We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym @math hyponym distributional hierarchy. Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages. Results on benchmark datasets show that HyperVec outperforms both state @math of @math the @math art unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.
These approaches are closest to our work. proposed a dynamic distance-margin model to learn term embeddings that capture properties of hypernymy. The neural model is trained on the taxonomic relation data which is pre-extracted. The resulting term embeddings are fed to an SVM classifier to predict hypernymy. However, this model only learns term pairs without considering their contexts, leading to a lack of generalization for term embeddings. introduced a dynamic weighting neural network to learn term embeddings that encode information about hypernymy and also about their contexts, considering all words between a hypernym and its hyponym in a sentence. The proposed model is trained on a set of hypernym relations extracted from WordNet @cite_33 . The embeddings are applied as features to detect hypernymy, using an SVM classifier. handles the drawback of the approach by , considering the contextual information between two terms; however the method still is not able to determine the directionality of a hypernym pair. proposed a method to encode order into learned distributed representations, to explicitly model partial order structure of the visual-semantic hierarchy or the hierarchy of hypernymy in WordNet. The resulting vectors are used to predict the transitive hypernym relations in WordNet.
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Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet 1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].