Abstract
Terms, such as compound nouns, named entities, acronyms, and other noun phrases, make up a bulk of documents. “Term translation,” a term description rendered in an alternative language with its meaning or what it sounds like (which is also called transliteration), plays an important role in lexicon construction, machine translation (MT), cross-language information retrieval (CLIR), and other natural language processing applications. However, with the advent of globalization and technology, many new terms are created and usually become out of vocabulary (OOV). In addition, the translations of a term often vary in different domains. Term translation, therefore, is difficult to handle via simple dictionary lookup, and presents a serious problem for such tasks as MT and CLIR. In this thesis, we present novel methods for learning to find translations of a given term on the Web. The methods involve two processing parts: during the training stage, we use a bilingual term list to learn source-target surface patterns, morpheme relations, and domain-specific knowledge query expansion terms for collecting more mixed-code data containing relevant translations. At run time, the proposed methods automatically transform the given term into expanded queries aimed at maximizing the probability of retrieving appropriate translations including transliterations or domain-specific translations from a very large collection of mixed-code documents via a Web search engine. Then, the methods extract translation candidates from retrieved snippets of the results of submitting the queries, and finally rank the candidates. We present an implementation of a prototype system, TermMine, which applies the methods to find appropriate translations of a given term. Evaluation on a set of experiments shows that the proposed methods can achieve high precision and recall, and outperform existing state-of-the-art machine translation systems.