基于高斯混合模型的生物医学领域双语句子对

双语术语词典在生物医学跨语言检索系统中有着非常重要的地位,而双语句子对齐是构建双语词典的第一步工作。为了构想面向生物医学领域的双语词典,该文将分类思想和迁移学习方法引入汉英句子对齐任务中,将句子对齐任务看成一个多类分类任务,考虑生物医学领域双语摘要的锚信息,利用高斯混合模型完成分类目标。同时,在模型训练过程中,该文引入了迁移学习的思想,结合无噪音的《新概念英语》双语语料对模型的句子长度特征进行训练,使得模型在测试语料上句子对齐的正确率得到较大提高。

Sentence Alignment for Biomedicine Texts Based on Gaussian Mixture Model

A bilingual lexicon of biomedical terms plays an important role in biomedical cross-language information retrieval. Sentence alignment is the first step to build a bilingual lexicon. The Gaussian mixture model and transfer learning are applied to align sentences. The basic idea is to consider the sentence alignment as a classification task, which can be solved by the Gaussian mixture model classifiers based on the anchor information included in medical literature abstracts. At the same time, the sentence alignment model is built by combining biomedicine literature abstracts with New Concept English corpora, and it aims at applying transfer learning to train the length features and transfer them to the model. The experiments show it improves the performance of the sentence alignment model.
Key wordscomputer application; Chinese information processing; sentence alignment; gaussian mixture model; transfer learning; anchor information