摘要
术语自动识别目的是获取领域术语表中未登录的规范化词汇,是信息抽取、文本挖掘等领域中的重要任务。近年来,利用统计方法抽取术语取得了一定进展,出现了C-Value、NC-Value、TermExtractor等有效方法。但是,对各种统计指标进行加权投票的方法研究较少。该文首先从大量已知术语中收集术语的词性模板,并借之抽取候选术语,接着利用了统计指标加权投票对这些候选术语进行排序。在IEEE 2006-2007电子工程领域文献上的实验结果表明,加权投票方法比任一单独指标的识别效果更好。
Automatic Term Recognition（ATR）,as an important task in Information Extraction and Text Mining,aims at acquiring formalized words that are not recorded in time in the glossary.In recent years,several statistical methods have made substantial progresses in this field,and emerging methods such as C-Value,NC-Value,Term-Extractor have shown great advantages on this task.However,few work has been done on the Weighted Voting algorithm which could merge those statistical metrics as a whole.In this paper,we first collect part-of-speech rules from already-known terms,then match them with pos-tagged strings to acquire candidate terms,and finally sort those terms by Weighted Voting algorithm.The experiment on literature in Electric Engineering field from IEEE2006-2007 metadata shows that the weighted voting algorithm performs better than any seperate metrics alone.
出处
《中文信息学报》
CSCD
北大核心
2011年第3期9-16,共8页
Journal of Chinese Information Processing
基金
国家自然科学基金资助项目(60673087)
关键词
自动术语识别
投票算法
信息抽取
文本挖掘
automatic term recognition
voting algorithm
information extraction
text mining
