Kazeminejad Ghazaleh, Palmer Martha, Brown Susan Windisch, Pustejovsky James
Department of Linguistics, University of Colorado Boulder, Boulder, CO, United States.
Department of Computer Science, Brandeis University, Waltham, MA, United States.
Front Artif Intell. 2022 May 30;5:780385. doi: 10.3389/frai.2022.780385. eCollection 2022.
Computational lexical resources such as WordNet, PropBank, VerbNet, and FrameNet are in regular use in various NLP applications, assisting in the never-ending quest for richer, more precise semantic representations. Coherent class-based organization of lexical units in VerbNet and FrameNet can improve the efficiency of processing by clustering similar items together and sharing descriptions. However, class members are sometimes quite different, and the clustering in both can gloss over useful fine-grained semantic distinctions. FrameNet officially eschews syntactic considerations and focuses primarily on semantic coherence, associating nouns, verbs and adjectives with the same semantic frame, while VerbNet considers both syntactic and semantic factors in defining a class of verbs, relying heavily on meaning-preserving diathesis alternations. Many VerbNet classes significantly overlap in membership with similar FrameNet Frames, e.g., VerbNet and FrameNet Apply_heat, but some VerbNet classes are so heterogeneous as to be difficult to characterize semantically, e.g., . We discuss a recent addition to the VerbNet class semantics, verb-specific semantic features, that provides significant enrichment to the information associated with verbs in each VerbNet class. They also implicitly group together verbs sharing semantic features within a class, forming more semantically coherent subclasses. These efforts began with introspection and dictionary lookup, and progressed to automatic techniques, such as using NLTK sentiment analysis on verb members of VerbNet classes with an Experiencer argument role, to assign positive, negative or neutral labels to them. More recently we found the Brandeis Semantic Ontology (BSO) to be an invaluable source of rich semantic information and were able to use a VerbNet-BSO mapping to find fine-grained distinctions in the semantic features of verb members of 25 VerbNet classes. This not only confirmed the assignments previously made to classes such as , but also gave a more fine-grained semantic decomposition for the members. Also, for the class, the new method revealed new, more fine-grained existing semantic features for the verbs. Overall, the BSO mapping produced promising results, and as a manually curated resource, we have confidence the results are reliable and need little (if any) further hand-correction. We discuss our various techniques, illustrating the results with specific classes.
诸如WordNet、PropBank、VerbNet和FrameNet等计算词汇资源在各种自然语言处理应用中经常使用，有助于人们不断追求更丰富、更精确的语义表示。VerbNet和FrameNet中基于类的词汇单元连贯组织可以通过将相似项聚类在一起并共享描述来提高处理效率。然而，类成员有时差异很大，而且两者中的聚类可能会掩盖有用的细粒度语义区别。FrameNet正式回避句法考虑，主要关注语义连贯，将名词、动词和形容词与相同的语义框架相关联，而VerbNet在定义动词类时既考虑句法因素也考虑语义因素，严重依赖保持意义的语态交替。许多VerbNet类在成员上与类似的FrameNet框架有显著重叠，例如VerbNet和FrameNet的Apply_heat，但一些VerbNet类非常异类，以至于难以从语义上进行描述，例如。我们讨论了VerbNet类语义的一个最新补充，即特定于动词的语义特征，它为与每个VerbNet类中的动词相关联的信息提供了显著丰富。它们还在一个类中隐式地将共享语义特征的动词归为一组，形成语义上更连贯的子类。这些工作始于自省和词典查找，然后发展到自动技术，例如对具有体验者论元角色的VerbNet类的动词成员使用NLTK情感分析，为它们分配积极、消极或中性标签。最近，我们发现布兰代斯语义本体（BSO）是丰富语义信息的宝贵来源，并且能够使用VerbNet-BSO映射在25个VerbNet类的动词成员的语义特征中找到细粒度区别。这不仅证实了之前对诸如等类所做的分配，还为成员提供了更细粒度的语义分解。此外，对于类，新方法揭示了动词新的、更细粒度的现有语义特征。总体而言，BSO映射产生了有希望的结果，并且作为一个人工策划的资源，我们相信结果是可靠的，几乎不需要（如果需要的话）进一步人工校正。我们讨论了我们的各种技术，并用特定的类来说明结果。
