if you can restrict the domain, variations of sentences (including verbs and verb phrases) are also trackable, and can be used to understand user’s natural language message.

The current trend in NER is to use the machine-learning approach, which is more attractive in that it is trainable and adoptable and the maintenance of a machine-learning system is much cheaper than that of a rule-based one. The representative machinelearning approaches used in NER are Hidden Markov Model (HMM) (BBN’s IdentiFinder [1]), Maximum Entropy (ME) (New York University’s MENE [2]) and Conditional Random Fields (CRFs) [3]. Support Vector Machines (SVMs) based NER system was proposed by Yamada et al.

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