Combining String and Context Similarity for Bilingual Term Alignment from Comparable Corpora

Automatically compiling bilingual dictio-naries of technical terms from comparable corpora is a challenging problem, yet with many potential applications. In this paper, we exploit two independent observations about term translations: (a) terms are of-ten formed by corresponding sub-lexical units across languages and (b) a term and its translation tend to appear in similar lex-ical context. Based on the first observa-tion, we develop a new character n-gram compositional method, a logistic regres-sion classifier, for learning a string similar-ity measure of term translations. Accord-ing to the second observation, we use an existing context-based approach. For eval-uation, we investigate the performance of compositional and context-based methods on: (a) similar and unrelated languages, (b) corpora of different degree of compa-rability and (c) the translation of frequent and rare terms. Finally, we combine the two translation clues, namely string and contextual similarity, in a linear model and we show substantial improvements over the two translation signals.