A Class-based Approach to Word Alignment

This paper presents an algorithm capable of identifying the translation for each word in a bilingual corpus. Previously proposed methods rely heavily on word-based statistics. Under a word-based approach, frequent words with a consistent translation can be aligned at a high rate of precision. However, words that are less frequent or exhibit diverse translations generally do not have statistically significant evidence for confident alignment, thereby leading to incomplete or incorrect alignments. The algorithm proposed herein attempts to broaden coverage by exploiting lexicographic resources. To this end, we draw on the two classification systems of words in Longman Lexicon of Contemporary English (LLOCE) and Tongyici Cilin (Synonym Forest, CILIN). Automatically acquired class-based alignment rules are used to compensate for what is lacking in a bilingual dictionary such as the English-Chinese version of the Longman Dictionary of Contemporary English (LecDOCE). In addition, this alignment method is implemented using LecDOCE examples and their translations for training and testing, while further examples from a technical manual in both English and Chinese are used for an open test. Quantitative results of the closed and open tests are also summarized.