关键词: 加权超图, 加权策略, 关键词推荐, 随机游走, 自然语言处理, 数据挖掘

Abstract: It is difficult for the existing natural language processing algorithms to model the time and authority of short texts such as paper titles of scientific literature.Besides,the short texts always tend to have fewer words and thus suffer from high dimension and sparsity.A keyword extraction method involving both real-time and authoritativeness is presented.A weighted hyper-graph is constructed where vertexes represent weighted terms and weighted hyper-edges measure the semantic relatedness of both binary relations and nary relations among terms.On one hand,the source of the documents,the year of publication and number of citations are considered for weighting hyper-edges,on the other hand,the degree of association between the nodes and co-occurrence distance for each pair of nodes in particular title are calculated for weighting hyper-vertexes.The random walk approach is performed on the weighted hyper-graph to obtain the recommended keywords.Experimental results demonstrated that compared with three baseline algorithms,the proposed approach is able to extract keywords with higher precision and recall.

Key words: weighted hypergraph, weighting strategy, keywords extraction, random walk, natural language processing, data mining