ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Research on an automatic retrieval method for special topic news based on semantic frame

  • 1.

    Department of Automation, University of Science & Technology of China, Hefei 230027, China

  • 2.

    Department of Computer Science and technology, University of Science and Technology of China, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.03.011
  • Received Date: 29 January 2015
  • Accepted Date: 25 February 2016
  • Rev Recd Date: 25 February 2016
  • Publish Date: 30 March 2016
    Abstract

  • Abstract

    A novel negative news retrieving semantic frame(NNFrame) and its identification were presented. Different from traditional semantic FrameNets, which were defined based on word-sense disambiguation, NNFrames were defined on each subcategory of negative news with a single semantic context. By constructing the NNFrame knowledge base, domain ontology repository, and a collection of annotated example sentences for each NNFrame, a method was described for identifying NNFrame by a task-specific extended conditional log-likelihood model, that takes dependency-syntax structure representations, and the part of speech tags as input. This approach is practical, efficient, and can achieve state-of-the-art results on precision/recall metrics for identification and classification of negative news whose subcategories are pre-defined in the NNFrame knowledge base.

    Abstract

    A novel negative news retrieving semantic frame(NNFrame) and its identification were presented. Different from traditional semantic FrameNets, which were defined based on word-sense disambiguation, NNFrames were defined on each subcategory of negative news with a single semantic context. By constructing the NNFrame knowledge base, domain ontology repository, and a collection of annotated example sentences for each NNFrame, a method was described for identifying NNFrame by a task-specific extended conditional log-likelihood model, that takes dependency-syntax structure representations, and the part of speech tags as input. This approach is practical, efficient, and can achieve state-of-the-art results on precision/recall metrics for identification and classification of negative news whose subcategories are pre-defined in the NNFrame knowledge base.
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    • References(16)
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    [1]
    林政, 谭松波, 程学旗. 基于情感关键句抽取的情感分类研究[J].计算机研究与发展, 2012, 49(11): 2376-2382.
    LIN Z, TAN S B, CHENG X Q. Sentiment classification analysis based on extraction of sentiment key sentence[J]. Journal of Computer Research and Development, 2012, 49(11): 2376-2382.
    [2]
    刘永丹, 曾海泉, 李荣陆, 等. 基于语义分析的倾向性文本过滤[J]. 通信学报, 2004, 25(7): 78-85.
    LIU Y D, ZENG H Q, LI R L, et al. Polarity text filtering based on semantic analysis[J]. Journal of China Institute of Communications, 2004, 25(7): 78-85.
    [3]
    苏金树, 张博锋, 徐昕. 基于机器学习的文本分类技术研究进展[J]. 软件学报, 2006, 9(1): 1848-1859.
    SU J S, ZHANG B F, XU X. Advances in machine learning based text categorization[J]. Journal of Software, 2006, 9(1): 1848-1859.
    [4]
    DAS D. Statistical models for frame-semantic parsing[C]// Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore. Baltimore, USA: ACL Press, 2014:26-29.
    [5]
    BAKER C F, FILLMORE C J, LOWE J B. The Berkeley FrameNet project[C]// Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Montréal, Canada: ACM Press, 1998: 86-90.
    [6]
    PALMER M, GILDEA D, KINGSBURY P. The proposition bank: An annotated corpus of semantic roles[J]. Computational Linguistics, 2005, 31(1): 71-105.
    [7]
    BAKER C, ELLSWORTH M, ERK K. SemEval-2007 task 19: Frame semantic structure extraction[C]// Proceedings of the 4th International Workshop on Semantic Evaluations. Prague, Czech Republic: ACM Press, 2007: 99-104.
    [8]
    牛之贤, 白鹏洲, 段富. 基于框架语义标注的自由文本信息抽取研究[J]. 计算机工程与应用, 2008, 44(25): 143-145.
    NIU Z X, BAI P Z, DUAN F. Free text information extraction based on frame semantic tagging[J]. Computer Engineering and Application, 2008, 44(25): 143-145.
    [9]
    HAN L F, WONG D F, CHAO L S, et al. A study of Chinese word segmentation based on the characteristics of Chinese[A]// Lecture Notes in Computer Science, 2013, 8105(1): 111-118.
    [10]
    PUNYAKANOK V, ROTH D, YIH W T. The importance of syntactic parsing and inference in semantic role labeling[J]. Computational Linguistics, 2008, 34(2): 257-287.
    [11]
    QIU X P, ZHANG Q, HUANG X J. FudanNLP: A toolkit for Chinese natural language processing[C]// Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Sofia, Bulgaria: IEEE Press, 2013: 49-54.
    [12]
    NIVRE J. Non-projective dependency parsing in expected linear time[C]// Proceedings of the 4th International Joint Conference Natural Linguage Processing of the AFNLP. Singapore: ACM Press, 2009: 351-359.
    [13]
    COLLINS M. Log-linear models[EB/OL]. http://www.cs.columbia.edu/~mcollins/loglinear.pdf, 2014.
    [14]
    GIMPEL K, SMITH N A. Softmax-margin CRFs: Training log-linear models with cost functions[C]// HLT'10: The 2010 Annual Conference on Human Language Technologies. Los Angeles, USA: ACM Press, 2010: 733-736.
    [15]
    CHANG C C, LIN C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligence Systems and Technology, 2011, 2(3): (No.27)1-27.
    [16]
    FAN R E, CHANG K W, HSIEH C J, et al. LIBLINEAR: A library for large linear classification[J]. Journal of Machine Learning Research, 2010, 9(12): 1871-1874.
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