从阿拉伯语-英语词典自动提取词汇知识，提升多语言NLP资源
Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction
- 通过n-gram与关键词上下文分析发现词汇模式
- 同义词抽取召回率高，形态与语义信息提取效果良好
- 适合需要阿拉伯语词汇资源的NLP研究者使用
自然语言处理应用需要大量丰富的语言知识。随着电子语言资源如词典、百科和语料库的普及，自动方法应运而生，以克服知识获取瓶颈。本文提出一种从机器可读的阿拉伯语-英语Al-Mawrid词典中自动提取词汇信息的方法。利用n-gram分析和关键词上下文（KWIC）分析，识别出体现形态、句法或语义信息的词汇模式；再通过手工规则的基于信息抽取技术进行信息提取。此外，借助标点符号和启发式规则，从子条目中提取一组同义词。研究显示，各类信息提取精度均较高，同义词召回率高，其他信息召回率较低。结果表明，Al-Mawrid词典包含大量派生词（形态信息）以及同义词、领域标签、上下位关系（语义信息）。
原文摘要 · Abstract (English)
Natural language processing (NLP) applications need large and rich amount of linguistic knowledge. Furthermore, electronic language sources such as dictionaries, encyclopedia, and corpora became available. So, automatic methods are emerged to extract lexical information from those sources to overcome the knowledge acquisition bottleneck. We presented a method to automatically extract lexical information from a machine-readable version of the Arabic-English Al-Mawrid dictionary. We used n-gram analysis and key-word-in-context (KWIC) analysis to discover lexical patterns that manifest morphologic, syntactic, or semantic information. Then, we used hand-crafted rule-based information extraction to extract that information. Furthermore, we used punctuation marks and some heuristics to extract a set of synonyms in a subentry. This study registered high precision for all types of information, high recall for synonyms, and low recall for the other information. The study also showed that the Al-Mawrid has significant amount of derivations (morphologic information) and synonyms, domain labels, and hyponym/hypernym relations (semantic information).
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