兵工自动化
兵工自動化
병공자동화
ORDNANCE INDUSTRY AUTOMATION
2013年
9期
57-62
,共6页
刘方驰%钟志农%雷霖%吴烨
劉方馳%鐘誌農%雷霖%吳燁
류방치%종지농%뢰림%오엽
实体关系抽取%机器学习%有监督%无监督%弱监督
實體關繫抽取%機器學習%有鑑督%無鑑督%弱鑑督
실체관계추취%궤기학습%유감독%무감독%약감독
entity relation extraction%machine learning%supervised%unsupervised%semi-supervised
实体关系抽取是信息抽取的一项重要内容,总结现有的方法对于该领域的发展具有指导和借鉴意义。结合当前的研究进展,分析和比较了有监督、无监督和弱监督3类关系抽取方法的原理和代表性算法,总结了各类方法的特性并对关系抽取的发展趋势进行了展望。
實體關繫抽取是信息抽取的一項重要內容,總結現有的方法對于該領域的髮展具有指導和藉鑒意義。結閤噹前的研究進展,分析和比較瞭有鑑督、無鑑督和弱鑑督3類關繫抽取方法的原理和代錶性算法,總結瞭各類方法的特性併對關繫抽取的髮展趨勢進行瞭展望。
실체관계추취시신식추취적일항중요내용,총결현유적방법대우해영역적발전구유지도화차감의의。결합당전적연구진전,분석화비교료유감독、무감독화약감독3류관계추취방법적원리화대표성산법,총결료각류방법적특성병대관계추취적발전추세진행료전망。
Relation extraction is an important section of information extraction, summarization of the existing methods is instructional for the developing of this field. Combined with the current research status, firstly, analyzed and compared the principle and representative algorithms of three relation extraction methods, including supervised, unsupervised and semi-supervised based on machine leaning, then summarized the characteristic of all the three methods. Finally, put forward outlook for development tendency.