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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 4 5...11 Hits 1 – 20 of 218 1 Universal Segmentations 1.0 (UniSegments 1.0) Žabokrtský, Zdeněk; Bafna, Nyati; Bodnár, Jan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022 BASE Show details 2 Investigating alignment interpretability for low-resource NMT Zanon Boito, Marcely; Villavicencio, Aline; Besacier, Laurent In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, ⟨10.1007/s10590-020-09254-w⟩ (2021) BASE Show details 3 Is there a bilingual disadvantage for word segmentation? A computational modeling approach Fibla, Laia; Sebastian-Galles, Nuria; Cristia, Alejandrina In: ISSN: 0305-0009 ; EISSN: 1469-7602 ; Journal of Child Language ; https://hal.archives-ouvertes.fr/hal-03498905 ; Journal of Child Language, Cambridge University Press (CUP), 2021, pp.1-28. ⟨10.1017/S0305000921000568⟩ (2021) BASE Show details 4 SM to: Is there a bilingual disadvantage for word segmentation? A computational modeling approach ... Fibla, Laia. - : Open Science Framework, 2021 BASE Show details 5 Early Tashelhiyt Berber word segmentation: the role of the Possible Word Constraint ... Elouatiq, Abdellah. - : Open Science Framework, 2021 BASE Show details 6 Discovering structure in speech recordings: Unsupervised learning of word and phoneme like units for automatic speech recognition Walter, Oliver. - 2021 In: Fraunhofer IAIS (2021) BASE Show details 7 Handling cross and out-of-domain samples in Thai word segmentation Sarwar, Raheem; Phatthiyaphaibun, Wannaphong; Nutanong, Sarana... In: 1003 ; 1016 (2021) BASE Show details 8 Measuring (online) word segmentation in adults and children Iris Broedelet; Paul Boersma; Judith Rispens In: Dutch Journal of Applied Linguistics, Vol 10 (2021) (2021) BASE Show details 9 Investigating Language Impact in Bilingual Approaches for Computational Language Documentation Zanon Boito, Marcely; Villavicencio, Aline; Besacier, Laurent In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), ; SLTU-CCURL workshop, LREC 2020 ; https://hal.archives-ouvertes.fr/hal-02895907 ; SLTU-CCURL workshop, LREC 2020, May 2020, Marseille, France (2020) BASE Show details 10 F0 Slope and Mean: Cues to Speech Segmentation in French Cordero Rull, Maria del Mar; Meunier, Fanny; Grimault, Nicolas... In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-03042331 ; Interspeech 2020, Oct 2020, Shanghai, China. pp.1610-1614, ⟨10.21437/Interspeech.2020-2509⟩ (2020) BASE Show details 11 The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ... Lavi-Rotbain, Ori; Arnon, Inbal. - : PsychArchives, 2020 BASE Show details 12 Data for: The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ... Lavi-Rotbain, Ori; Arnon, Inbal. - : PsychArchives, 2020 BASE Show details 13 The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ... Lavi-Rotbain, Ori; Arnon, Inbal. - : PsychArchives, 2020 BASE Show details 14 Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech Soderstrom, M; Karadayi, J; Casillas, M. - : Elsevier BV, 2020 BASE Show details 15 Infants Segment Words from Songs—An EEG Study Snijders; Benders; Fikkert In: Brain Sciences ; Volume 10 ; Issue 1 (2020) BASE Show details 16 Not all words are equally acquired: transitional probabilities and instructions affect the electrophysiological correlates of statistical learning Soares, Ana Paula; Gutiérrez-Domínguez, Francisco-Javier; Vasconcelos, Margarida Fátima Gomes. - : Frontiers Media, 2020 BASE Show details 17 Controlling Utterance Length in NMT-based Word Segmentation with Attention Godard, Pierre; Besacier, Laurent; Yvon, François In: International Workshop on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-02343206 ; International Workshop on Spoken Language Translation, Nov 2019, Hong-Kong, China (2019) BASE Show details 18 Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus Cristia, Alejandrina; Dupoux, Emmanuel; Bernstein Ratner, Nan... In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-02274050 ; Open Mind, MIT Press, 2019, 3, pp.13-22. ⟨10.1162/opmi_a_00022⟩ (2019) BASE Show details 19 Unsupervised word discovery for computational language documentation ; Découverte non-supervisée de mots pour outiller la linguistique de terrain Godard, Pierre. - : HAL CCSD, 2019 In: https://tel.archives-ouvertes.fr/tel-02286425 ; Artificial Intelligence [cs.AI]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLS062⟩ (2019) Abstract: Language diversity is under considerable pressure: half of the world’s languages could disappear by the end of this century. This realization has sparked many initiatives in documentary linguistics in the past two decades, and 2019 has been proclaimed the International Year of Indigenous