WordNet as an Interlingual Lexical Resource

WordNet 作为语际词汇资源

• 批准号: 9805732
• 负责人: Christiane Fellbaum
• 金额: $ 9.98万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-11-15 至 2000-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9805732&HistoricalAwards=false
• 关键词: WordNet; Interlingual; Lexical; Resource

This work represents a two-pronged extension of the lexical database WordNet to create a resource suitable for automated word sense disambiguation, multilingual adoption, and crosslinguistic applications. Mapping WordNet's words and concepts into those of other languages requires disambiguation, identification, and discrimination of related entities. We first group WordNet's finely distinguished senses of highly polysemous nouns, verbs, and adjectives into underspecified "super"-senses to facilitate discrimination and disambiguation. Second, we add to the verbs in WordNet sentences illustrating their subcategorization patterns and selectional restrictions so as to better distinguish senses and facilitate matching the verb lexicons of different languages. The project is significant because it creates a resource that meets present demands for multilingual Natural Language Processing applications. Moreover, the augmentations carry considerable theoretical interest. Systematic underspecification of lexical entries might lead towards a psychologically realistic lexicon. A principled grouping of closely related senses will shed light on the nature of polysemy and specific ways in which flexible word meanings yield possible sense extensions in all areas of the lexicon. Subcategorization information and selectional restrictions will reveal the extent to which the semantic relatedness of verbs, expressed in WordNet's relational structure, is correlated with syntactic relatedness.

这项工作代表了词汇数据库WordNet的双管齐下的扩展，以创建一个适合于自动词义消歧，多语言采用和跨语言应用程序的资源。 将WordNet的单词和概念映射到其他语言的单词和概念需要消除歧义，识别和区分相关实体。 我们首先组WordNet的高度多义名词，动词和形容词的精细区分的意义到未指定的“超级”的意义，以促进歧视和消歧。 其次，我们添加到WordNet句子中的动词说明其次范畴化模式和选择限制，以便更好地区分意义，并促进匹配不同语言的动词词汇。 该项目意义重大，因为它创建了一个满足当前多语言自然语言处理应用程序需求的资源。 此外，增广进行了相当大的理论兴趣。 系统性的词条不规范可能导致心理上真实的词汇。 一个原则性的密切相关的意义分组将揭示一词多义的性质和具体的方式，灵活的词义产生可能的意义扩展在所有领域的词汇。 次范畴化信息和选择性限制将揭示在WordNet的关系结构中表达的动词的语义相关性与句法相关性的程度。