基于一般文本的缩写定义自动提取

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2013-07-23 DOI:10.1109/FSKD.2013.6816313

Zhihua Zhou, Guang Chen

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引用次数: 1

摘要

对缩略语识别的研究大多局限于生物医学文献。缩略语在一般文本中广泛使用，包括网络数据和新闻专线数据，这就要求我们对缩略语的定义进行处理和提取。本文提出了一种缩写定义识别算法，该算法采用多种规则并结合文本的浅层解析，从一般文本中识别出最可能的缩写定义。采用2012 NIST1 TAC-KBP2提供的数据集对系统进行了性能测试，获得了94.2%的召回率和95.5%的准确率。

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Automatic extraction of abbreviation definitions based on general texts

The study of abbreviation identifications mostly is limited to the biomedical literature. The wide use of abbreviations in general texts, including web data and newswire data, requires us to process and extract the abbreviation definition. In this paper, we propose an abbreviation definition identification algorithm, which employs a variety of rules and incorporates shallow parsing of the text to identify the most probable abbreviation definition from general texts. The performance of our system was tested with data set provided by 2012 NIST1 TAC-KBP2, obtaining a performance of 94.2% recall and 95.5% precision.

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2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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