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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 53 1 Deriving Disinformation Insights from Geolocalized Twitter Callouts ... Tuxworth, David; Antypas, Dimosthenis; Espinosa-Anke, Luis. - : arXiv, 2021 BASE Show details 2 XLM-T: A Multilingual Language Model Toolkit for Twitter ... Barbieri, Francesco; Anke, Luis Espinosa; Camacho-Collados, Jose. - : arXiv, 2021 BASE Show details 3 Distilling Relation Embeddings from Pre-trained Language Models ... Ushio, Asahi; Camacho-Collados, Jose; Schockaert, Steven. - : arXiv, 2021 BASE Show details 4 Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction ... Ushio, Asahi; Liberatore, Federico; Camacho-Collados, Jose. - : arXiv, 2021 Abstract: Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. In particular, term weighting is the basis for keyword extraction. However, there are relatively few evaluation studies that shed light about the strengths and shortcomings of each weighting scheme. In fact, in most cases researchers and practitioners resort to the well-known tf-idf as default, despite the existence of other suitable alternatives, including graph-based models. In this paper, we perform an exhaustive and large-scale empirical comparison of both statistical and graph-based term weighting methods in the context of keyword extraction. Our analysis reveals some interesting findings such as the advantages of the less-known lexical specificity with respect to tf-idf, or the qualitative differences between statistical and graph-based methods. Finally, based on our findings we discuss and devise some suggestions for practitioners. Source code to reproduce our experimental results, including a keyword ... : Accepted by EMNLP 2021 main conference ... Keyword: Artificial Intelligence cs.AI; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2104.08028 https://arxiv.org/abs/2104.08028 BASE Hide details 5 Modelling general properties of nouns by selectively averaging contextualised embeddings Li, Na; Bouraoui, Zied; Camacho Collados, Jose. - 2021 BASE Show details 6 BERT is to NLP what AlexNet is to CV: can pre-trained language models identify analogies? Ushio, Asahi; Schockaert, Steven; Camacho Collados, Jose. - 2021 BASE Show details 7 Back to the basics: a quantitative analysis of statistical and graph-based term weighting schemes for keyword extraction Camacho Collados, Jose; Liberatore, Federico; Ushio, Asahi. - : Association for Computational Linguistics, 2021 BASE Show details 8 XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization ... Raganato, Alessandro; Pasini, Tommaso; Camacho-Collados, Jose. - : arXiv, 2020 BASE Show details 9 Learning Cross-Lingual Word Embeddings from Twitter via Distant Supervision Barbieri, Francesco; Doval, Yerai; Martínez-Cámara, Eugenio... In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 14 (2020): Fourteenth International AAAI Conference on Web and Social Media; 72-82 ; 2334-0770 ; 2162-3449 (2020) BASE Show details 10 Analysis and Evaluation of Language Models for Word Sense Disambiguation ... Loureiro, Daniel; Rezaee, Kiamehr; Pilehvar, Mohammad Taher. - : arXiv, 2020 BASE Show details 11 Understanding the source of semantic regularities in word embeddings Chiang, Hsiao-Yu; Camacho-Collados, Jose; Pardos, Zachary. - : Association for Computational Linguistics, 2020 BASE Show details 12 Learning cross-lingual word embeddings from Twitter via distant supervision Camacho Collados, Jose; Doval, Yerai; Martínez-Cámara, Eugenio. - : Association for the Advancement of Artificial Intelligence, 2020 BASE Show details 13 Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings ... Doval, Yerai; Camacho-Collados, Jose; Espinosa-Anke, Luis. - : arXiv, 2019 BASE Show details 14 On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning ... Doval, Yerai; Camacho-Collados, Jose; Espinosa-Anke, Luis. - : arXiv, 2019 BASE Show details 15 Relational word embeddings Espinosa-Anke, Luis; Camacho Collados, Jose; Schockaert, Steven. - 2019 BASE Show details 16 SenseDefs: a multilingual corpus of semantically annotated textual definitions : Exploiting multiple languages and resources jointly for high-quality Word Sense Disambiguation and Entity Linking [<Journal>] Camacho-Collados, Jose [Verfasser]; Delli Bovi, Claudio [Sonstige]; Raganato, Alessandro [Sonstige]. DNB Subject Category Language Show details 17 Improving Cross-Lingual Word Embeddings by Meeting in the Middle ... Doval, Yerai; Camacho-Collados, Jose; Espinosa-Anke, Luis. - : arXiv, 2018 BASE Show details 18 The Interplay between Lexical Resources and Natural Language Processing ... Camacho-Collados, Jose; Espinosa-Anke, Luis; Pilehvar, Mohammad Taher. - : arXiv, 2018 BASE Show details 19 From Word to Sense Embeddings: A Survey on Vector Representations of Meaning ... Camacho-Collados, Jose; Pilehvar, Mohammad Taher. - : arXiv, 2018 BASE Show details 20 A Short Survey on Sense-Annotated Corpora ... Pasini, Tommaso; Camacho-Collados, Jose. - : arXiv, 2018 BASE Show details Page: 1 2 3

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