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Seq-to-NSeq model for multi-summary generation
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In: ESANN 2020 ; https://hal.archives-ouvertes.fr/hal-02902734 ; ESANN 2020, Oct 2020, Bruges, Belgium (2020)
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Quality of syntactic implication of RL-based sentence summarization
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In: AAAI Workshop on Engineering Dependable and Secure Machine Learning Systems 2020 ; https://hal.archives-ouvertes.fr/hal-02883327 ; AAAI Workshop on Engineering Dependable and Secure Machine Learning Systems 2020, Feb 2020, New York, United States (2020)
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Normalisation of 16th and 17th century texts in French and geographical named entity recognition
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In: 4th ACM SIGSPATIAL International Workshop on Geospatial Humanities ; ACM SIGSPATIAL GeoHumanities'20 ; https://hal-upec-upem.archives-ouvertes.fr/hal-02955867 ; ACM SIGSPATIAL GeoHumanities'20, ACM, Nov 2020, Seattle (virtual), United States. pp.28-34, ⟨10.1145/3423337.3429437⟩ ; https://ludovicmoncla.github.io/sigspatial-geohumanities-2020/ (2020)
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Automatic processing of Historical Arabic Documents: a comprehensive survey
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In: ISSN: 0031-3203 ; Pattern Recognition ; https://hal.archives-ouvertes.fr/hal-02481354 ; Pattern Recognition, Elsevier, 2020, 100, pp.107144-1:107144-17. ⟨10.1016/j.patcog.2019.107144⟩ (2020)
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A study of semantic projection from single word terms to multi-word terms in the environment domain
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In: 6th International Workshop on Computational Terminology (COMPUTERM 2020) ; https://hal.archives-ouvertes.fr/hal-03478024 ; 6th International Workshop on Computational Terminology (COMPUTERM 2020), May 2020, Marseille, France. pp.ISBN 979-10-95546-57-3 (2020)
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An Evaluation Dataset for Identifying Communicative Functions of Sentences in English Scholarly Papers
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In: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020) ; 12th Conference on Language Resources and Evaluation (LREC 2020) ; https://hal.archives-ouvertes.fr/hal-03272825 ; 12th Conference on Language Resources and Evaluation (LREC 2020), May 2020, Marseille, France (2020)
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Improving Short Text Classification Through Global Augmentation Methods
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In: Lecture Notes in Computer Science ; 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE) ; https://hal.inria.fr/hal-03414750 ; 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.385-399, ⟨10.1007/978-3-030-57321-8_21⟩ (2020)
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Overview of the Fourth BUCC Shared Task: Bilingual Dictionary Induction from Comparable Corpora
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In: 13th Workshop on Building and Using Comparable Corpora (BUCC) ; https://hal.archives-ouvertes.fr/hal-03100822 ; 13th Workshop on Building and Using Comparable Corpora (BUCC), May 2020, Marseille, France. pp.6-13 (2020)
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From Linguistic Research Projects to Language Technology Platforms: A Case Study in Learner Data
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In: LREC2020 ; https://hal.archives-ouvertes.fr/hal-02634745 ; LREC2020, European Language Resources Association (ELRA), May 2020, Marseille, France. pp.112-120 ; http://www.lrec-conf.org/proceedings/lrec2020/workshops/IWLTP2020/index.html (2020)
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Transformations syntaxiques pour une aide à l'apprentissage de la lecture : typologie, adéquation et corpus adaptés
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In: ISSN: 2261-2424 ; SHS Web of Conferences ; https://hal.archives-ouvertes.fr/hal-02562205 ; SHS Web of Conferences, EDP Sciences, 2020, 7e Congrès Mondial de Linguistique Française 78, pp.14006. ⟨10.1051/shsconf/20207814006⟩ (2020)
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Représentations lexicales pour la détection non supervisée d'événements dans un flux de tweets : étude sur des corpus français et anglais
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In: Extraction et Gestion des connaissances, EGC 2020 ; https://hal-centralesupelec.archives-ouvertes.fr/hal-02432990 ; Extraction et Gestion des connaissances, EGC 2020, Jan 2020, Bruxelles, Belgique (2020)
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Patch-based Identification of Lexical Semantic Relations
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In: 42nd European Conference on Information Retrieval (ECIR) ; https://hal.archives-ouvertes.fr/hal-02400661 ; 42nd European Conference on Information Retrieval (ECIR), 2020, Lisbon, Portugal (2020)
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Inference Annotation of a Chinese Corpus for Opinion Mining
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In: LREC ; https://hal-inalco.archives-ouvertes.fr/hal-02507170 ; LREC, May 2020, Marseille, France (2020)
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Lexicon-Grammar based open information extraction from natural language sentences in Italian
