Abstract
Query translation mining is a key technique in cross-language information retrieval and machine translation knowl-edge acquisition. For better performance, the queries are classified into transliterated words and non-transliterated words based on transliterated word identification model, and are further channeled to different mining processes. This paper is a pilot study on query classification for better translation mining performance, which is based on supervised classification and linguistic heuristics. The person name identification gets a precision of over 97%. Transliterated word translation mining shows satisfactory performance.
Highlights
For both cross-language information retrieval and machine translation knowledge acquisition, translation mining for out-of-vocabulary words is an important module which can help translate named-entities, organization and location names, book and movie titles, technical terms, and newly-coined words that are not included in the dictionary.The web is a rich mineral for translation mining based on co-occurrence statistics
Query translation mining is a key technique in cross-language information retrieval and machine translation knowledge acquisition
Result analysis shows that transliteration characters feature is a good feature for transliterated word identification, which can serve as a basis for transliteration word identification
Summary
For both cross-language information retrieval and machine translation knowledge acquisition, translation mining for out-of-vocabulary words is an important module which can help translate named-entities, organization and location names, book and movie titles, technical terms, and newly-coined words that are not included in the dictionary. Besides the co-occurrence statistics, natural language processing techniques such as word alignment is utilized in recent research work. All query terms go through the same process for translation mining, which omits the difference between transliterated words and non-transliterated words. In present researches on query translation mining, transliterated words are not separated from non-transliterated words. This method leads to a compromised solution in the modeling. A method is proposed in this paper to decide whether the query word is a transliterated word or not, which utilizes a unigram-based transliteration statistics plus some heuristic rules. The section of the paper describes the unigrambased transliteration identification modeling based on a supervised-learning process. The last section concludes the method and describes future works in the field
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