计算机科学 ›› 2020, Vol. 47 ›› Issue (3): 110-115.doi: 10.11896/jsjkx.190700041
高楠,李利娟,李伟,祝建明
GAO Nan,LI Li-juan,Wei-william LEE,ZHU Jian-ming
摘要： 关键词提取被广泛应用于文本挖掘领域,是文本自动摘要、自动分类、自动聚类等研究的基础。因此,提取高质量的关键词具有十分重要的研究意义。已有关键词提取方法研究中大多仅考虑了部分文本的统计特征,没有考虑词语的隐式语义特征,导致提取结果的准确率不高,且关键词缺乏语义信息。针对这一问题,文中设计了一种针对词语与文本主题之间的特征进行量化的算法。该算法首先用词向量的方法挖掘文本中词语的上下文语义关系,然后通过聚类方法抽取文本中主要的语义特征,最后用相似距离的方式计算词语与文本主题之间的距离并将其作为该词语的语义特征。此外,通过将语义特征与多种描述词语的词频、长度、位置和语言等特征结合,文中还提出了一种融合语义特征的短文本关键词提取方法,简称SFKE方法。该方法从统计信息和语义层面分析了词语的重要性,从而可以综合多方面因素提取出最相关的关键词集合。实验结果表明,相比TFIDF,TextRank,Yake,KEA和AE等方法,融合多种特征的关键词提取方法的性能有了明显的提升。该方法与基于有监督的AE方法相比,F-Score提升了9.3%。最后,用信息增益的方法对特征的重要性进行评估,结果表明,添加语义特征后模型的F-Score提升了7.2%。
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| [1]ZHAO J S,ZHU Q M,ZHOU G D,et al.Review of Research in Automatic Keyword Extraction[J].Journal of Software,2017,28(9):2431-2449. [2]BABAR S A,PATIL P D.Improving Performance of Text Summarization[J].Procedia Computer Science,2015,46:354-363. [3]ONAN A,KORUKGLU S,BULUT H.Ensemble of Keyword Extraction Methods and Classifiers in Text Classification[J].Expert Systems with Applications,2016,57(C):232-247. [4]LUHN H P.A Statistical Approach to Mechanized Encoding and Searching of Literary Information [J].IBM Journal of Research and Development 1957,1(4):309-317. [5]MIHALCEA R,TARAU P.TextRank:Bringing Order into Texts[C]∥Proceeding Conference on Empirical Methods in Natural Language Processing.Barcelona,Spain:2004:404-411. [6]CHEN W,WU Y Z,CHEN W L,et al.Automatic keyword extraction Based on BiLSTM-CRF[J].Computer Science,2018,45(S1):104-109. [7]CAMPPOS R,MANGARAVITE V,PASQUALI A,et al.A Text Feature Based Automatic Keyword Extraction Method for Single Documents[C]∥Advances in Information Retrieval (EDS).Cham:Springer,2018:10772. [8]ARDIANSYAH S,MAJID M A,ZAIN J M.Knowledge of extraction from trained neural network by using decision tree[C]∥International Conference on Science in Information Technology.IEEE,2017. [9]FRANK E,PAYNTER G W,et al.Domain-Specic Keyphrase Extraction [C]∥International Joint Conference on Artificial Intelligence.1999:668-673. [10]CHEN Y,YIN J,ZHU W,et al.Novel Word Features for Keyword Extraction [M]∥Web-Age Information Management.Springer International Publishing,2015:148-160. [11]KANIS J.Digging Language Model-Maximum Entropy Phrase Extraction[C]∥International Conference on Text.Speech:Brno,Czech,2016:46-53. [12]ZHOU C,LI S.Research of Information Extraction Algorithm based on Hidden Markov Model[C]∥International Conference on Information Science and Engineering.Springer,2010:1-4. [13]ZHANG C.Automatic Keyword Extraction from Documents Using Conditional Random Fields[J].Journal of Computational Information Systems,2008,4(3):1169-1180. [14]ZHANG Q,WANG Y,GONG Y,et al.Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter[C]∥Empirical Methods in Nnatural Language Processing.2016:836-845. [15]AQUINO,GERMAN O,LANZARINI L C.Keyword Identification in Spanish Documents using Neural Networks[J].Journal of Computer Science & Technology,2015,15(2):55-60. [16]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[C]∥International Conference on Learning Representations(ICLR).2013:1301-3781. [17]LIU Z Y.Research on Keyword Extraction Method Based on Document Topic Structure[D].Beijing:Tsinghua University,2011. [18]GitHub[OL].https://github.com/uk9921/StopWords. [19]CHEN Y C,ZHANG Y X,WANG H,et al.Features Oriented Survey of State-of-the-Art Keyphrase Extraction Algorithms[J].Journal of Software,2018,29(7):2046-2070. [20]LI S,ZHAO Z,HU R,et al.Analogical Reasoning on Chinese Morphological and Semantic Relations[J].Meeting of the Association for Computational Linguistics,2018,2:138-143. |
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