Flow Network Models for Word Alignment and Terminology Extraction from Bilingual Corpora

Flow Network Models for Word Alignment and Terminology Extraction from Bilingual Corpora I~ric Gaussier Xerox Research Centre Europe 6, Chemin de Maupertuis 3S240 Meylan F. Eric.Gaussier~xrce.xerox.com Abstract This paper presents a new model for word align- ments between parallel sentences, which allows one ~o accurately estimate different parameters, in a computationally efficient way. An applica- tion of this model to bilingual terminology ex- traction, where terms are identified in one lan- guage and guessed, through the alignment pro- cess, in the other one, is also described. An ex- periment conducted on a small English-French parallel corpus gave results with high precision, demonstrating the validity of the model. 1 Introduction l?[arly works, (Gale and Church, 1993; Brown et el., 1993), and to a certain extent (Kay and RSscheisen, 1993), presented methods to ex- t,'act b!Ji!!gua! lexicons of words from a parallel corpus, relying on the dist:ribu;ioa of tl:e wo:ds in the set of parallel sentences (or other units). (Brown et el., 1993) then extended their method and established a sound probabilistic model se- ries, relying on different parameters describing how words within parallel sentences are aligned to each other. On the other hand, (Dagan et el., 1993) proposed an algorithm, borrowed to the field of dynamic programming and based on the output of their previous work, to find the best alignment, subject to certain constraints, between words in parallel sentences. A simi- lar algorithm was used by (Vogel et el., 1996). Investigating alignments at the sentence level allows to clean and to refine the leMcons other- wise extracted from a parallel corpus as a whole, pruning what (Melamed, 1996) calls "indirect associations". Now, what differentiates the models and algo- rithms proposed are the sets of parameters and constraints they rely on, their ability to find an appropriate solution under the constraints de- fined and their ability to nicely integrate new parameters. We want to present here a model of the possible alignments in the form of flow net- works. This representation allows to define dif- ferent kinds of alignments and to find the most probable or an approximation of this most prob- able alignment, under certain constraints. Our procedure presents the advantage of an accurate modelling of the possible alignments, and can be used on small corpora. We will introduce this model in the next section. Section 3 describes a particular use of this model to find term trans- lations, and presents the results we obtained for this task on a small corpus. Finally, the main features of our work and the research directions we envisage are summarized in the conlcusion. 2 Alignments and flow networks Let us ~irst consider the following a!igIled sentences, with the actual alignment beween words~: X X Assuming that we have probabilities of associ- ating English and French words, one way to find the preceding alignment is to search for the most 1All the examples consider English and French ;ts tile source and target languages, even though the method we propose is independent of the language …

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