解决命名实体中的规则多义现象
Resolving Regular Polysemy in Named Entities
AI总结：
本文提出组合式 WSD 模型，将专有名称的规则多义形式化为点对象，并结合 CWN 释义与例句，在稀疏数据上同时实现普通名词和专有名词的竞争性消歧效果。
摘要：
词义消歧主要基于预定义的词义清单处理普通词的词汇歧义。相反，专有名称通常被认为表示一个临时的现实世界指称对象。一旦指称确定，歧义据称便得到解决。然而，专有名称也会通过名物化（appellativization）表现出歧义，即它们像普通词一样发挥作用，并可能表示其指称对象的不同方面。我们提议借助规则多义现象来处理专有名称的歧义，并将其形式化为点对象（dot objects）。本文介绍了一种组合式词义消歧（WSD）模型，用于依据中文词网（Chinese Wordnet, CWN）消歧普通词，并将专有名称作为点对象进行消歧。该模型利用了基于释义的模型架构的灵活性，该架构充分利用 CWN 的释义和例句。我们表明，即使在词义数据相对稀疏的数据集上，该模型在普通名词和专有名词上都取得了具有竞争力的结果。除了作为高性能的 WSD 工具之外，该模型还进一步促进了词汇资源的未来发展。
英文摘要：
Word sense disambiguation primarily addresses the lexical ambiguity of common words based on a predefined sense inventory. Conversely, proper names are usually considered to denote an ad-hoc real-world referent. Once the reference is decided, the ambiguity is purportedly resolved. However, proper names also exhibit ambiguities through appellativization, i.e., they act like common words and may denote different aspects of their referents. We proposed to address the ambiguities of proper names through the light of regular polysemy, which we formalized as dot objects. This paper introduces a combined word sense disambiguation (WSD) model for disambiguating common words against Chinese Wordnet (CWN) and proper names as dot objects. The model leverages the flexibility of a gloss-based model architecture, which takes advantage of the glosses and example sentences of CWN. We show that the model achieves competitive results on both common and proper nouns, even on a relatively sparse sense dataset. Aside from being a performant WSD tool, the model further facilitates the future development of the lexical resource.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
