7 research outputs found

    Unsupervised Sense-Aware Hypernymy Extraction

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    In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.Comment: In Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018). Vienna, Austri

    Improving Hypernymy Extraction with Distributional Semantic Classes

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    In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms. We present methods for inducing sense-aware semantic classes using distributional semantics and using these induced semantic classes for filtering noisy hypernymy relations. Denoising of hypernyms is performed by labeling each semantic class with its hypernyms. On the one hand, this allows us to filter out wrong extractions using the global structure of distributionally similar senses. On the other hand, we infer missing hypernyms via label propagation to cluster terms. We conduct a large-scale crowdsourcing study showing that processing of automatically extracted hypernyms using our approach improves the quality of the hypernymy extraction in terms of both precision and recall. Furthermore, we show the utility of our method in the domain taxonomy induction task, achieving the state-of-the-art results on a SemEval'16 task on taxonomy induction.Comment: In Proceedings of the 11th Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japa

    Crowsdsourcing semantic web

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    Finding easier and less resource-intensive ways of building knowledge resources is neces- sary to help broaden the coverage and use of semantic web technologies. Crowdsourcing presents a means through which knowledge can be efficiently acquired to build semantic resources. Crowds can be identified that represent communities whose knowledge could be used to build domain ontologies. This work presents a knowledge acquisition approach aimed at incorporating ontology engineering tasks into community crowd activity. The success of this approach is evaluated by the degree to which a crowd consensus is reached regarding the description of the target domain. Two experiments are described which test the effectiveness of the approach. The first experiment tests the approach by using a crowd that is aware of the knowledge acquisition task. In the second experiment, the crowd is unaware of the knowledge acquisition task and is motivated to contribute through the use of an interactive map. The results of these two experiments show that a similar consensus is reached from both experiments, suggesting that the approach offered provides a valid mechanism for incorporating knowledge acquisition tasks into routine crowd activity

    Extracting Hypernym Pairs from the Web

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    We apply pattern-based methods for collecting hypernym relations from the web. We compare our approach with hypernym extraction from morphological clues and from large text corpora. We show that the abundance of available data on the web enables obtaining good results with relatively unsophisticated techniques.
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