1,103 research outputs found
Unsupervised Sense-Aware Hypernymy Extraction
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
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
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks
Taxonomy Induction using Hypernym Subsequences
We propose a novel, semi-supervised approach towards domain taxonomy
induction from an input vocabulary of seed terms. Unlike all previous
approaches, which typically extract direct hypernym edges for terms, our
approach utilizes a novel probabilistic framework to extract hypernym
subsequences. Taxonomy induction from extracted subsequences is cast as an
instance of the minimumcost flow problem on a carefully designed directed
graph. Through experiments, we demonstrate that our approach outperforms
stateof- the-art taxonomy induction approaches across four languages.
Importantly, we also show that our approach is robust to the presence of noise
in the input vocabulary. To the best of our knowledge, no previous approaches
have been empirically proven to manifest noise-robustness in the input
vocabulary
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