7 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
Recommended from our members
Identifying lexical relationships and entailments with distributional semantics
Many modern efforts in Natural Language Understanding depend on rich and powerful semantic representations of words. Systems for sophisticated logical and textual reasoning often depend heavily on lexical resources to provide critical information about relationships between words, but these lexical resources are expensive to create and maintain, and are never fully comprehensive. Distributional Semantics has long offered methods for automatically inducing meaning representations from large corpora, with little or no annotation efforts. The resulting representations are valuable proxies of semantic similarity, but simply knowing two words are similar cannot tell us their relationship, or whether one entails the other.
In this thesis, we consider how methods from Distributional Semantics may be applied to the difficult task of lexical entailment, where one must predict whether one word implies another. We approach this by showing contributions in areas of hypernymy detection, lexical relationship prediction, lexical substitution, and textual entailment. We propose novel experimental setups, models, analysis, and interpretations, which ultimate provide us with a better understanding of both the nature of lexical entailment, as well as the information available within distributional representations.Computer Science
Crowsdsourcing semantic web
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
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.