CAJ | 학술논문

词义消歧要解决如何让计算机理解多义词在上下文中的具体含义，对信息检索、机器翻译、文本分类和自动文摘等自然语言处理问题有着十分重要的作用。通过引入句法信息，提出了一种新的词义消歧方法。构造歧义词汇上下文的句法树，提取句法信息、词性信息和词形信息作为消歧特征。利用贝叶斯模型来建立词义消歧分类器，并将其应用到测试数据集上。实验结果表明：消歧的准确率有所提升，达到了65%。
사의소기요해결여하양계산궤리해다의사재상하문중적구체함의，대신식검색、궤기번역、문본분류화자동문적등자연어언처리문제유착십분중요적작용。통과인입구법신식，제출료일충신적사의소기방법。구조기의사회상하문적구법수，제취구법신식、사성신식화사형신식작위소기특정。이용패협사모형래건립사의소기분류기，병장기응용도측시수거집상。실험결과표명：소기적준학솔유소제승，체도료65%。
The task of word sense disambiguation is to make computers choose the correct sense of an ambiguous word in a given context. It is important for problems in natural language processing, such as information retrieval, machine transla-tion, text classification and automatic summarization. In this paper, a new method of word sense disambiguation is pro-posed, where syntactic information is introduced. The parsing tree of its context including the ambiguous word is built. Disambiguation features are extracted including parsing information, part of speech and word information. The Bayesian model is used to build word sense disambiguation classifier. Experimental results show that accuracy rate of disambigua-tion is improved and arrives at 65%.