为副词建立系统语义分类，填补WordNet空白
Adverbs Revisited: Enhancing WordNet Coverage of Adverbs with a Supersense Taxonomy
- 基于语言学构建副词语义类型体系，涵盖方式、时间等七类
- 人工标注验证显示分类覆盖自然文本中多数副词，一致性高
- 助力词义消歧与情感分析，适合语义研究者和NLP开发者
WordNet对名词和动词提供了丰富的语义层次，但副词仍缺乏系统性语义分类。本文提出一种基于语言学的副词语义类型体系，通过人工标注实证验证，涵盖方式、时间、频率、程度、领域、说话人导向及主体导向等功能类别。初步标注研究结果显示，该分类体系能广泛覆盖自然文本中的副词，且人类标注者可稳定标注。引入该体系可扩展WordNet的覆盖范围，更贴近语言学理论，并促进词义消歧、事件抽取、情感分析与话语建模等下游NLP应用。文中呈现了所提语义类别、标注结果及未来方向。
原文摘要 · Abstract (English)
WordNet offers rich supersense hierarchies for nouns and verbs, yet adverbs remain underdeveloped, lacking a systematic semantic classification. We introduce a linguistically grounded supersense typology for adverbs, empirically validated through annotation, that captures major semantic domains including manner, temporal, frequency, degree, domain, speaker-oriented, and subject-oriented functions. Results from a pilot annotation study demonstrate that these categories provide broad coverage of adverbs in natural text and can be reliably assigned by human annotators. Incorporating this typology extends WordNet's coverage, aligns it more closely with linguistic theory, and facilitates downstream NLP applications such as word sense disambiguation, event extraction, sentiment analysis, and discourse modeling. We present the proposed supersense categories, annotation outcomes, and directions for future work.
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