What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Natural language processing
- Linguistics
His primary scientific interests are in Artificial intelligence, Natural language processing, SemEval, Word-sense disambiguation and WordNet.
His Artificial intelligence research includes themes of Machine learning and Set.
His Natural language processing study incorporates themes from Embedding, Graph and Information retrieval.
His SemEval study integrates concerns from other disciplines, such as Speech recognition and Word sense.
Eneko Agirre works mostly in the field of Word-sense disambiguation, limiting it down to topics relating to Lexical ambiguity and, in certain cases, Taxonomy and Brown Corpus, as a part of the same area of interest.
Eneko Agirre combines subjects such as Ontology, Decision list, Word lists by frequency, World Wide Web and Lexical knowledge with his study of WordNet.
His most cited work include:
- A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches (715 citations)
- SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (552 citations)
- SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity (492 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Natural language processing, Information retrieval, WordNet and Word-sense disambiguation.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Domain, Machine learning and Set.
His work carried out in the field of Natural language processing brings together such families of science as Similarity and SemEval.
Eneko Agirre focuses mostly in the field of Information retrieval, narrowing it down to topics relating to Context and, in certain cases, Entity linking.
His work investigates the relationship between Word-sense disambiguation and topics such as Graph that intersect with problems in PageRank.
His studies deal with areas such as Language model and Translation as well as Machine translation.
He most often published in these fields:
- Artificial intelligence (73.65%)
- Natural language processing (67.91%)
- Information retrieval (20.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (73.65%)
- Natural language processing (67.91%)
- Word (18.92%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Natural language processing, Word, Machine translation and Question answering.
His research in Artificial intelligence focuses on subjects like Machine learning, which are connected to Document classification.
Eneko Agirre has included themes like Similarity, Inference and Representation in his Natural language processing study.
His research integrates issues of Ontology, Embedding, Set and Semantic similarity in his study of Word.
His research in Machine translation intersects with topics in Algorithm, Initialization and Translation.
His Question answering research incorporates elements of Relational database, Graph, Natural language and Closed-world assumption.
Between 2018 and 2021, his most popular works were:
- An Effective Approach to Unsupervised Machine Translation (58 citations)
- Analyzing the Limitations of Cross-lingual Word Embedding Mappings. (27 citations)
- Survey on evaluation methods for dialogue systems (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Natural language processing
- Linguistics
Eneko Agirre focuses on Artificial intelligence, Natural language processing, Word, Machine translation and Word embedding.
His Artificial intelligence study focuses on WordNet in particular.
Eneko Agirre undertakes interdisciplinary study in the fields of Natural language processing and Content through his research.
The concepts of his Word study are interwoven with issues in Language model, Embedding, Similarity, Semantic similarity and Representation.
His Semantic similarity research incorporates themes from Margin, Ontology and Similarity.
His Machine translation research integrates issues from Translation and Cross lingual.
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