Term-list Translation using Mono-lingual Word Co-occurrence Vectors

A term-list is a list of content words that characterize a consistent text or a concept. This paper presents a new method for translating a term-list by using a corpus in the target language. The method first retrieves alternative translations for each input word from a bilingual dictionary. It then determines the most 'coherent' combination of alternative translations, where the coherence of a set of words is defined as the proximity among multi-dimensional vectors produced from the words on the basis of co-occurrence statistics. The method was applied to term-lists extracted from newspaper articles and achieved 81% translation accuracy for ambiguous words (i.e., words with multiple translations). 1 I n t r o d u c t i o n A list of content words, called a term-list, is widely used as a compact representation of documents in information retrieval and other document processing. Automatic translation of term-lists enables this processing to be cross-linguistic. This paper presents a new method for translating term-lists by using cooccurrence statistics in the target language. Although there is little study on automatic translation of term-lists, related studies are found in the area of target word selection (for content words) in conventional full-text machine translation (MT). Approaches for target word selection can be classified into two types. The first type, which has been adopted in many commercial MT systems, is based on hand assembled disambiguation rules, and/or dictionaries. The problem with this approach is that creating these rules requires much cost and that they are usually domain-dependent 1 The second type, called the statistics-based approach, learns disambiguation knowledge from large corpora. Brown et al. presented an algorithm that * This r e s e a r c h w a s done when the au thor w a s a t Center for the S tudy of Language and Information(CSLI) , Stanford University. 1In fact, this is par t ly shown by the fact tha t many MT s y s t e m s have subs t i tu tab le domain-dependen t (or "user" ) dict ionaries . relies on translation probabilities estimated from large bilingual corpora (Brown et al., 1990)(Brown et al., 1991). Dagan and Itai (1994) and Tanaka and Iwasaki (1996) proposed algorithms for selecting target words by using word co-occurrence statistics in the target language corpora. The latter algorithms using mono-lingual corpora are particularly important because, at present, we cannot always get a sufficient amount of bilingual or parallel corpora. Our method is closely related to (Tanaka and Iwasaki, 1996) from the viewpoint that they both rely on mono-lingual corpora only and do not require any syntactic analysis. The difference is that our method uses "coherence scores", which can capture associative relations between two words which do not co-occur in the training corpus. This paper is organized as follows, Section 2 describes the overall translation process. Section 3 presents a disambiguation algorithm, which is the core part of our translation Inethod. Section 4 and 5 give experimental results and discussion. 2 T e r m l i s t T r a n s l a t i o n Our term-list translation method consists of two steps called Dictionary Lookup and Disambiguation. 1. Dictionary Lookup: For each word in the given term-list, all the alternative translations are retrieved from a bilingual dictionary. A translation candidate is defined as a combination of one translation for each input word. For example, if the input term-list consists of two words, say Wl and w2, and their translation include Wll for Wl and W2a for w2, then (wu, w~a) is a translation candidate. If wl and w2 have two and three alternatives respectively then there are 6 possible translation candidates.