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On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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Come hither or go away? Recognising pre-electoral coalition signals in the news ...
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Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
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Come hither or go away? Recognising pre-electoral coalition signals in the news
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AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings ...
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Word Sense Disambiguation for 158 Languages using Word Embeddings Only ...
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Logacheva, Varvara; Teslenko, Denis; Shelmanov, Artem; Remus, Steffen; Ustalov, Dmitry; Kutuzov, Andrey; Artemova, Ekaterina; Biemann, Chris; Ponzetto, Simone Paolo; Panchenko, Alexander. - : arXiv, 2020
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Abstract:
Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al. (2018), enabling WSD in these ... : 10 pages, 5 figures, 4 tables, accepted at LREC 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2003.06651 https://arxiv.org/abs/2003.06651
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment
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Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.2, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020
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A Twitter Political Corpus of the 2019 10N Spanish Election
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AraWEAT: Multidimensional analysis of biases in Arabic word embeddings
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SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
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Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ...
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Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ...
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HHMM at SemEval-2019 Task 2: Unsupervised frame induction using contextualized word embeddings
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Policy preference detection in parliamentary debate motions
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Watset: Local-global graph clustering with applications in sense and frame induction
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