Abstract
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word’s similarity with each of the two semantically similar words, based on an extended word embedding method and WordNet. Instead of computing the similarities between the attribute and semantically similar words by using standard word embeddings, we propose a novel method that combines word and context embeddings which can better measure similarities. Our model is simple and effective, which achieves an average F1 score of 0.62 on the test set.
Highlights
Capturing discriminative attributes is a novel task, which is very different from classical semantic tasks that model similarities in semantics
Such results demonstrate that using word and context embeddings together can better distinguish two semantically similar words with an attribute word, than using standard word embeddings
We extended traditional word embedding methods (CBOW and Skip-gram) to distinguish two semantically similar words using an attribute word
Summary
Capturing discriminative attributes is a novel task, which is very different from classical semantic tasks that model similarities in semantics. How to capture discriminative attributes between semantically similar words is still a challenge for traditional word embedding methods, because these methods are designed to capture similar semantics. We propose a novel framework that differentiates two semantically similar words with the attribute word by using their word and context embeddings. We experimented with both Continuous Bag of Words (CBOW) and Skip-gram, demonstrating that using the combination of word and context embeddings outperforms using word embeddings alone. Our similarity measure can recognise the discriminative attributes of two semantically similar words more accurately
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