RI: Collaborative Proposal: Complementary Lexical Resources: Towards an Alignment of WordNet and FrameNet

RI：协作提案：补充词汇资源：实现 WordNet 和 FrameNet 的协调

• 批准号: 0705199
• 负责人: Christiane Fellbaum
• 金额: --
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705199&HistoricalAwards=false
• 关键词: RI; Collaborative; Proposal; Complementary; Lexical

Machine-readable lexical resources are essential to Natural LanguageProcessing applications such as information extraction and machine translation. The largest lexicon is WordNet, with semantic information about more than 150,000, or lexical units (LUs). A smaller, independently developed resource is FrameNet, which provides detailed information about the syntactic patterns for LUs. The project investigates the ways in which these complementary resources can be combined using the semantic-syntactic information fromFrameNet (FN) where available and falling back on less detailedentries from WordNet (WN) in other cases. WN and FN exhibit fundamentally different design principles. WN groups (near) synonymous LUs into "synsets," which are interconnected via conceptual and lexical relations to form a semantic network. FN groups LUs according to the"semantic frame" they evoke, which is a type of event, relation orstate along with the participants involved in the event. Thus,while antonyms such as _praise_ and _blame_ may be in the same FN frame they are in different, though interlinked, WN synsets. Moreover, FN frames cover semantically related nouns, verbs and adjectives; WN synsetsdo not mix part of speech. Crucially for NLP applications, the resources differ with respect to sense distinctions.Alignment will be investigated for the following differences: lexical coverage, sense distinctions, taxonomic andother semantic relations, and scalar frames for adjectives.Some 1,000 word senses are examined in detail so as to provide an idea ofthe distribution of each of these phenomena over the entire lexicon. This theoretical work lays the foundation for constructing a unique, invaluable resource for the NLP community.

机器可读的词汇资源对于信息提取和机器翻译等自然语言处理应用是必不可少的。最大的词典是WordNet，拥有超过150，000个或词汇单位（LU）的语义信息。一个较小的、独立开发的资源是FrameNet，它提供了关于逻辑单元语法模式的详细信息。该项目调查的方式，这些互补的资源可以结合使用的语义句法信息从框架网（FN）在可用的情况下，并回落到较不详细的条目从WordNet（WN）在其他情况下。WN和FN表现出根本不同的设计原理。WN将（近）同义逻辑单元分组为“同义集合”，这些同义集合通过概念和词汇关系相互连接，形成语义网络。FN根据逻辑单元所唤起的“语义框架”对逻辑单元进行分组，语义框架是一种事件、关系或状态，沿着事件中涉及的参与者。因此，虽然反义词，如_praise_和_blame_可能在同一个FN框架中，但它们在不同的WN同义词集中，尽管是相互联系的。此外，FN框架覆盖语义相关的名词、动词和形容词; WN句法组合不混合词性。 对于NLP应用来说，至关重要的是，资源在意义区别方面存在差异。对齐将针对以下差异进行研究：词汇覆盖率，意义区别，分类和其他语义关系，以及形容词的标量框架。详细检查了大约1，000个单词的意义，以便提供这些现象在整个词汇中的分布的想法。 这项理论工作为NLP社区构建一个独特的、无价的资源奠定了基础。