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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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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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Abstract:
We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph that reflects the “ambiguity” of its nodes. It uses hard clustering to discover clusters in this “disambiguated” intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can be also applied to other networks of linguistic data.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/48979/ https://www.mitpressjournals.org/doi/full/10.1162/coli_a_00354 https://doi.org/10.1162/coli_a_00354
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