Abstract: The quality of project topic model has a direct impact on recommended effect of the follow-up evaluation experts.In order to effectively exploit the association relationships among project document fragments to analyze project topics,we proposed a project topic model construction method based on semi-supervised graph clustering.We first analyzed structural characteristics of project documents to extract project name,project keywords and other structural information that responds project topics.Combined with expert evidence documents,expert topic relationship networks and other external resources which can indicate expert topics,we defined and extracted the association relationship features among project document fragments.Then,we used different association relationships to calculate correlation among project document fragments and built undirected graph model for project document fragments.Finally,using the marked association relationship features as supervised information for clustering,we applied semi-supervised graph clustering algorithm to cluster for project document fragments to realize the construction of the project topic model.The comparative experimental results of project topic extraction verify the effectiveness of the proposed method.Structural features of the project documents,expert evidence documents and expert topic relationship networks have certain guidance function for the construction of the project topic model.

Key words: Topic model,Semi-supervised graph clustering,Association relationship features,Evaluation experts recommendation