Building Web Corpora for Minority Languages

Web corpora creation for minority languages that do not have their own top-level Internet domain is no trivial matter. Web pages in such minority languages often contain text and links to pages in the dominant language of the country. When building corpora in specific languages, one has to decide how and at which stage to make sure the texts gathered are in the desired language. In the {``}Finno-Ugric Languages and the Internet{''} (Suki) project, we created web corpora for Uralic minority languages using web crawling combined with a language identification system in order to identify the language while crawling. In addition, we used language set identification and crowdsourcing before making sentence corpora out of the downloaded texts. In this article, we describe a strategy for collecting textual material from the Internet for minority languages. The strategy is based on the experiences we gained during the Suki project.Peer reviewe

Web Language Identification Testing Tool

Nowadays a variety of tools for automatic language identification are available. Regardless of the approach used, at least two features can be identified as crucial to evaluate the performances of such tools: the precision of the presented results and the range of languages that can be detected. In this work we shall focus on a subtask of written language identification that is important to preserve and enhance multilinguality in the Web, i.e. detecting the language of a Web page given its URL. Most specifically, the final aim is to verify to which extent under-represented languages are recognized by available tools. The main specificity of Web Language Identification (WLI) lies in the fact that often an HTML page can provide interesting extralinguistic clues (URL domain name, metadata, encoding, etc) that can enhance accuracy. We shall first provide some data and statistics on the presence of languages on the web, secondly discuss existing practices and tools for language identification according to different metrics - for instance the approaches used and the number of supported languages - and finally make some proposals on how to improve current Web Language Identifiers. We shall also present a preliminary WLI service that builds on the Google Chromium Compact Language Detector; the WLI tool allows us to test the Google n-gram based algorithm against an adhoc gold standard of pages in various languages. The gold standard, based on a selection of Wikipedia projects, contains samples in languages for which no automatic recognition has been attempted; it can thus be used by specialists to develop and evaluate WLI systems

Babylon parallel text builder: Gathering parallel texts for low-density languages

This paper describes BABYLON, a system that attempts to overcome the shortage of parallel texts in low-density languages by supplementing existing parallel texts with texts gathered automatically from the Web. In addition to the identification of entire Web pages, we also propose a new feature specifically designed to find parallel text chunks within a single document. Experiments carried out on the Quechua-Spanish language pair show that the system is successful in automatically identifying a significant amount of parallel texts on the Web. Evaluations of a machine translation system trained on this corpus indicate that the Web-gathered parallel texts can supplement manually compiled parallel texts and perform significantly better than the manually compiled texts when tested on other Web-gathered data. 1

A hybrid approach for transliterated word-level language identification: CRF with post processing heuristics

© {Owner/Author | ACM} {Year}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation, http://dx.doi.org/10.1145/2824864.2824876[EN] In this paper, we describe a hybrid approach for word-level language (WLL) identification of Bangla words written in Roman script and mixed with English words as part of our participation in the shared task on transliterated search at Forum for Information Retrieval Evaluation (FIRE) in 2014. A CRF based machine learning model and post-processing heuristics are employed for the WLL identification task. In addition to language identification, two transliteration systems were built to transliterate detected Bangla words written in Roman script into native Bangla script. The system demonstrated an overall token level language identification accuracy of 0.905. The token level Bangla and English language identification F-scores are 0.899, 0.920 respectively. The two transliteration systems achieved accuracies of 0.062 and 0.037. The word-level language identification system presented in this paper resulted in the best scores across almost all metrics among all the participating systems for the Bangla-English language pair.We acknowledge the support of the Department of Electronics and Information Technology (DeitY), Government of India, through the project “CLIA System Phase II”. The research work of the last author was carried out in the framework of WIQ-EI IRSES (Grant No. 269180) within the FP 7 Marie Curie, DIANA-APPLICATIONS (TIN2012-38603-C02-01) projects and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Banerjee, S.; Kuila, A.; Roy, A.; Naskar, SK.; Rosso, P.; Bandyopadhyay, S. (2014). A hybrid approach for transliterated word-level language identification: CRF with post processing heuristics. 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Springer International Publishing, 2014.U. Barman, J. Wagner, G. Chrupala, and J. Foster. Identification of languages and encodings in a multilingual document. page 127. EMNLP, 2014.K. R. Beesley. Language identifier: A computer program for automatic natural-language identification of on-line text. pages 47--54. ATA, 1988.P. F. Brown, S. A. D. Pietra, V. J. D. Pietra, and R. L. Mercer. Mercer: The mathematics of statistical machine translation: parameter estimation. pages 263--311. Computational Linguistics, 1993.M. Carpuat. Mixed-language and code-switching in the canadian hansard. page 107. EMNLP, 2014.G. Chittaranjan, Y. Vyas, K. Bali, and M. Choudhury. Word-level language identification using crf: Code-switching shared task report of msr india system. pages 73--79. EMNLP, 2014.A. Das, A. Ekbal, T. Mandal, and S. Bandyopadhyay. English to hindi machine transliteration system at news. pages 80--83. Proceeding of the Named Entities Workshop ACL-IJCNLP, Singapore, 2009.A. Ekbal, S. Naskar, and S. Bandyopadhyay. A modified joint source channel model for transliteration. pages 191--198. COLING-ACL Australia, 2006.I. Goto, N. Kato, N. Uratani, and T. Ehara. Transliteration considering context information based on the maximum entropy method. pages 125--132. MT-Summit IX, New Orleans, USA, 2003.R. Haque, S. Dandapat, A. K. Srivastava, S. K. Naskar, and A. Way. English to hindi transliteration using context-informed pb-smt:the dcu system for news 2009. NEWS 2009, 2009.S. Y. Jung, S. Hong, and E. Paek. An english to korean transliteration model of extended markov window.S. Y. Jung, S. L. Hong, and E. Paek. An english to korean transliteration model of extended markov window. pages 383--389. COLING, 2000.B. J. Kang and K. S. Choi. Automatic transliteration and back-transliteration by decision tree learning. LERC, May 2000.B. King and S. Abney. Labeling the languages of words in mixed-language documents using weakly supervised methods. pages 1110--1119. 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Parallel Strands: A Preliminary Investigation into Mining the Web for Bilingual Text

Parallel corpora are a valuable resource for machine translation, but at present their availability and utility is limited by genre- and domain-specificity, licensing restrictions, and the basic difficulty of locating parallel texts in all but the most dominant of the world's languages. A parallel corpus resource not yet explored is the World Wide Web, which hosts an abundance of pages in parallel translation, offering a potential solution to some of these problems and unique opportunities of its own. This paper presents the necessary first step in that exploration: a method for automatically finding parallel translated documents on the Web. The technique is conceptually simple, fully language independent, and scalable, and preliminary evaluation results indicate that the method may be accurate enough to apply without human intervention.Comment: LaTeX2e, 11 pages, 7 eps figures; uses psfig, llncs.cls, theapa.sty. An Appendix at http://umiacs.umd.edu/~resnik/amta98/amta98_appendix.html contains test dat