Languages by the United Nations, to raise public awareness of the issue and foster initiatives for language documentation and preservation. Yet documentation and preservation are time-consuming processes, and the supply of field linguists is limited. Consequently, the emerging field of computational language documentation (CLD) seeks to assist linguists in providing them with automatic processing tools. The Breaking the Unwritten Language Barrier (BULB) project, for instance, constitutes one of the efforts defining this new field, bringing together linguists and computer scientists. This thesis examines the particular problem of discovering words in an unsegmented stream of characters, or phonemes, transcribed from speech in a very-low-resource setting. This primarily involves a segmentation procedure, which can also be paired with an alignment procedure when a translation is available. Using two realistic Bantu corpora for language documentation, one in Mboshi (Republic of the Congo) and the other in Myene (Gabon), we benchmark various monolingual and bilingual unsupervised word discovery methods. We then show that using expert knowledge in the Adaptor Grammar framework can vastly improve segmentation results, and we indicate ways to use this framework as a decision tool for the linguist. We also propose a tonal variant for a strong nonparametric Bayesian segmentation algorithm, making use of a modified backoff scheme designed to capture tonal structure. To leverage the weak supervision given by a translation, we finally propose and extend an attention-based neural segmentation method, improving significantly the segmentation performance of an existing bilingual method. ; La diversité linguistique est actuellement menacée : la moitié des langues connues dans le monde pourraient disparaître d'ici la fin du siècle. Cette prise de conscience a inspiré de nombreuses initiatives dans le domaine de la linguistique documentaire au cours des deux dernières décennies, et 2019 a été proclamée Année internationale des langues autochtones par les Nations Unies, pour sensibiliser le public à cette question et encourager les initiatives de documentation et de préservation. Néanmoins, ce travail est coûteux en temps, et le nombre de linguistes de terrain, limité. Par conséquent, le domaine émergent de la documentation linguistique computationnelle (CLD) vise à favoriser le travail des linguistes à l'aide d'outils de traitement automatique. Le projet Breaking the Unwritten Language Barrier (BULB), par exemple, constitue l'un des efforts qui définissent ce nouveau domaine, et réunit des linguistes et des informaticiens. Cette thèse examine le problème particulier de la découverte de mots dans un flot non segmenté de caractères, ou de phonèmes, transcrits à partir du signal de parole dans un contexte de langues très peu dotées. Il s'agit principalement d'une procédure de segmentation, qui peut également être couplée à une procédure d'alignement lorsqu'une traduction est disponible. En utilisant deux corpus en langues bantoues correspondant à un scénario réaliste pour la linguistique documentaire, l'un en Mboshi (République du Congo) et l'autre en Myene (Gabon), nous comparons diverses méthodes monolingues et bilingues de découverte de mots sans supervision. Nous montrons ensuite que l'utilisation de connaissances linguistiques expertes au sein du formalisme des Adaptor Grammars peut grandement améliorer les résultats de la segmentation, et nous indiquons également des façons d'utiliser ce formalisme comme outil de décision pour le linguiste. Nous proposons aussi une variante tonale pour un algorithme de segmentation bayésien non-paramétrique, qui utilise un schéma de repli modifié pour capturer la structure tonale. Pour tirer parti de la supervision faible d'une traduction, nous proposons et étendons, enfin, une méthode de segmentation neuronale basée sur l'attention, et améliorons significativement la performance d'une méthode bilingue existante. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; Alignement bilingue; Apprentissage non-supervisé; Automatic word segmentation; Bayesian models; Bilingual alignment; Langues peu dotées; Low-resource languages; Modèles bayésiens; Segmentation automatique en mots; Unsupervised learning URL: https://tel.archives-ouvertes.fr/tel-02286425/file/75551_GODARD_2019_archivage.pdf https://tel.archives-ouvertes.fr/tel-02286425 https://tel.archives-ouvertes.fr/tel-02286425/document BASE Hide details 20 MiNgMatch—A Fast N-gram Model for Word Segmentation of the Ainu Language Karol Nowakowski; Michal Ptaszynski; and Fumito Masui In: Information ; Volume 10 ; Issue 10 (2019) BASE Show details Page: 1 2 3 4 5...11

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