关键词: 分类,依存句法分析,查询词权重,查询分析,term重要性,搜索引擎,信息检索

Abstract: In the field of traditional search engines and information retrieval,term weights for the input query are typically derived in a context independent fashion．Most information retrieval techniques employ bag-of-words approaches like Boolean models,vector-space models and other probabilistic ranking approaches to obtain term-weight of a term in a query．However,all these algorithms treat terms independently,and do not take the relationship among the terms．This paper employed supervised machine learning based on classification and syntactic parsing to derive a context-sensitive and query-dependent term weight for each word in a search query．By taking the result of syntactic parsing as a major feature of the classification,it is now able to avoid the information loss and increase the features of the short text．Meanwhile the classifier could achieve soft output,in order to give a more accurate quantized value to term importance.

Key words: Classification,Dependency parsing,Term-weight,Query analysis,Term importance,Search engine,Information retrieval