Automatic Term Recognition Using Hybrid Method Based on Rewriting and Statistic

Abstract:

Machine aided human translation (MAHT) for the abstract of patent texts is an important step to the deep processing of the patent data, where the terms have significant application value. This paper investigates the automatic term recognition (ATR), and proposes a new hybrid method based on two-phase analysis and statistic to generate English candidate terms. The segments including stop words were not simply discarded; instead, a rewriting method using beginning patterns, ending patterns, and inner patterns on the phase two was employed for the processing of the segments. In the meantime, generalized statistical measures were used for the evaluation of the candidates such as the generalized mutual information (MI), Log-Likelihood Ratio (LLR), and C-value to filter the low score’s candidate terms and to attain the intersection set of them. The experiments on the patent abstract texts extracted randomly show the availability of the method.

• Periodical: Advanced Materials Research (Volumes 1049-1050)
• Pages: 1544-1549
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1544
• Online since: October 2014
• Authors: Wen Xiong*
• Keywords: Automatic Term Recognition (ATR), Information Retrieve (IR), Machine Aided Human Translation (MAHT), Term Extraction, Text Mining (TM)

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