Common algorithms for sentence and word-alignment allow the automatic identification of word translations from paxalhl texts. This study suggests that the identification of word translations should also be possible with non-paxMlel and even unrelated texts. The method proposed is based on the assumption that there is a correlation between the patterns of word cooccurrences in texts of different languages. In a number of recent studies it has been shown that word translations can be automatically derived from the statistical distribution of words in bilingual paxallel texts (e. g. Catizone, Russell & Warwick, 1989; Brown et al., 1990; Dagan, Church & Gale, 1993; Kay & Rbscheisen, 1993). Most of the proposed algorithms first conduct an alignment of sentences, i. e. those palxs of sentences axe located that are translations of each other. In a second step a word alignment is performed by analyzing the correspondences of words in each pair of sentences. The results achieved with these algorithms have been found useful for the compilation of dictionaries, for checking the consistency of terminological usage in translations, and for assisting the terminological work of translators and interpreters. However, despite serious efforts in the compilation of corpora (Church & Mercer, 1993; Armstrong & Thompson, 1995 ) the availability of a large enough paxallel corpus in a specific field and for a given pair of languages will always be the exception, not the rule. Since the acquisition of non-paxallel texts is usually much easier, it would be desirable to have a program that can determine the translations of words from comparable or even unrelated texts. It is assumed that there is a correlation between the co-occurrences of words which are translations of each other. If -for example -in a text of one language two words A and B co-occur more often than expected from chance, then in a text of another language those words which axe translations of A and B should also co-occur more frequently than expected. This assumption is reasonable for parallel texts. However, in this paper it is further assumed that the co-occurrence patterns in original texts axe not fundamentally different from those in translated texts. Starting from an English vocabulary of six words and the corresponding German translations, table la and b show an English and a German co-occurrence mat~x. In these matrices the entries belonging to those pairs of words that in texts co-occur more frequently than expected have been marked with a dot. In general, word order in the lines and columns of a co-occurrence matrix is independent of each other, but for the purpose of this paper can always be assumed to be equal without loss of generality. If now the word order of the English matrix is permuted until the resulting pattern of dots is most similar to that of the German matrix (see table lc), then this increases the likelihood that the English and German words axe in corresponding order. Word n in the English matrix is then the translation of word n in the German matrix. 3 Simulation A simulation experiment was conducted in order to see whether the above assumptions concerning the similarity of co-occurrence patterns actually hold. In this experiment, for an equivalent English and German vocabulary two co-occurrence matrices were computed and then compared. As the English vocabulary a list of 100 words was used, which h~l been suggested by Kent & Rosanoff (1910) for association experiments. The German vocabulary consisted of one by one translations of these words as chosen by Russell (1970) . The word co-occurrences were computed on the basis of an English corpus of 33 and a German corpus of 46 million words. The English corpus consists of 320 Table 1 : When the word orders of the English and the German matrix correspond, the dot patterns of the two matrices are identical.
REF suggested his idea about the usage of context vectors in order to find the words that are the translation of each other in comparable corpora.
Identifying Word Translations In Non-Parallel Texts
Annual Meeting Of The Association For Computational Linguistics
Computer Science