Fully-Unsupervised Embeddings-Based Hypernym Discovery
| Autor: | Simone Balloccu, Maurizio Atzori |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
word embeddings
Relation (database) Computer science 02 engineering and technology computer.software_genre unsupervised learning Ranking (information retrieval) Set (abstract data type) Semantic similarity 020204 information systems 0202 electrical engineering electronic engineering information engineering Word2vec natural language processing lcsh:T58.5-58.64 business.industry lcsh:Information technology word2vec hypernym discovery SemEval Word lists by frequency Unsupervised learning 020201 artificial intelligence & image processing natural language understanding Artificial intelligence business computer Natural language processing Information Systems |
| Zdroj: | Information Volume 11 Issue 5 Information, Vol 11, Iss 268, p 268 (2020) |
| ISSN: | 2078-2489 |
| DOI: | 10.3390/info11050268 |
| Popis: | The hypernymy relation is the one occurring between an instance term and its general term (e.g., &ldquo lion&rdquo and &ldquo animal&rdquo &ldquo Italy&rdquo country&rdquo ). This paper we addresses Hypernym Discovery, the NLP task that aims at finding valid hypernyms from words in a given text, proposing HyperRank, an unsupervised approach that therefore does not require manually-labeled training sets as most approaches in the literature. The proposed algorithm exploits the cosine distance of points in the vector space of word embeddings, as already proposed by previous state of the art approaches, but the ranking is then corrected by also weighting word frequencies and the absolute level of similarity, which is expected to be similar when measuring co-hyponyms and their common hypernym. This brings us two major advantages over other approaches&mdash (1) we correct the inadequacy of semantic similarity which is known to cause a significant performance drop and (2) we take into accounts multiple words if provided, allowing to find common hypernyms for a set of co-hyponyms&mdash a task ignored in other systems but very useful when coupled with set expansion (that finds co-hyponyms automatically). We then evaluate HyperRank against the SemEval 2018 Hypernym Discovery task and show that, regardless of the language or domain, our algorithm significantly outperforms all the existing unsupervised algorithms and some supervised ones as well. We also evaluate the algorithm on a new dataset to measure the improvements when finding hypernyms for sets of words instead of singletons. |
| Databáze: | OpenAIRE |
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