Abstract
Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification, extending the traditional semantic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem.
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Notes
- 1.
Cause-Effect, Instrument-Agency, Product-Producer, Content-Container, Entity-Origin, Entity-Destination, Component-Whole, Member-Collection, Communication-Topic.
- 2.
Composite Semantic Relation Classification.
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Barzegar, S., Freitas, A., Handschuh, S., Davis, B. (2017). Composite Semantic Relation Classification. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_49
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