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- Eneko Agirre, Aitor Soroa
- EACL
- 2009
In this paper we propose a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation. Our algorithm uses the full graph of… (More)
- Eneko Agirre, Enrique Alfonseca, Keith B. Hall, Jana Kravalova, Marius Pasca, Aitor Soroa
- HLT-NAACL
- 2009
This paper presents and compares WordNetbased and distributional similarity approaches. The strengths and weaknesses of each approach regarding similarity and relatedness tasks are discussed, and a… (More)
- Eneko Agirre, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre
- SemEval@NAACL-HLT
- 2012
Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two texts. This paper presents the results of the STS pilot task in Semeval. The training data contained 2000… (More)
- Eneko Agirre, German Rigau
- COLING
- 1996
This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. The method relies on the use of the widecoverage noun taxonomy of… (More)
- Jordi Atserias, Luis Villarejo, +4 authors Piek T. J. M. Vossen
- 2004
This paper describes the first version of the Multilingual Central Repository, a lexical knowledge base developed in the framework of the MEANING project. Currently the MCR integrates into the… (More)
- Mikel Artetxe, Gorka Labaka, Eneko Agirre, Kyunghyun Cho
- ArXiv
- 2017
In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been… (More)
- Eneko Agirre, Olatz Ansa, Eduard H. Hovy, David MartÃnez
- ECAI Workshop on Ontology Learning
- 2000
This paper explores the possibility to exploit text on the world wide web in order to enrich the concepts in existing ontologies. First, a method to retrieve documents from the WWW related to a… (More)
- Eneko Agirre, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo
- *SEM@NAACL-HLT
- 2013
In Semantic Textual Similarity (STS), systems rate the degree of semantic equivalence, on a graded scale from 0 to 5, with 5 being the most similar. This year we set up two tasks: (i) a core task… (More)
- Mikel Artetxe, Gorka Labaka, Eneko Agirre
- EMNLP
- 2016
Mapping word embeddings of different languages into a single space has multiple applications. In order to map from a source space into a target space, a common approach is to learn a linear mapping… (More)