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In: ISSN: 0957-4174 ; Expert Systems with Applications ; https://hal.archives-ouvertes.fr/hal-02291746 ; Expert Systems with Applications, Elsevier, 2020, pp.112954. ⟨10.1016/j.eswa.2019.112954⟩ (2020)
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Abstract:
International audience ; In the last decade, the quantity of readily accessible text has grown rapidly and enormously, long exceeding the capacity of humans to read and understand it. One of the most interesting strategies proposed to fulfil this need is known as Open Information Extraction (OIE). It is essentially devised to read in sentences and rapidly extract one or more domain-independent coherent propositions, each represented by a verb relation and its arguments. Even though many OIE approaches exist for English, no significant research has been conducted about OIE on Italian texts. Due to the usage of language-specific features, OIE systems operating in other languages are not directly applicable for Italian. Therefore, this paper proposes, as first contribution, a novel approach to perform OIE for Italian language, based on standard linguistic structures to analyze sentences and on a set of verbal behavior patterns to extract information from them. These patterns are built combining a solid linguistic theoretical framework, i.e. Lexicon-Grammar (LG), and distributional profiles extracted from a contemporary Italian corpus, i.e. itWaC. Starting from simple sentences, the approach is able to determine elementary tuples, then, all their permutations, by adding complements and adverbials, and, finally, n-ary propositions, by granting syntactic invariance, preserving the overall grammaticality and also respecting some syntactic constraints and selection preferences, thus approximating a first level of semantic acceptability. As second contribution of this work, a gold standard dataset for the Italian language has been built from the itWaC corpus, aimed at being widely used to enable the experimental validation of OIE solutions. It has been manually and independently labeled by four Italian native speakers with all the n-ary propositions that can be extracted, following the criteria of grammaticality and acceptability, i.e. granting syntactic well-formedness and meaningfulness in the context. Finally, the proposed approach has been experimented and quantitatively validated on this gold standard dataset, also in comparison with an indirect approach translating input sentences and output propositions from Italian to English and vice versa and embedding an OIE approach for English, as well as with an OIE system for Italian previously presented by the authors. The results obtained have shown the effectiveness of the proposed approach in generating propositions with respect to these criteria of grammaticality and acceptability. Even if the approach has been evaluated for the Italian language, it is essentially based on linguistic resources produced by LG, which exist for many languages besides Italian and a representative corpus for the language under consideration. Given these premises, it has a general basis from a methodological perspective and can be proficiently extended also to other languages.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; Italian Language; Lexicon-Grammar; N-ary Propositions; Natural Language Processing; Open Information Extraction
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URL: https://doi.org/10.1016/j.eswa.2019.112954
https://hal.archives-ouvertes.fr/hal-02291746
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Syntactic and Semantic Impact of Prepositions in Machine Translation : An Empirical Study of French-English Translation of Prepositions ‘à’, ‘de’ and ‘en’
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In: Human Language Technology. Challenges for Computer Science and Linguistics 8th Language and Technology Conference, LTC 2017, Poznań, Poland, November 17–19, 2017, Revised Selected Papers ; 8th Language and Technology Conference (LTC 2017) ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-03091307 ; Human Language Technology. Challenges for Computer Science and Linguistics 8th Language and Technology Conference, LTC 2017, Poznań, Poland, November 17–19, 2017, Revised Selected Papers, 12598, pp.273-287, 2020, Lecture Notes in Computer Science, 978-3-030-66526-5. ⟨10.1007/978-3-030-66527-2_20⟩ (2020)
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Automated Transliteration of Late Egyptian Using Neural Networks: An Experiment in “Deep Learning”
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In: ISSN: 0942-5659 ; Lingua Aegyptia - Journal of Egyptian Language Studies ; https://hal.archives-ouvertes.fr/hal-03118369 ; Lingua Aegyptia - Journal of Egyptian Language Studies, Widmaier Verlag, 2020, 28, pp.233-257. ⟨10.37011/lingaeg.28.07⟩ (2020)
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Designing an IIR Research Apparatus with Users with Severe Intellectual Disability
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In: ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR) ; https://hal-amu.archives-ouvertes.fr/hal-02470797 ; ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR), Mar 2020, Vancouver, Canada. pp.412-416, ⟨10.1145/3343413.3378008⟩ (2020)
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Using Twitter Streams for Opinion Mining: a case study on Airport Noise
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In: ISSN: 1865-0929 ; Communications in Computer and Information Science ; https://hal.archives-ouvertes.fr/hal-03018998 ; Communications in Computer and Information Science, Springer Verlag, 2020, ⟨10.1007/978-3-030-44900-1_10⟩ (2020)
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