Workshop on Natural Language Processing Methods and Corpora in Translation, Lexicography, and Language Learning 2009
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Preparation and Exploitation of Bilingual Texts Dusko Vitas, Cvetana Krstev, Eric Laporte
Preparation and exploitation of bilingual texts Dusko Vitas, Cvetana Krstev, Eric Laporte To cite this version: Dusko Vitas, Cvetana Krstev, Eric Laporte. Preparation and exploitation of bilingual texts. Lux Coreana, 2006, 1, pp.110-132. hal-00190958v2 HAL Id: hal-00190958 https://hal.archives-ouvertes.fr/hal-00190958v2 Submitted on 27 Nov 2007 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Preparation and exploitation of bilingual texts Duško Vitas Faculty of Mathematics Studentski trg 16, CS-11000 Belgrade, Serbia Cvetana Krstev Faculty of Philology Studentski trg 3, CS-11000 Belgrade, Serbia Éric Laporte Institut Gaspard-Monge, Université de Marne-la-Vallée 5, bd Descartes, 77454 Marne-la-Vallée CEDEX 2, France Introduction A bitext is a merged document composed of two versions of a given text, usually in two different languages. An aligned bitext is produced by an alignment tool or aligner, that automatically aligns or matches the versions of the same text, generally sentence by sentence. A multilingual aligned corpus or collection of aligned bitexts, when consulted with a search tool, can be extremely useful for translation, language teaching and the investigation of literary text (Veronis, 2000). -
The Iafor European Conference Series 2014 Ece2014 Ecll2014 Ectc2014 Official Conference Proceedings ISSN: 2188-1138
the iafor european conference series 2014 ece2014 ecll2014 ectc2014 Official Conference Proceedings ISSN: 2188-1138 “To Open Minds, To Educate Intelligence, To Inform Decisions” The International Academic Forum provides new perspectives to the thought-leaders and decision-makers of today and tomorrow by offering constructive environments for dialogue and interchange at the intersections of nation, culture, and discipline. Headquartered in Nagoya, Japan, and registered as a Non-Profit Organization 一般社( 団法人) , IAFOR is an independent think tank committed to the deeper understanding of contemporary geo-political transformation, particularly in the Asia Pacific Region. INTERNATIONAL INTERCULTURAL INTERDISCIPLINARY iafor The Executive Council of the International Advisory Board IAB Chair: Professor Stuart D.B. Picken IAB Vice-Chair: Professor Jerry Platt Mr Mitsumasa Aoyama Professor June Henton Professor Frank S. Ravitch Director, The Yufuku Gallery, Tokyo, Japan Dean, College of Human Sciences, Auburn University, Professor of Law & Walter H. Stowers Chair in Law USA and Religion, Michigan State University College of Law Professor David N Aspin Professor Emeritus and Former Dean of the Faculty of Professor Michael Hudson Professor Richard Roth Education, Monash University, Australia President of The Institute for the Study of Long-Term Senior Associate Dean, Medill School of Journalism, Visiting Fellow, St Edmund’s College, Cambridge Economic Trends (ISLET) Northwestern University, Qatar University, UK Distinguished Research Professor of Economics, -
Champollion: a Robust Parallel Text Sentence Aligner
Champollion: A Robust Parallel Text Sentence Aligner Xiaoyi Ma Linguistic Data Consortium 3600 Market St. Suite 810 Philadelphia, PA 19104 [email protected] Abstract This paper describes Champollion, a lexicon-based sentence aligner designed for robust alignment of potential noisy parallel text. Champollion increases the robustness of the alignment by assigning greater weights to less frequent translated words. Experiments on a manually aligned Chinese – English parallel corpus show that Champollion achieves high precision and recall on noisy data. Champollion can be easily ported to new language pairs. It’s freely available to the public. framework to find the maximum likelihood alignment of 1. Introduction sentences. Parallel text is a very valuable resource for a number The length based approach works remarkably well on of natural language processing tasks, including machine language pairs with high length correlation, such as translation (Brown et al. 1993; Vogel and Tribble 2002; French and English. Its performance degrades quickly, Yamada and Knight 2001;), cross language information however, when the length correlation breaks down, such retrieval, and word disambiguation. as in the case of Chinese and English. Parallel text provides the maximum utility when it is Even with language pairs with high length correlation, sentence aligned. The sentence alignment process maps the Gale-Church algorithm may fail at regions that contain sentences in the source text to their translation. The labor many sentences with similar length. A number of intensive and time consuming nature of manual sentence algorithms, such as (Wu 1994), try to overcome the alignment makes large parallel text corpus development weaknesses of length based approaches by utilizing lexical difficult. -
