A good lexicon is an important resource for various cross-lingual tasks such as information retrieval and text mining. In this paper, we focus on extracting translation pairs from non-parallel cross-lingual corpora. Previous lexicon extraction algorithms for non-parallel data generally rely on an accurate seed dictionary and extract translation pairs by using context similarity. However, there are two problems. One, a lot of semantic information is lost if we just use seed dictionary words to construct context vectors and obtain the context similarity. Two, in practice, we may not have a clean seed dictionary. For example, if we use a generic dictionary as a seed dictionary in a special domain, it might be very noisy. To solve these two problems, we propose two new bilingual topic models to better capture the semantic information of each word while discriminating the multiple translations in a noisy seed dictionary. We then use an effective measure to evaluate the similarity of words in different languages and select the optimal translation pairs. Results of experiments using real Japanese-English data demonstrate the effectiveness of our models.

本文提出了两种新的双语主题模型，以更好地捕捉每个单词的语义信息，同时区分嘈杂的种子词典中的多个翻译，解决了应用主题模型到非平行数据的两个挑战：难以建模词语关系和嘈杂的种子词典。此外，我们还提出了有效的词语相似性评估措施和翻译选择概率。实验结果表明了所提出的模型的实用性和有效性。

逆向双语主题模型用于从非平行数据中提取词库