从词网中选择适当的词义 [英] Choosing appropriate sense of a word from wordnet

问题描述

我正在使用Wordnet查找本体概念的同义词.我如何才能找到适合我的本体论概念的意义.例如，存在一个本体论概念会议"，它在wordnet中具有以下同义词集 名词会议有3种感觉(前3种来自标签文本)

I am using Wordnet for finding synonyms of ontology concepts. How can i find choose the appropriate sense for my ontology concept. e.g there is an ontlogy concept "conference" it has following synsets in wordnet The noun conference has 3 senses (first 3 from tagged texts)

1. (12)会议-(为协商，信息交换或讨论(特别是具有正式议程的会议)而预先安排的会议)
2. (2)联赛，会议-(运动队协会，为其成员组织比赛)
3. (2)会议，小组讨论-(具有一致(严重)主题的与会人员之间的讨论) 现在，第一和第三同义词集对我的本体论概念具有适当的意义.我怎样才能从Wordnet中仅选择这两个?

1. (12) conference -- (a prearranged meeting for consultation or exchange of information or discussion (especially one with a formal agenda))
2. (2) league, conference -- (an association of sports teams that organizes matches for its members)
3. (2) conference, group discussion -- (a discussion among participants who have an agreed (serious) topic) now 1st and 3rd synsets have apprpriate sense for my ontology concept. How can i choose only these two from wordnet?

推荐答案

您正在寻找的技术是语义消歧/表示的方向.

The technology you're looking for is in the direction of semantic disambiguation / representation.

最传统的方法"是词义歧义消除(WSD)，请看一下

The most "traditional approach" is Word Sense Disambiguation (WSD), take a look at

• https://en.wikipedia.org/wiki/Word-sense_disambiguation
• https://stackoverflow.com/questions/tagged/word-sense-disambiguation
• 有人知道一些好的Word Sense消歧软件吗? /a>

• https://en.wikipedia.org/wiki/Word-sense_disambiguation
• https://stackoverflow.com/questions/tagged/word-sense-disambiguation
• Anyone know of some good Word Sense Disambiguation software?

然后是下一代词义归纳/主题建模/知识表示:

• https://en.wikipedia.org/wiki/Word-sense_induction
• https://en.wikipedia.org/wiki/Topic_model
• https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning

• https://en.wikipedia.org/wiki/Word-sense_induction
• https://en.wikipedia.org/wiki/Topic_model
• https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning

然后是最近的炒作:

• 词嵌入，向量空间模型，神经网络

有时候人们跳过语义表示，直接去做文本相似性，并通过比较成对的句子，区别/相似性，然后再达到文本处理的最终目的.

Sometimes people skip the semantic representation and goes directly to do text similarity and by comparing pairs of sentences, the differences/similarities before getting to the ultimate aim of the text processing.

看看使用权重归类排名得分 STS相关工作清单.

Take a look at Normalize ranking score with weights for a list of STS related work.

反之，则是

• 本体创建(Cyc，Yago，Freebase等)
• 语义网( https://en.wikipedia.org/wiki/Semantic_Web )
• 语义词汇资源(WordNet，开放式多语言WordNet等)
• 知识基础人口( http://www.nist.gov/tac/2014/KBP/)

• ontology creation (Cyc, Yago, Freebase, etc.)
• semantic web (https://en.wikipedia.org/wiki/Semantic_Web)
• semantic lexical resources (WordNet, Open Multilingual WordNet, etc.)
• Knowledge base population (http://www.nist.gov/tac/2014/KBP/)

最近还有一个关于本体归纳/扩展的任务:

There's also a recent task on ontology induction / expansion:

• http://alt.qcri.org/semeval2015/task17/
• http://alt.qcri.org/semeval2016/task13/
• http://alt.qcri.org/semeval2016/task14/

• http://alt.qcri.org/semeval2015/task17/
• http://alt.qcri.org/semeval2016/task13/
• http://alt.qcri.org/semeval2016/task14/

根据最终任务，也许上述两种技术都会有所帮助.

Depending on the ultimate task, maybe either of the above technology would help.

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