首页> 外文会议>Conference on object-oriented information systems >Guessing Hierarchies and Symbols for Word Meanings through Hyperonyms and Conceptual Vectors
【24h】

Guessing Hierarchies and Symbols for Word Meanings through Hyperonyms and Conceptual Vectors

机译:通过相位和概念向量猜测单词含义的层次结构和符号

获取原文

摘要

The NLP team of LIRMM currently works on lexical disambiguation and thematic text analysis [Lafourcade, 2001]. We built a system, with automated learning capabilities, based on conceptual vectors for meaning representation. Vectors are supposed to encode ideas associated to words or expressions. In the framework o knowledge and lexical meaning representation, we devise some conceptual vectors based strategies to automatically construct hierarchical taxonomies and validate (or invalidate) hyperonymy (or superordinate) relations among terms. Conceptual vectors are used through the thematic distance for decision making and link quality assessment.
机译:Lirmm的NLP团队目前在词汇消歧和主题文本分析中工作[Lafourcade,2001]。根据概念向量,我们建立了一个系统,自动化学习能力,概念向量。向量应该编码与单词或表达相关的想法。在框架O知识和词汇意义表示中,我们设计了一些基于概念向量的策略,以自动构建分层分类和验证(或无效)的术语(或无效)的过义(或上级)关系。概念向量通过主题距离来决策和链接质量评估。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号