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Aitor Soroa

Researcher at University of the Basque Country

Publications -  109
Citations -  4376

Aitor Soroa is an academic researcher from University of the Basque Country. The author has contributed to research in topics: WordNet & Computer science. The author has an hindex of 24, co-authored 96 publications receiving 3551 citations. Previous affiliations of Aitor Soroa include National University of Distance Education & Polytechnic University of Catalonia.

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Proceedings ArticleDOI

A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches

TL;DR: This paper presents and compares WordNet-based and distributional similarity approaches, and pioneer cross-lingual similarity, showing that the methods are easily adapted for a cross-lingsual task with minor losses.
Proceedings ArticleDOI

Personalizing PageRank for Word Sense Disambiguation

TL;DR: This paper proposes a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation, performing better than previous approaches in English all-words datasets.
Journal ArticleDOI

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Teven Le Scao, +386 more
- 09 Nov 2022 - 
TL;DR: BLOOM as discussed by the authors is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total).
Journal ArticleDOI

Random walks for knowledge-based word sense disambiguation

TL;DR: This article presents a WSD algorithm based on random walks over large Lexical Knowledge Bases (LKB) that performs better than other graph-based methods when run on a graph built from WordNet and eXtended WordNet.
Proceedings ArticleDOI

SemEval-2007 Task 02: Evaluating Word Sense Induction and Discrimination Systems

TL;DR: This work reused the SemEval-2007 English lexical sample subtask of task 17, and set up both clustering-style unsupervised evaluation and a supervised evaluation (using the part of the dataset for mapping) to allow for comparison across sense-induction and discrimination systems.