Kernerman Kdictionaries.Com/Kdn DICTIONARY News the European Network of E-Lexicography (Enel) Tanneke Schoonheim
Number 22 ● July 2014 Kernerman kdictionaries.com/kdn DICTIONARY News The European Network of e-Lexicography (ENeL) Tanneke Schoonheim On October 11th 2013, the kick-off meeting of the European production and reception of dictionaries. The internet offers Network of e-Lexicography (ENeL) project took place in entirely new possibilities for developing and presenting Brussels. This meeting was the outcome of an idea ventilated dictionary information, such as with the integration of sound, a year and a half earlier, in March 2012 in Berlin, at the maps or video, and various novel ways of interacting with European Workshop on Future Standards in Lexicography. dictionary users. For editors of scholarly dictionaries the new The workshop participants then confirmed the imperative to medium is not only a source of inspiration, it also generates coordinate and harmonise research in the field of (electronic) new and serious challenges that demand cooperation and lexicography across Europe, namely to share expertise relating standardization on various levels: to standards, discuss new methodologies in lexicography that a. Through the internet scholarly dictionaries can potentially fully exploit the possibilities of the digital medium, reflect on reach large audiences. However, at present scholarly the pan-European nature of the languages of Europe and attain dictionaries providing reliable information are often not easy a wider audience. to find and are hard to decode for a non-academic audience; A proposal was written by a team of researchers from -
TANGO: Bilingual Collocational Concordancer
TANGO: Bilingual Collocational Concordancer Jia-Yan Jian Yu-Chia Chang Jason S. Chang Department of Computer Inst. of Information Department of Computer Science System and Applictaion Science National Tsing Hua National Tsing Hua National Tsing Hua University University University 101, Kuangfu Road, 101, Kuangfu Road, 101, Kuangfu Road, Hsinchu, Taiwan Hsinchu, Taiwan Hsinchu, Taiwan [email protected] [email protected] [email protected] du.tw on elaborated statistical calculation. Moreover, log Abstract likelihood ratios are regarded as a more effective In this paper, we describe TANGO as a method to identify collocations especially when the collocational concordancer for looking up occurrence count is very low (Dunning, 1993). collocations. The system was designed to Smadja’s XTRACT is the pioneering work on answer user’s query of bilingual collocational extracting collocation types. XTRACT employed usage for nouns, verbs and adjectives. We first three different statistical measures related to how obtained collocations from the large associated a pair to be collocation type. It is monolingual British National Corpus (BNC). complicated to set different thresholds for each Subsequently, we identified collocation statistical measure. We decided to research and instances and translation counterparts in the develop a new and simple method to extract bilingual corpus such as Sinorama Parallel monolingual collocations. Corpus (SPC) by exploiting the word- We also provide a web-based user interface alignment technique. The main goal of the capable of searching those collocations and its concordancer is to provide the user with a usage. The concordancer supports language reference tools for correct collocation use so learners to acquire the usage of collocation. -
Aconcorde: Towards a Proper Concordance for Arabic
aConCorde: towards a proper concordance for Arabic Andrew Roberts, Dr Latifa Al-Sulaiti and Eric Atwell School of Computing University of Leeds LS2 9JT United Kingdom {andyr,latifa,eric}@comp.leeds.ac.uk July 12, 2005 Abstract Arabic corpus linguistics is currently enjoying a surge in activity. As the growth in the number of available Arabic corpora continues, there is an increased need for robust tools that can process this data, whether it be for research or teaching. One such tool that is useful for both groups is the concordancer — a simple tool for displaying a specified target word in its context. However, obtaining one that can reliably cope with the Arabic lan- guage had proved extremely difficult. Therefore, aConCorde was created to provide such a tool to the community. 1 Introduction A concordancer is a simple tool for summarising the contents of corpora based on words of interest to the user. Otherwise, manually navigating through (of- ten very large) corpora would be a long and tedious task. Concordancers are therefore extremely useful as a time-saving tool, but also much more. By isolat- ing keywords in their contexts, linguists can use concordance output to under- stand the behaviour of interesting words. Lexicographers can use the evidence to find if a word has multiple senses, and also towards defining their meaning. There is also much research and discussion about how concordance tools can be beneficial for data-driven language learning (Johns, 1990). A number of studies have demonstrated that providing access to corpora and concordancers benefited students learning a second language. -
