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Language Models Explain Word Reading Times Better Than Empirical Predictability ...
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SCoT: Sense Clustering over Time: a tool for the analysis of lexical change ...
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Language Models Explain Word Reading Times Better Than Empirical Predictability
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In: Front Artif Intell (2022)
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Probing Pre-trained Language Models for Semantic Attributes and their Values ...
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Comparison of Different Lexical Resources With Respect to the Tip-of-the-Tongue Problem
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In: ISSN: 1598-2327 ; EISSN: 1976-6939 ; Journal of Cognitive Science ; https://hal.archives-ouvertes.fr/hal-03168850 ; Journal of Cognitive Science, Institute for Cognitive Science, Seoul National University, 2020, 21 (2), pp.193-252. ⟨10.17791/jcs.2020.21.2.193⟩ (2020)
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Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets ...
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Word Sense Disambiguation for 158 Languages using Word Embeddings Only ...
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Individual corpora predict fast memory retrieval during reading ...
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Individual corpora predict fast memory retrieval during reading ...
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Making Fast Graph-based Algorithms with Graph Metric Embeddings ...
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On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings ...
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Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings ...
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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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Adaptive Approaches to Natural Language Processing in Annotation and Application ; Adaptive Ansätze zur Verarbeitung natürlicher Sprache in Annotation und Anwendung
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Yimam, Seid Muhie. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2019
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HHMM at SemEval-2019 Task 2: Unsupervised frame induction using contextualized word embeddings
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