Abstract This article presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine transliteration and acquires knowledge iteratively from the Web. We study the unsupervised learning and the active learning strategies that minimize human supervision in terms of data labeling. The learning process refines the PSM and constructs a transliteration lexicon at the same time. We evaluate the proposed PSM and its learning algorithm through a series of systematic experiments, which show that the proposed framework is reliably effective on two independent databases.

This article proposes an approach for the automatic extraction of transliteration pairs from Chinese Web corpora. In this approach, we formulate the machine transliteration process using a syllable-based phonetic similarity model which consists of phonetic confusion matrices and a Chinese character n -gram language model. With the phonetic similarity model, the extraction of transliteration pairs becomes a two-step process of recognition followed by validation: First, in the recognition process, we identify the most probable transliteration in the k -neighborhood of a recognized English word. Then, in the validation process, we qualify the transliteration pair candidates with a hypothesis test. We carry out an analytical study on the statistics of several key factors in English-Chinese transliteration to help formulate phonetic similarity modeling. We then conduct both supervised and unsupervised learning of a phonetic similarity model on a development database. The experimental results validate the effectiveness of the phonetic similarity model by achieving an F -measure of 0.739 in supervised learning. The unsupervised learning approach works almost as well as the supervised one, thus allowing us to deploy automatic extraction of transliteration pairs in the Web space.

This paper presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine transliteration, and acquires knowledge iteratively from the Web. We study the active learning and the unsupervised learning strategies that minimize human supervision in terms of data labeling. The learning process refines the PSM and constructs a transliteration lexicon at the same time. We evaluate the proposed PSM and its learning algorithm through a series of systematic experiments, which show that the proposed framework is reliably effective on two independent databases.