非结构化文本环境下双语本体构建的概念词提取*

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI:10.1109/PRML52754.2021.9520708

Turdi Tohti, Le Chang, A. Hamdulla, Hankiz Yilahun

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引用次数: 1

摘要

针对从非结构化文本中提取概念词用于领域本体构建效率不理想的问题，本工作首先采用组合统计对分词确定的概念词边界的正确性进行判断，并对错误的分词位置进行纠正，从而增强了分词候选概念词的结构完整性。在此基础上，利用改进的方法和各种资源库对概念词的权重进行调整，主要目的是加强概念词的权重与其领域属性之间的相关性。我们在英汉双语语料库上进行了实验和对比，发现本文提出的加强概念词结构完整性的方法和动态调整概念词权重的方法都对概念词提取的效率带来了一定的提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Concept Word Extraction for Bilingual Ontology Construction in Unstructured Text Environment*

Aiming at the unsatisfactory efficiency of concept word extraction from unstructured text for domain ontology construction, this work first uses a combined statistic to judge the correctness of the concept word boundary determined by the word segmentation, and corrects the wrong segmentation position, thereby strengthening the structural integrity of the segmented candidate concept words. On this basis, the improved methods and various resource libraries are used to adjust the weight of concept words, and the main purpose is to strengthen the correlation between the weight and its domain attributes of concept words. We conducted experiments and comparisons on English-Chinese bilingual corpus, and found that the method of strengthening the structural integrity of concept words and the method of dynamically adjusting the weight of concept words proposed in this paper both brought a certain improvement in the efficiency of concept word extraction.

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2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)

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