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Your search keyword '"Goenaga, Iakes"' showing total 8 results
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8 results on '"Goenaga, Iakes"'

3. Deep dive machine translation

Author

Skadiņa, Inguna, Vasiḷjevs, Andrejs, Pinnis, Mārcis, Bērziņš, Aivars, Aranberri, Nora, Van den Bogaert, Joachim, O'Connor, Sally, García-Martínez, Mercedes, Goenaga, Iakes, Hajič, Jan, Herranz, Manuel, Lieske, Christian, Popel, Martin, Popović, Maja, Castilho, Sheila, Gaspari, Federico, Rosa, Rudolf, Superbo, Riccardo, Way, Andy, Rehm, Georg, and Way, Andy

Subjects
Artificial intelligence, Translating and interpreting, Language
Abstract

Machine Translation (MT) is one of the oldest language technologies having been researched for more than 70 years. However, it is only during the last decade that it has been widely accepted by the general public, to the point where in many cases it has become an indispensable tool for the global community, supporting communication between nations and lowering language barriers. Still, there remain major gaps in the technology that need addressing before it can be successfully applied in under-resourced settings, can understand context and use world knowledge. This chapter provides an overview of the current state-of-the-art in the field of MT, offers technical and scientific forecasting for 2030, and provides recommendations for the advancement of MT as a critical technology if the goal of digital language equality in Europe is to be achieved.

Published
2023

5. Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages

Author

Seddah, Djamé, Tsarfaty, Reut, Kübler, Sandra, Candito, Marie, Choi, Jinho D., Farkas, Richárd, Foster, Jennifer, Goenaga, Iakes, Gojenola Galletebeitia, Koldo, Goldberg, Yoav, Green, Spence, Habash, Nizar, Kuhlmann, Marco, Maier, Wolfgang, Nivre, Joakim, Przepiórkowski, Adam, Roth, Ryan, Seeker, Wolfgang, Versley, Yannick, Vincze, Veronika, Wolińsk, Marcin, Wróblewska, Alina, Villemonte de La Clergerie, Éric, Analyse Linguistique Profonde à Grande Echelle, Large-scale deep linguistic processing (ALPAGE), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris Diderot - Paris 7 (UPD7), Faculty of Mathematics and Computer Science, Weizmann Institute of Science [Rehovot, Israël], Computational Linguistics, Department of Linguistics (DL), Indiana University [Bloomington], Indiana University System-Indiana University System-Indiana University [Bloomington], Indiana University System-Indiana University System, IPSOFT (Ipsoft), University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS), Department of Computer Algorithms and Artificial Intelligence., University of Szeged [Szeged], National Centre for Language Technology (NCLT), Dublin City University [Dublin] (DCU), Computer Languages and Systems, University of the Basque Country [Bizkaia] (UPV/EHU), Department of Computer Science, Bar-Ilan University [Israël], Stanford NLP Group, Stanford University, Center for Computational Learning Systems (CCLS), Columbia University [New York], Computational Linguistics and Language Technology, Uppsala University, Abteilung für Computerlinguistik [Düsseldorf ], Philosophische Fakultät [Düsseldorf], Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf]-Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf]-Instituts für Sprache und Information [Düsseldorf], Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Institute of Computer Science [Warszawa], Polska Akademia Nauk = Polish Academy of Sciences (PAN), Institut für Maschinelle Sprachverarbeitung, Universität Stuttgart [Stuttgart], Institute for Computational Linguistics, Universität Heidelberg [Heidelberg], Université Paris Diderot - Paris 7 (UPD7)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), and Universität Heidelberg [Heidelberg] = Heidelberg University

Subjects
langues à morphologie riche, evaluation, parsing en dépendance, Statistical Parsing, Morphologically-rich languages, parsing en constituents, non gold scenarios, Parsing statistiques, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], dependency parsing, TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, analyse syntaxique à large couverture, phrase based parsing, treebanking, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, non gold tokenization
Abstract

International audience; This paper reports on the first shared task on statistical parsing of morphologically rich lan- guages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the eval- uation metrics for parsing MRLs given dif- ferent representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios.

Published
2013

6. Determination of Features for a Machine Learning Approach to Pronominal Anaphora Resolution in Basque

Author

Arregi Uriarte, Olatz, Ceberio Berger, Klara, Díaz de Ilarraza Sánchez, Arantza, Goenaga, Iakes, Sierra, Basilio, and Zelaia Jauregi, Ana Victoria

Subjects
Machine learning, Lenguajes y Sistemas Informáticos, Resolución de anáfora, Aprendizaje automático, Anaphora resolution
Abstract

En este trabajo presentamos una primera aproximación basada en el aprendizaje automático para resolver la anáfora pronominal en euskara. Asimismo, determinamos las características más relevantes para esta tarea. In this paper we present the preliminaries for a machine learning approach to resolve the pronominal anaphora in Basque language. In this work we determine the appropriate features to be used in this task. This work was supported by KNOW2 (TIN2009-14715-C04-01) and Berbatek (IE09-262) projects.

Published
2010

8. A Combination of Classifiers for the Pronominal Anaphora Resolution in Basque.

Author

Zelaia Jauregi, Ana, Sierra, Basilio, Arregi Uriarte, Olatz, Ceberio, Klara, Díaz de Illarraza, Arantza, and Goenaga, Iakes

Abstract

In this paper we present a machine learning approach to resolve the pronominal anaphora in Basque language. We consider different classifiers in order to find the system that fits best to the characteristics of the language under examination. We apply the combination of classifiers which improves results obtained with single classifiers. The main contribution of the paper is the use of bagging having as base classifier a non-soft one for the anaphora resolution in Basque. [ABSTRACT FROM AUTHOR]

Published
2010
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