The Web As a Parallel Corpus
The Web as a Parallel Corpus Philip Resnik∗ Noah A. Smith† University of Maryland Johns Hopkins University Parallel corpora have become an essential resource for work in multilingual natural language processing. In this article, we report on our work using the STRAND system for mining parallel text on the World Wide Web, first reviewing the original algorithm and results and then presenting a set of significant enhancements. These enhancements include the use of supervised learning based on structural features of documents to improve classification performance, a new content- based measure of translational equivalence, and adaptation of the system to take advantage of the Internet Archive for mining parallel text from the Web on a large scale. Finally, the value of these techniques is demonstrated in the construction of a significant parallel corpus for a low-density language pair. 1. Introduction Parallel corpora—bodies of text in parallel translation, also known as bitexts—have taken on an important role in machine translation and multilingual natural language processing. They represent resources for automatic lexical acquisition (e.g., Gale and Church 1991; Melamed 1997), they provide indispensable training data for statistical translation models (e.g., Brown et al. 1990; Melamed 2000; Och and Ney 2002), and they can provide the connection between vocabularies in cross-language information retrieval (e.g., Davis and Dunning 1995; Landauer and Littman 1990; see also Oard 1997). More recently, researchers at Johns Hopkins University and the -
Resourcing Machine Translation with Parallel Treebanks John Tinsley
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by DCU Online Research Access Service Resourcing Machine Translation with Parallel Treebanks John Tinsley A dissertation submitted in fulfilment of the requirements for the award of Doctor of Philosophy (Ph.D.) to the Dublin City University School of Computing Supervisor: Prof. Andy Way December 2009 I hereby certify that this material, which I now submit for assessment on the programme of study leading to the award of Ph.D. is entirely my own work, that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge breach any law of copyright, and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of my work. Signed: (Candidate) ID No.: Date: Contents Abstract vii Acknowledgements viii List of Figures ix List of Tables x 1 Introduction 1 2 Background and the Current State-of-the-Art 7 2.1 ParallelTreebanks ............................ 7 2.1.1 Sub-sentential Alignment . 9 2.1.2 Automatic Approaches to Tree Alignment . 12 2.2 Phrase-Based Statistical Machine Translation . ...... 14 2.2.1 WordAlignment ......................... 17 2.2.2 Phrase Extraction and Translation Models . 18 2.2.3 ScoringandtheLog-LinearModel . 22 2.2.4 LanguageModelling . 25 2.2.5 Decoding ............................. 27 2.3 Syntax-Based Machine Translation . 29 2.3.1 StatisticalTransfer-BasedMT . 30 2.3.2 Data-OrientedTranslation . 33 2.3.3 OtherApproaches ........................ 35 2.4 MTEvaluation............................. -
Panacea D6.1
SEVENTH FRAMEWORK PROGRAMME THEME 3 Information and communication Technologies PANACEA Project Grant Agreement no.: 248064 Platform for Automatic, Normalized Annotation and Cost-Effective Acquisition of Language Resources for Human Language Technologies D6.1 Technologies and Tools for Lexical Acquisition Dissemination Level: Public Delivery Date: July 16th 2010 Status – Version: Final Author(s) and Affiliation: Laura Rimell (UCAM), Anna Korhonen (UCAM), Valeria Quochi (ILC-CNR), Núria Bel (UPF), Tommaso Caselli (ILC-CNR), Prokopis Prokopidis (ILSP), Maria Gavrilidou (ILSP), Thierry Poibeau (UCAM), Muntsa Padró (UPF), Eva Revilla (UPF), Monica Monachini(CNR-ILC), Maurizio Tesconi (CNR-IIT), Matteo Abrate (CNR-IIT) and Clara Bacciu (CNR-IIT) D6.1 Technologies and Tools for Lexical Acquisition This document is part of technical documentation generated in the PANACEA Project, Platform for Automatic, Normalized Annotation and Cost-Effective Acquisition (Grant Agreement no. 248064). This documented is licensed under a Creative Commons Attribution 3.0 Spain License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/es/. Please send feedback and questions on this document to: [email protected] TRL Group (Tecnologies dels Recursos Lingüístics), Institut Universitari de Lingüística Aplicada, Universitat Pompeu Fabra (IULA-UPF) D6.1 – Technologies and Tools for Lexical Acquisition Table of contents Table of contents .......................................................................................................................... -
Translate's Localization Guide
