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| Terms with explicit meanings are used in the academic semantic search system to represent specific research domains.The major works of Automatic Term Recognition (ATR) focus on measuring the relationship between term and paper as the feature of term.The academic semantic search system does not provide full papers, and the short-text-corpus constructed by titles and abstracts of papers reduces the influence of the feature.This paper proposes a novel ATR approach.Firstly, new types of features are provided by measuring the relationships between term and other entities.Secondly, based on the relations between the features of term, the TRBN (term recognition bayesian network) model which is represented by Bayesian Network is proposed to integrate the features.The results of experiments, which are implemented on the corpus containing 7,750,000 titles and 4,500,000 abstracts from the domain of telecommunication and computer science, illustrate the good performance of this new approach that is 10 percent of precision outperforms the baseline method. |
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| Keywords:Natural Language Processing; Automatic Term Recognition; Bayesian Network |
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