首页> 外文OA文献 >Word vs. Class-Based Word Sense Disambiguation
【2h】

Word vs. Class-Based Word Sense Disambiguation

机译:词与基于类的词义消歧

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. Many authors argue that one possible reason could be the use of inappropriate sets of word meanings. In particular, WordNet has been used as a de-facto standard repository of word meanings in most of these tasks. Thus, instead of using the word senses defined in WordNet, some approaches have derived semantic classes representing groups of word senses. However, the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained semantic class level (also called SuperSenses). We suspect that an appropriate level of abstraction could be on between both levels. The contributions of this paper are manifold. First, we propose a simple method to automatically derive semantic classes at intermediate levels of abstraction covering all nominal and verbal WordNet meanings. Second, we empirically demonstrate that our automatically derived semantic classes outperform classical approaches based on word senses and more coarse-grained sense groupings. Third, we also demonstrate that our supervised WSD system benefits from using these new semantic classes as additional semantic features while reducing the amount of training examples. Finally, we also demonstrate the robustness of our supervised semantic class-based WSD system when tested on out of domain corpus.
机译:正如最近的SensEval / SemEval练习中的词义歧义消除(WSD)任务所证明的那样,为上下文中的单词分配适当的含义已阻止了所有成功解决的尝试。许多作者认为,一个可能的原因可能是使用了不恰当的词义集。特别是,WordNet已在大多数这些任务中用作事实上的词义标准存储库。因此,不是使用WordNet中定义的词义,而是一些方法派生了表示词义组的语义类。但是,WordNet表示的含义仅用于WSD的非常精细的意义级别或非常粗糙的语义类级别(也称为SuperSenses)。我们怀疑在这两个级别之间可能存在适当的抽象级别。本文的贡献是多方面的。首先,我们提出了一种简单的方法,可以自动在抽象的中间级别派生语义类,涵盖所有名义和口头的WordNet含义。其次,我们凭经验证明,我们自动派生的语义类优于基于词义和更粗粒度的意义分组的经典方法。第三,我们还证明了受监督的WSD系统受益于将这些新的语义类用作附加的语义特征,同时减少了训练示例的数量。最后,我们还演示了在域外语料库上进行测试时,基于监督的基于语义类的WSD系统的鲁棒性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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