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自然语言处理应用程序至关重要，例如信息提取和机器翻译。最大的词典是WordNet，具有超过150,000或词汇单元（LUS）的语义信息。 Framenet是一个较小，独立开发的资源，该资源提供了有关LUS的句法模式的详细信息。该项目研究了这些互补资源可以使用froframenet（FN）的语义 - 句法信息组合的方式，在其他情况下，可用的是Wordnet（WN）的较少详细信息。 WN和FN具有根本不同的设计原理。 wn组（近）同义词中的“综合”，它们通过概念和词汇关系互连以形成语义网络。 FN小组根据他们唤起的“语义框架”，这是一种事件，与活动的参与者一起。因此，尽管诸如_praise_和_blame_之类的反义词可能位于相同的fn帧中，但它们在不同的fn框架中，尽管相互关联，但wn synsets。此外，FN框架涵盖了与语义相关的名词，动词和形容词； wn synsetses不混合语音的一部分。 对于NLP的应用至关重要，资源在感官区分方面有所不同。将对以下差异进行调查：词汇覆盖范围，感官区别，分类学和其他语义关系以及形容词的标量框架。1,000个单词感觉详细检查，以提供这些现象对整个词汇量的分布的概念。 这项理论工作奠定了为NLP社区构建独特而宝贵的资源的基础。