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45 results on '"Xun, Endong"'

30. A Sequence to Sequence Learning for Chinese Grammatical Error Correction

Author

Ren, Hongkai, Yang, Liner, Xun, Endong, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Zhang, Min, editor, Ng, Vincent, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2018
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33. Can We Neglect Function Words in Word Embedding?

Author

Tang, Gongbo, Rao, Gaoqi, Yu, Dong, Xun, Endong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lin, Chin-Yew, editor, Xue, Nianwen, editor, Zhao, Dongyan, editor, Huang, Xuanjing, editor, and Feng, Yansong, editor

Published
2016
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34. A Hybrid Re-ranking Method for Entity Recognition and Linking in Search Queries

Author

Tang, Gongbo, Guo, Yuting, Yu, Dong, Xun, Endong, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Li, Juanzi, editor, Ji, Heng, editor, Zhao, Dongyan, editor, and Feng, Yansong, editor

Published
2015
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36. Morpheme-Based Chemical Term Analysis and Recognition

Author

Xia Bo, Xun Endong, Qian Qingqing, Rao Gaoqi, Xiao Ye, and Wang Guirong

Subjects
Computer science, business.industry, Word formation, computer.software_genre, Terminology, Term (time), Domain (software engineering), Morpheme, Feature (machine learning), Forward algorithm, Artificial intelligence, Hidden Markov model, business, computer, Natural language processing
Abstract

Term recognition is the foundation of natural language processing, especially in chemical domain. The most important feature of domain text is terminology. Because of the variety and the complexity of chemical terms, chemical term NER is too difficult to be used in a production environment such as patents search. Based on the theory of morphemes and data of Chinese patents, we construct a chemical morpheme classification according to the composition characteristics and formation rules of chemical terms. A simple and stable HMM (The Hidden Markov Model) model is used to model the word formation relationship among different morphemes. The improved forward algorithm is applied to estimate the word formation probability in terminology recognition. It solves the problem of poor recognition of the long terms in low frequency in traditional statistical methods. It also solves the problem of subjectivity and completeness in establishing rules to some extent. Finally, the F1 reaches 91.58% in chemical patents.

Published
2021

38. Research on Extraction of Simple Modifier-Head Chunks Based on Corpus

Author

Wang Chengwen, Xun Endong, Zhang Zheng, Miao Jingjing, and Rao Gaoqi

Subjects
Set (abstract data type), Knowledge extraction, business.industry, Head (linguistics), Simple (abstract algebra), Computer science, Extraction (chemistry), Extraction algorithm, Artificial intelligence, business, computer.software_genre, computer, Natural language processing
Abstract

The purpose of this study is to automatically extract a set of simple modifier-head chunks from a large-scale corpus. By analyzing the distribution of simple modifier-head chunks in usage, a set of formal rules of chunks extraction are formulated and a rule-based automatic extraction algorithm is designed. In the experiment of random sampling, the precision of extraction result with this method reaches 82.63%, which casts light on knowledge extraction based on large-scale corpus.

Published
2020

42. PENS

Author

Liu, Ting, primary, Zhou, Ming, additional, Gao, Jianfeng, additional, Xun, Endong, additional, and Huang, Changning, additional

Published
2000
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43. Construction and Statistical Analysis of Discourse Cohesive Components in Modern Chinese

Author

Wang, Jingyi and Xun, Endong

Abstract

In discourse semantic analysis, the primary focus lies on cohesive elements that maintain discourse coherence. Additionally, attention is given to referring components and topic description structures that facilitate articulation. Various terms are used to refer to these cohesive elements, such as connecting components, logical connectives, relational words and cohesive components. However, there is a lack of unified terminology. The concept of cohesive components has been proposed before. In prior research, it primarily encompassed connectives, discourse markers and tone words, all of which contribute to discourse coherence. However, these definitions were relatively imprecise, and previous studies did not systematically categorize discourse cohesive components based on specific semantic functions. Consequently, they were inadequate for chapter-level semantic analysis. This paper extends the definition and scope of cohesive components based on existing research. It categorizes cohesive components according to both their formal characteristics and semantic functions. We extract cohesive components from the BLCU-CST tree bank, filter them based on specific scope and definition, and summarize a total of 247 logical connectives, 118 discourse connective markers and six types of numerical sequence connectives. In terms of semantic function, we analyze the distribution of 14 different logical-semantic relations that cohesive components indicate within discourse. This analysis not only enriches the understanding of discourse semantics for natural language comprehension but also offers valuable insights for linguistic ontology research.

Published
2023
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