Abstract
One of the most fundamental works for providing better Web services is the discovery of inter-word relations. However, the state of the art is either to acquire specific relations (e.g., causality) by involving much human efforts, or incapable of specifying relations in detail when no human effort is needed. In this paper, we propose a novel mechanism based on linguistics and cognitive psychology to automatically learn and specify association relations between words. The proposed mechanism, termed as ALSAR, includes two major processes: the first is to learn association relations from the perspective of verb valency grammar in linguistics, and the second is to further lable/specify the association relations with the help of related verbs. The resultant mechanism (i.e., ALSAR) is able to provide semantic descriptors which make inter-word relations more explicit without involving any human labeling. Furthermore, ALSAR incurs a very low complexity, and experimental evaluations on Chinese news articles crawled from Baidu News demonstrate good performance of ALSAR.
Research work reported in this paper was partly supported by the key basic research program of Shanghai under grant no. 09JC1406200, the National Science Foundation of China under grant nos. 91024012, 61071110, and 90612010, the National High-tech R&D Program of China under grant no. 2009AA012201, and by the Shanghai Leading Academic Discipline Project under grant no. J50103.
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Zhang, J., Li, Q., Luo, X., Wei, X. (2014). Automatically Learning and Specifying Association Relations between Words. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_63
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DOI: https://doi.org/10.1007/978-3-319-08010-9_63
Publisher Name: Springer, Cham
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