Translate’s Localization Guide Release 0.9.0 Translate Jun 26, 2020 Contents 1 Localisation Guide 1 2 Glossary 191 3 Language Information 195 i ii CHAPTER 1 Localisation Guide The general aim of this document is not to replace other well written works but to draw them together. So for instance the section on projects contains information that should help you get started and point you to the documents that are often hard to find. The section of translation should provide a general enough overview of common mistakes and pitfalls. We have found the localisation community very fragmented and hope that through this document we can bring people together and unify information that is out there but in many many different places. The one section that we feel is unique is the guide to developers – they make assumptions about localisation without fully understanding the implications, we complain but honestly there is not one place that can help give a developer and overview of what is needed from them, we hope that the developer section goes a long way to solving that issue. 1.1 Purpose The purpose of this document is to provide one reference for localisers. You will find lots of information on localising and packaging on the web but not a single resource that can guide you. Most of the information is also domain specific ie it addresses KDE, Mozilla, etc. We hope that this is more general. This document also goes beyond the technical aspects of localisation which seems to be the domain of other lo- calisation documents. -
Towards Controlled Counterfactual Generation for Text
The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text Nishtha Madaan, Inkit Padhi, Naveen Panwar, Diptikalyan Saha 1IBM Research AI fnishthamadaan, naveen.panwar, [email protected], [email protected] Abstract diversity will ensure high coverage of the input space de- fined by the goal. In this paper, we aim to generate such Machine Learning has seen tremendous growth recently, counterfactual text samples which are also effective in find- which has led to a larger adoption of ML systems for ed- ing test-failures (i.e. label flips for NLP classifiers). ucational assessments, credit risk, healthcare, employment, criminal justice, to name a few. Trustworthiness of ML and Recent years have seen a tremendous increase in the work NLP systems is a crucial aspect and requires guarantee that on fairness testing (Ma, Wang, and Liu 2020; Holstein et al. the decisions they make are fair and robust. Aligned with 2019) which are capable of generating a large number of this, we propose a framework GYC, to generate a set of coun- test-cases that capture the model’s ability to misclassify or terfactual text samples, which are crucial for testing these remove unwanted bias around specific protected attributes ML systems. Our main contributions include a) We introduce (Huang et al. 2019), (Garg et al. 2019). This is not only lim- GYC, a framework to generate counterfactual samples such ited to fairness but the community has seen great interest that the generation is plausible, diverse, goal-oriented and ef- in building robust models susceptible to adversarial changes fective, b) We generate counterfactual samples, that can direct (Goodfellow, Shlens, and Szegedy 2014; Michel et al. -
Natural Language Processing Security- and Defense-Related Lessons Learned
July 2021 Perspective EXPERT INSIGHTS ON A TIMELY POLICY ISSUE PETER SCHIRMER, AMBER JAYCOCKS, SEAN MANN, WILLIAM MARCELLINO, LUKE J. MATTHEWS, JOHN DAVID PARSONS, DAVID SCHULKER Natural Language Processing Security- and Defense-Related Lessons Learned his Perspective offers a collection of lessons learned from RAND Corporation projects that employed natural language processing (NLP) tools and methods. It is written as a reference document for the practitioner Tand is not intended to be a primer on concepts, algorithms, or applications, nor does it purport to be a systematic inventory of all lessons relevant to NLP or data analytics. It is based on a convenience sample of NLP practitioners who spend or spent a majority of their time at RAND working on projects related to national defense, national intelligence, international security, or homeland security; thus, the lessons learned are drawn largely from projects in these areas. Although few of the lessons are applicable exclusively to the U.S. Department of Defense (DoD) and its NLP tasks, many may prove particularly salient for DoD, because its terminology is very domain-specific and full of jargon, much of its data are classified or sensitive, its computing environment is more restricted, and its information systems are gen- erally not designed to support large-scale analysis. This Perspective addresses each C O R P O R A T I O N of these issues and many more. The presentation prioritizes • identifying studies conducting health service readability over literary grace. research and primary care research that were sup- We use NLP as an umbrella term for the range of tools ported by federal agencies.