UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes

2018-06-01 · SEMEVAL 2018 6 · Milton King, Ali Hakimi Parizi, Paul Cook

In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.

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Tasks

Semantic Textual SimilaritySentenceWord Embeddings

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Lexical Complexity Prediction Prediction

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Sentence

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