【作者】 曹晶； 孙铁利； 杨柳；

【机构】 东北师范大学计算机系；

【摘要】 <正>1 引言目前大多数信息检索系统采用的是浅层的词频统计方法例如向量空间模型,这类方法具有易于实现、检索速度快且不依赖于具体领域和语言等优点, 但由于缺乏对文档的语义分析,不能深层次地理解更多还原

【Abstract】 Current approaches to index weighting for information retrieval from texts are based on statistical analysis of the texts’ contents. A key shortcoming of these indexing schemes, which consider only the terms in a document, is that they cannot extract semantically indexes that represent the semantic content of a document To address this issue, we proposed a new indexing formalism that considers not only the terms in a document, but also the concepts. In the proposed method, concepts are extracted by exploiting clusters of terms that are semantically related, referred to as concept clusters. The index term dimension of the proposed method is lower than the TF-based method, which is expected to significantly reduce the document search time in a real environment更多还原