A Statistical Word-Level Translation Model for Comparable Corpora

In this paper, we present a model of statistical word-level mapping for comparable corpora. The approach is based on the assumption that if two terms have close distributional profiles, their corresponding translations' distributional profiles should be close in a comparable corpus. The proposed model is described. A preliminary investigation on intralanguage comparable corpora is laid out. The preliminary results are >92% accurate, suggesting the feasibility of the model. The model needs to undergo some improvements and should be tested cross linguistically before assessing its significance. (Also cross-referenced as UMIACS-TR-2000-41, LAMP-TR-048

Contrastive Approach towards Text Source Classification based on Top-Bag-Word Similarity

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

METRICC: Harnessing Comparable Corpora for Multilingual Lexicon Development

International audienceResearch on comparable corpora has grown in recent years bringing about the possibility of developing multilingual lexicons through the exploitation of comparable corpora to create corpus-driven multilingual dictionaries. To date, this issue has not been widely addressed. This paper focuses on the use of the mechanism of collocational networks proposed by Williams (1998) for exploiting comparable corpora. The paper first provides a description of the METRICC project, which is aimed at the automatically creation of comparable corpora and describes one of the crawlers developed for comparable corpora building, and then discusses the power of collocational networks for multilingual corpus-driven dictionary development

Filtering parallel texts to improve translation model and cross-language information retrieval

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal