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New Technology of Library and Information Service  2016, Vol. 32 Issue (2): 9-15    DOI: 10.11925/infotech.1003-3513.2016.02.02
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Automatic Extraction of Domain Terms Using Continuous Bag-of-Words Model
Jiang Lin1,2,Wang Dongbo3()
1 School of Information Management, Nanjing University, Nanjing 210023, China
2Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
3College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
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[Objective] This study tries to extract domain terms more accurately and conveniently. [Methods] First, proposed a method using the CBOW model to build word vectors for each component of the terms. Then, applied the cosine similarity to calculate the internal correlation degree among each term’s individual components. To get more representative terms, we used the PageRank algorithm to rank the candidates. [Results] We obtained high recall and precision rates using the paper abstacts in the field of natural language processing as the training pool. [Limitations] The training pool was relatively small, which might influence the results. [Conclusions] This study shows that CBOW model is a more appropriate method to extract terminologies.

Key wordsTerminology extraction      Neural network      Continuous Bag-of-Words Model     
Received: 06 September 2015      Published: 08 March 2016

Cite this article:

Jiang Lin,Wang Dongbo. Automatic Extraction of Domain Terms Using Continuous Bag-of-Words Model. New Technology of Library and Information Service, 2016, 32(2): 9-15.

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