A Comparison of Selectional Preference Models for Automatic Verb Classification

Roberts, Will; Egg, Markus

doi:10.3115/v1/d14-1057

Cited by 2 publications

(2 citation statements)

References 35 publications

(29 reference statements)

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“…Apart from these, methods which induce verb classes of other languages (e.g. Estonian [39] and German [40]) as well as of a particular type of verb (e.g. Propositional attitude verbs including think and want [41]) have also recently emerged.…”

Section: Related Workmentioning

confidence: 99%

A neural classification method for supporting the creation of BioVerbNet

et al. 2019

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BackgroundVerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data.ResultsDirect evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet.ConclusionThis work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine.Electronic supplementary materialThe online version of this article (10.1186/s13326-018-0193-x) contains supplementary material, which is available to authorized users.

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Section: Related Workmentioning

confidence: 99%

A neural classification method for supporting the creation of BioVerbNet

et al. 2019

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“…The concept of reciprocity has been used to nd the selectional preferences between two users, which means the number of times these users have directly communicated with each other. Verb classi cation (Roberts et al, 2014), in this paper, Robert and his colleagues have presented a report for comparing SP models in the application of automatic verb classi cation in the German language. In this research, German verbs are categorized through valence and subcategorization.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Knowledge graph with Selectional Preferences

Torabian,

Radaei,

Minaei-Bidgoli

et al. 2023

Preprint

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Knowledge graphs facilitate the extraction of knowledge from data and provide a comprehensive overview of all knowledge within departments, across departments, and global organizations. To enrich the extracted knowledge, several solutions have been proposed to complete the knowledge graph. This study investigates the effectiveness of using the selectional preferences technique to establish the dependency between triple entities in a knowledge graph. To this end, this paper proposes a three-phase approach, Selectional Preferences Knowledge Graph (SP-KG) to determine the relevance degree of all triple entities in the knowledge graph based on selectional preferences. The results demonstrate that the three-phase approach accurately identifies entity dependencies, which can be applied for knowledge extraction. Furthermore, this approach uses a Persian knowledge graph, which enhances the completeness of Persian language knowledge. Finally, the SP-KG model is evaluated on the SP-10K dataset proposed in state-of-the-art research to prove its accuracy. Similar results for both datasets indicate good performance. Glove and Word2Vec are also used to compare the proposed model with other methods. The accuracy results of the 2-way and 3-way pseudo-disambiguation demonstrate the high quality of the results produced by the proposed model.

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A Comparison of Selectional Preference Models for Automatic Verb Classification

Cited by 2 publications

References 35 publications

A neural classification method for supporting the creation of BioVerbNet

A neural classification method for supporting the creation of BioVerbNet

Enhancing Knowledge graph with Selectional Preferences

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