CAJ | 학술논문

为了解决向量空间模型（VSM）对短文本内容表示能力不足的问题，提出了一种基于频繁词集的特征扩展方法。定义了单词间的共现关系和类别同向关系，通过计算单词集的支持度和置信度，挖掘出具有相同类别倾向的频繁词集，并将其作为短文本特征扩展的背景知识库。对于短文本中的每个原始单词，从背景知识库中查找包含有该单词的频繁词集，将其作为扩展特征加入原特征向量中。搜狗语料集上的实验结果表明，置信度和支持度对背景知识库的规模有较大的影响，但是扩展过多的特征存在冗余性，对分类效果没有进一步的提升。基于频繁词集构建的短文本背景知识库可以作为有效的扩展特征；当训练文本数较为有限时，特征扩展对支持向量机SVM的分类效果有显著的提升。
위료해결향량공간모형（VSM）대단문본내용표시능력불족적문제，제출료일충기우빈번사집적특정확전방법。정의료단사간적공현관계화유별동향관계，통과계산단사집적지지도화치신도，알굴출구유상동유별경향적빈번사집，병장기작위단문본특정확전적배경지식고。대우단문본중적매개원시단사，종배경지식고중사조포함유해단사적빈번사집，장기작위확전특정가입원특정향량중。수구어료집상적실험결과표명，치신도화지지도대배경지식고적규모유교대적영향，단시확전과다적특정존재용여성，대분류효과몰유진일보적제승。기우빈번사집구건적단문본배경지식고가이작위유효적확전특정；당훈련문본수교위유한시，특정확전대지지향량궤SVM적분류효과유현저적제승。
A short text feature extension method based on frequent term sets is proposed to overcome the drawbacks of the vector space model (VSM)on representing short text content.After defining the co-occurring and class orientation relations between terms,frequent term sets with identical class orientation are generated by calculating the support and confidence of word sets,and then are taken as the background knowledge for short text feature extension.For each single term of the short text, the term sets containing this term are found in the background knowledge and added into the original term vector as the feature extension.The experimental results on Sougou corpus show that the sup-port and confidence have great impact on the scale of the background knowledge,but excessive ex-tension also has redundancy and cannot obtain further improvement.The background knowledge based on frequent term sets is an effective way for feature extension.When the number of the train-ing documents is limited,these extended features can greatly improve the classification results of the support vector mechine(SVM).