Abstract
Comprehensive analysis of multi-domain texts has generated an important effect on text mining. Although the objects described by these multi-domain texts belong to different fields, they sometimes are overlapped partially; and linking these texts fragments which are overlapped or complementary is a necessary step for many tasks, such as entity resolution, information retrieval and text clustering. Previous works for computing text similarity mainly focus on string-based, corpus-based and knowledge-based approaches. However cross-domain texts exhibit very special features compared to texts in the same domain: (1) entity ambiguity, texts from different domains may contain various references to the same entity; (2) content skewness, cross domain texts are overlapped partially. In this paper, we propose a novel fine-grained approach based on text graph for evaluating the semantic similarity of cross-domain texts to link the similar parts. The experiment results show that our approach gives an effective solution to discover the semantic relationship between cross domain text fragments.
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Hu, Y., Nie, T., Shen, D., Kou, Y. (2015). Graph-Based Approach for Cross Domain Text Linking. In: Cai, R., Chen, K., Hong, L., Yang, X., Zhang, R., Zou, L. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9461. Springer, Cham. https://doi.org/10.1007/978-3-319-28121-6_14
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DOI: https://doi.org/10.1007/978-3-319-28121-6_14
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