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.
A variety of directional measures for unsupervised hypernymy detection @cite_20 @cite_5 @cite_19 @cite_37 @cite_27 all rely on some variation of the : If @math is a semantically narrower term than @math , then a significant number of salient distributional features of @math is expected to be included in the feature vector of @math as well. In addition, proposed the , that hypernyms tend to be less informative than hyponyms, and that they occur in more general contexts than their hyponyms. All of these approaches represent words as vectors in distributional semantic models @cite_23 , relying on the @cite_28 @cite_21 . For evaluation, these directional models use the AP measure to assess the proportion of hypernyms at the top of a score-sorted list. In a different vein, introduced three unsupervised methods drawn from visual properties of images to determine a concept's generality in hypernymy tasks.
@cite_37 @cite_28 @cite_21 @cite_19 @cite_27 @cite_23 @cite_5 @cite_20
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We test the Distributional Inclusion Hypothesis, which states that hypernyms tend to occur in a superset of contexts in which their hyponyms are found. We find that this hypothesis only holds when it is applied to relevant dimensions. We propose a robust supervised approach that achieves accuracies of .84 and .85 on two existing datasets and that can be interpreted as selecting the dimensions that are relevant for distributional inclusion. Hypernymy identification aims at detecting if is A relationship holds between two words or phrases. Most previous methods are based on lexical patterns or the Distributional Inclusion Hypothesis, and the accuracy of such methods is not ideal. In this paper, we propose a simple yet effective supervision framework to identify hypernymy relations using distributed term representations (a.k.a term embeddings). First, we design a distance-margin neural network to learn term embeddings based on some pre-extracted hypernymy data. Then, we apply such embeddings as term features to identify positive hypernymy pairs through a supervision method. Experimental results demonstrate that our approach outperforms other supervised methods on two popular datasets and the learned term embeddings has better quality than existing term distributed representations with respect to hypernymy identification. In this paper we apply existing directional similarity measures to identify hypernyms with a state-of-the-art distributional semantic model. We also propose a new directional measure that achieves the best performance in hypernym identification. In this paper, we introduce SLQS , a new entropy-based measure for the unsupervised identification of hypernymy and its directionality in Distributional Semantic Models (DSMs). SLQS is assessed through two tasks: (i.) identifying the hypernym in hyponym-hypernym pairs, and (ii.) discriminating hypernymy among various semantic relations. In both tasks, SLQS outperforms other state-of-the-art measures.