Componential Analysis of English Verbs

Abstract

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 Cooking-45.3 and FrameNet Apply_heat, but some VerbNet classes are so heterogeneous as to be difficult to characterize semantically, e.g., Other_cos-45.4. 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 Admire-31.2, but also gave a more fine-grained semantic decomposition for the members. Also, for the Judgment-31.1 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.

Keywords: VerbNet; componential analysis; lexical semantics; logical predicates; verb lexicon.

Figure 1

A screenshot of a subset of the VN class hierarchy, from the UVI.

Figure 2

A screenshot of the VN Hit-18.1 class. For a complete interactive view, see https://uvi.colorado.edu/verbnet/hit-18.1.

Figure 3

Semantics representations of the Carry-11.4 class.

Figure 4

The extended qualia structure.

Figure 5

Distribution of VerbNet classes based on their verb member count.

Figure 6

Example of hierarchy in BSO types that illustrates distinct mappings to two members of the Other_cos-45.4 class: “anglicize” and “privatize”.

Figure 7

VN classes with extra large (verb member count more than 100) and large sizes (verb member count between 50 and 100). The width of each container is equal to the size of the class in the extra large classes, and scaled to twice the size of the class in large classes.

Figure 8

A summary of the verb specific semantic features in the Marvel-31.3 VN class.

Figure 9

Verb-specific features popping up as a result of hovering over the verb “blunt”.

Figure 10

A snapshot of the tree view of the XML file for the Butter-9.9 class, selected verbs that contain verb-specific features. @fn_mapping = FrameNet mapping; @grouping = OntoNotes grouping; @name = verb lemma; @verbnet_key = sense disambiguation of the verb in verbnet; @wn = WordNet mapping; @features = verb-specific features, as a list of key-value pairs (feature type: feature value).