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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 Hits 1 – 13 of 13 1 Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech Pedrelli, Luca; Hinaut, Xavier In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021) BASE Show details 2 Training corpus ssj500k 2.3 Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž. - : Centre for Language Resources and Technologies, University of Ljubljana, 2021 BASE Show details 3 Training corpus ssj500k 2.2 Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž. - : Centre for Language Resources and Technologies, University of Ljubljana, 2019 BASE Show details 4 Training corpus ssj500k 2.1 Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž. - : Centre for Language Resources and Technologies, University of Ljubljana, 2018 BASE Show details 5 Training corpus hr500k 1.0 Ljubešić, Nikola; Agić, Željko; Klubička, Filip. - : Jožef Stefan Institute, 2018 BASE Show details 6 Neural Models for Large-Scale Semantic Role Labelling FitzGerald, Nicholas Arthur. - 2018 Abstract: Thesis (Ph.D.)--University of Washington, 2018 ; Recovering predicate-argument structures from natural language sentences is an important task in natural language processing (NLP), where the goal is to identify ``who did what to whom'' with respect to events described in a sentence. A key challenge in this task is sparsity of labeled data: a given predicate-role instance may only occur a handful of times in the training set. While attempts have been made to collect large, diverse datasets which could help mitigate this sparseness, these effort are hampered by the difficulty inherent in labelling traditional SRL formalisms such as PropBank and FrameNet. We take a two-pronged approach to solving these issues. First, we develop models which can be used to jointly represent multiple SRL annotation schemes, allowing us to pool annotations between multiple datasets. We present a new method for semantic role labeling in which arguments and semantic roles are jointly embedded in a shared vector space for a given predicate. We further show how the model can learn jointly from PropBank and FrameNet annotations to obtain additional improvements on the smaller FrameNet dataset. Next, we demonstrate that crowdsourcing techniques can be used to collect a large, high-quality SRL dataset at much lower cost than previous methods, and that this data can be used to learn a high-quality SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. Finally, we combine these two approaches, investigating whether QA-SRL annotations can be used to improve perfomance on PropBank in a multitask learning setup. We find that using the QA-SRL data improves performance in regimes with small amounts of in-domain PropBank data, but that these improvements are overshadowed by those obtained by using deep contextual word representations trained on large amounts of unlabeled text, raising important questions for future work as to the utility of multitask training relative to these unsupervised approaches. Keyword: Computer science; Computer science and engineering; Deep Learning; Frame Semantic Parsing; Linguistics; Natural Language Processing; Parsing; Semantic Role Labelling; Semantics URL: http://hdl.handle.net/1773/43017 BASE Hide details 7 More than Word Cooccurrence: Exploring Support and Opposition in International Climate Negotiations with Semantic Parsing Ruiz, Pablo; Plancq, Clément; Poibeau, Thierry In: Proceedings of LREC, the 10th Language Resources and Evaluation Conference ; LREC: The 10th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-01326692 ; LREC: The 10th Language Resources and Evaluation Conference, ELRA, May 2016, Portorož, Slovenia. pp.1902 ; http://lrec2016.lrec-conf.org/en/ (2016) BASE Show details 8 Global methods for cross-lingual semantic role and predicate labelling Van Der Plas, Lonneke; Apidianaki, Marianna; Chen, Chenhua In: International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01838563 ; International Conference on Computational Linguistics, Jan 2014, Dublin, Ireland (2014) BASE Show details 9 Utilisation de représentations de mots pour l’étiquetage de rôles sémantiques suivant FrameNet Léchelle, William. - 2014 BASE Show details 10 Noun/Verb Inference Paul Bedaride; Claire Gardent; Université Henri Poincaré In: http://hal.inria.fr/docs/00/43/87/31/PDF/ltc-036-bedaride.pdf (2009) BASE Show details 11 Noun/Verb Inference Bedaride, Paul; Gardent, Claire In: Human Language Technologies au a Challenge for the Computer Science and Linguistic ; 4th Language & Technology Conference: Human Language Technologies au a Challenge for the Computer Science and Linguistics - LTC'09 ; https://hal.inria.fr/inria-00438731 ; 4th Language & Technology Conference: Human Language Technologies au a Challenge for the Computer Science and Linguistics - LTC'09, Nov 2009, Poznań, Poland. pp.311--315 (2009) BASE Show details 12 Natural language understanding in controlled virtual environments YE, PATRICK. - 2009 BASE Show details 13 PropBank: Semantics of New Predicate Types Claire Bonial; Julia Bonn; Kathryn Conger... In: http://www.lrec-conf.org/proceedings/lrec2014/pdf/1012_Paper.pdf BASE Show details

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