Word sense disambiguation with graph model based on domain knowledge

Whether domain knowledge is fully utilized would impact the performance of word sense disambiguation (WSD) on a specific domain. A WSD method with graph model based on domain knowledge is proposed in the paper. The method makes full use of domain knowledge: first, the keywords related with target text domain are collected as text domain knowledge, and domain annotations of each sense of target ambiguous word are obtained as sense domain knowledge; second, a disambiguation graph is constructed with text domain knowledge and sentence context words; thirdly, the disambiguation graph is adjusted based on sense domain knowledge; finally, the sense nodes in the graph are scored with an improved evaluation method to judge the right sense. This WSD method effectively integrates domain knowledge with graph model. Evaluation is performed on Koeling dataset. Compared with similar methods, the WSD method yields state-of-the-art performance. Besides, multiple graph evaluation models are improved and compared in detail.