title: Aligning words in French-English non-parallel medical texts: effect of term frequency distributions.
abstract: In this paper, we present a method for aligning words based on a statistical model of word distribution similarity. The basis underlying our method is that there is a correlation between the patterns of word co-occurrences in texts of different languages. Using automatically downloaded pages from different medical web sites and a combined bilingual lexicon of general and medical terms as language sources, a similarity score is assigned to each proposed translated pair of words, based on the distributional contexts of these two words. We vary several parameters of the method. Experimental results confirm a positive effect of frequency, show that medical words are better handled than less specialized words, and do not evidence a clear influence of context window size. Future directions for improvement include working with very large, part-of-speech tagged corpora.
authors.affiliation: STIM:DSI, Assistance Publique-Hopitaux de Paris, 91 boulevard de l'Hôpital, 75634 Paris Cedex 13, France. ycc@biomath.jussieu.fr
authors.forename: Yun-Chuang
authors.initials: YC
authors.lastname: Chiao
authors.forename: Pierre
authors.initials: P
authors.lastname: Zweigenbaum
mesh_terms: D000465:Algorithms; D007802:Language; D019303:Multilingualism; D009323:Natural Language Processing; D009626:Terminology as Topic; D014175:Translating; D018875:Vocabulary, Controlled