| 1 |
RCorp: a resource for chemical disease semantic extraction in Chinese
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| 2 |
PolyUCOMP: Combining Semantic Vectors with Skip-bigrams for Semantic Textual Similarity
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In: http://aclweb.org/anthology//S/S12/S12-1075.pdf (2012)
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Computational Linguistics Proceedings of the 6th Workshop on Ontologies and Lexical Resources
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In: http://aclweb.org/anthology-new/W/W10/W10-33.pdf (2010)
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Exploiting the role of position feature in chinese relation extraction
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In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/540_paper.pdf (2008)
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| 5 |
The design and construction of a Chinese collocation bank
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In: http://www.cs.brandeis.edu/~marc/misc/proceedings/lrec-2006/pdf/799_pdf.pdf (2006)
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| 6 |
Similarity Based Chinese Synonym Collocation Extraction
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In: http://www.aclclp.org.tw/clclp/v10n1/v10n1a6.pdf (2005)
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| 7 |
Text segmentation for Chinese spell checking
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Abstract:
Chinese spell checking is different from its counterparts for Western languages because Chinese words in texts are not separated by spaces. Chinese spell checking in this article refers to how to identify the misuse of char-acters in text composition. In other words, it is error correction at the word level rather than at the character level. Before Chinese sentences are spell checked, the text is segmented into semantic units. Error detection can then be carried out on the segmented text based on thesaurus and grammar rules. Segmentation is not a trivial process due to ambiguities in the Chinese lan-guage and errors in texts. Because it is not practical to define all Chinese words in a dictionary, words not pre-defined must also be dealt with. The number of word combinations increases exponentially with the length of the sentence. In this article, a Block-of-Combinations (BOC) segmentation method based on frequency of word usage is proposed to reduce the word combina-tions from exponential growth to linear growth. From experiments carried out on Hong Kong newspapers, BOC can correctly solve 10 % more ambiguities than the Maximum Match segmentation method. To make the segmentation more suitable for spell checking, user in-teraction is also suggested.
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.543.5004
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| 8 |
Extractive Summarization using Inter- and Intra- Event Relevance
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In: http://www.aclweb.org/anthology-new/P/P06/P06-1047.pdf
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| 9 |
Chinese Term Extraction Based on Delimiters
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In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/72_paper.pdf
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| 10 |
Interaction between Lexical Base and Ontology with Formal Concept Analysis
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In: http://www.cs.brandeis.edu/~marc/misc/proceedings/lrec-2006/pdf/796_pdf.pdf
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| 11 |
© The Association for Computational Linguistics and Chinese Language Processing The Design and Construction of the
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In: http://www.aclclp.org.tw/clclp/v10n3/v10n3a6.pdf
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| 12 |
AN AUTOMATIC CHINESE COLLOCATION EXTRACTION ALGORITHM BASED ON LEXICAL STATISTICS ABSTRACT
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In: http://www.icl.pku.edu.cn/webdata_http-dir-listable/proceedings/nl-pke2003/pdf/a14-02.pdf
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| 13 |
Using Synonym Relations In Chinese Collocation Extraction
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In: http://acl.ldc.upenn.edu/w/w04/w04-1113.pdf
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| 14 |
A Hybrid Extraction Model for Chinese Noun/Verb Synonym bi-gram
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In: http://www.aclweb.org/anthology/Y11-1045/
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| 15 |
!135 Taking Antonymy Mask off in Vector Space
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In: http://www.aclweb.org/anthology/Y/Y14/Y14-1018.pdf
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| 16 |
A Cluster-Sensitive Graph Model for Query-Oriented Multi-document Summarization
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In: http://www.cs.fiu.edu/%7Elli003/Sum/ECIR/2008/1.pdf
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| 17 |
Query-Sensitive Mutual Reinforcement Chain and Its Application in Query-Oriented Multi-Document Summarization
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In: http://www.cs.fiu.edu/%7Elli003/Sum/SIGIR/2008/4.pdf
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| 18 |
du. hk
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In: http://www.aclweb.org/anthology-new/C/C08/C08-1130.pdf
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| 19 |
A Grammar-informed Corpus-based Sentence Database for Linguistic and Computational Studies
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In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/401_Paper.pdf
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| 20 |
The 6th Workshop on Asian Languae Resources, 2008 Preliminary Chinese Term Classification for Ontology Construction
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In: http://aclweb.org/anthology/I/I08/I08-7003.pdf
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