Improving Hypernymy Detection with an Integrated Path-based and Distributional Method
arXiv:1603.06076
Abstract
Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches. Distributional methods, whose supervised variants are the current best performers, and path-based methods, which received less research attention. We suggest an improved path-based algorithm, in which the dependency paths are encoded using a recurrent neural network, that achieves results comparable to distributional methods. We then extend the approach to integrate both path-based and distributional signals, significantly improving upon the state-of-the-art on this task.
ACL 2016
References in corpus (5)
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Cited by in corpus (3)
- Hypernym Detection Using Strict Partial Order Networks
- IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings
- From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information