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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 – 4 of 4 1 Composition of Embeddings : Lessons from Statistical Relational Learning Sileo, Damien; Van De Cruys, Tim; Pradel, Camille; Muller, Philippe In: Proceedings of SEM 2019 ; 8th Joint Conference on Lexical and Computational Semantics (SEM 2019) ; https://hal.archives-ouvertes.fr/hal-02397476 ; 8th Joint Conference on Lexical and Computational Semantics (SEM 2019), Jun 2019, Minneapolis, United States. pp.33-43 (2019) Abstract: International audience ; Various NLP problems -- such as the prediction of sentence similarity, entailment, and discourse relations -- are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. A popular model for such problems is to embed sentences into fixed size vectors, and use composition functions (e.g. concatenation or sum) of those vectors as features for the prediction. At the same time, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference). We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; Embeddings; Relational learning URL: https://hal.archives-ouvertes.fr/hal-02397476/file/sileo_24995.pdf https://hal.archives-ouvertes.fr/hal-02397476 https://hal.archives-ouvertes.fr/hal-02397476/document BASE Hide details 2 Mining Discourse Markers for Unsupervised Sentence Representation Learning Sileo, Damien; Van De Cruys, Tim; Pradel, Camille... In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2019) ; https://hal.archives-ouvertes.fr/hal-02397473 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2019), Jun 2019, Minneapolis, United States. pp.3477-3486 (2019) BASE Show details 3 Système d’ensemble pour la classification de tweets, DEFT 2017 Sileo, Damien; Pradel, Camille; Muller, Philippe... In: Atelier Défi Fouille de Textes : Analyse d'opinion et langage figuratif dans des tweets en français@ TALN/RECITAL 2017 (DEFT 2017) ; https://hal.archives-ouvertes.fr/hal-03120281 ; Atelier Défi Fouille de Textes : Analyse d'opinion et langage figuratif dans des tweets en français@ TALN/RECITAL 2017 (DEFT 2017), Jun 2017, Orléans, France. pp.27-31 ; http://talnarchives.atala.org/ateliers/2017/DEFT/2.pdf (2017) BASE Show details 4 SWIP at QALD-3 : results, criticisms and lesson learned Pradel, Camille; Peyet, Guillaume; Haemmerlé, Ollivier... In: QALD-3 : Multilingual Question Answering over Linked Data ; 3rd open challenge on Question Answering over Linked Data (QALD 2013) ; https://hal.archives-ouvertes.fr/hal-01193095 ; 3rd open challenge on Question Answering over Linked Data (QALD 2013), Sep 2013, Valencia, Spain. pp. 1-13 (2013) BASE Show details

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