We present a method for performing machine transliteration without any parallel resources. We frame the transliteration task as a decipherment problem and show that it is possible to learn cross-language phoneme mapping tables using only monolingual resources. We compare various methods and evaluate their accuracies on a standard name transliteration task. 1

We present a novel method for discovering and modeling the relationship between informal Chinese expressions (including colloquialisms and instant-messaging slang) and their formal equivalents. Specifically, we proposed a bootstrapping procedure to identify a list of candidate informal phrases in web corpora. Given an informal phrase, we retrieve contextual instances from the web using a search engine, generate hypotheses of formal equivalents via this data, and rank the hypotheses using a conditional log-linear model. In the log-linear model, we incorporate as feature functions both rule-based intuitions and data co-occurrence phenomena (either as an explicit or indirect definition, or through formal/informal usages occurring in free variation in a discourse). We test our system on manually collected test examples, and find that the (formal-informal) relationship discovery and extraction process using our method achieves an average 1-best precision of 62%. Given the ubiquity of informal conversational style on the internet, this work has clear applications for text normalization in text-processing systems including machine translation aspiring to broad coverage. 1

We introduce a method for learning to find domain-specific translations for a given term on the Web. In our approach, the source term is transformed into an expanded query aimed at maximizing the probability of retrieving translations from a very large collection of mixed-code documents. The method involves automatically generating sets of targetlanguage words from training data in specific domains, automatically selecting target words for effectiveness in retrieving documents containing the sought-after translations. At run time, the given term is transformed into an expanded query and submitted to a search engine, and ranked translations are extracted from the document snippets returned by the search engine. We present a prototype, TermMine, which applies the method to a Web search engine. Evaluations over a set of domains and terms show that TermMine outperforms state-of-the-art machine translation systems. 1

This paper focuses on the Web-based English-Chinese OOV term translation pattern, and emphasizes particularly on the translation selection strategy based on the fusion of multiple features and the ranking mechanism based on Ranking Support Vector Machine (Ranking SVM). By utilizing the CoNLL2003 corpus for the English Named Entity Recognition (NER) task and selected new terms, the experiments based on different data sources show the consistent results. Our OOV term translation model can “filter ” the most possible translation candidates with better ability. From the experimental results for combining our OOV term translation model with English-Chinese Cross-Language Information Retrieval (CLIR) on the data sets of Text Retrieval Evaluation Conference (TREC), it can be found that the obvious performance improvement for both query translation and retrieval can also be obtained. 1