Abstract：The conventional word embeddings are learned from the co-occurrence probabilities between the words within a same sentence. The learning algorithm is task-independent and unsupervised. A method for constructing word embeddings is proposed by utilizing the constraints of paraphrasing to improve the performance of paraphrase scoring with word embeddings and bag-of-words model in knowledge base (KB) based question answering (QA). In the proposed method, the pairs of paraphrase questions and non-paraphrase questions are collected respectively from a database of question paraphrases according to some designed rules. Then, the inequalities describing the similarities between the pairs of questions are adopted to represent the semantic constraint at the sentence level. These inequalities are integrated into the objective function for training word embeddings. Experimental results show that the proposed method improves the accuracies of paraphrase scoring and KB-based question answering compared with conventional word embedding methods.
詹晨迪，凌震华，戴礼荣. 面向知识库问答中复述问句评分的词向量构建方法*[J]. 模式识别与人工智能, 2016, 29(9): 825-831.
ZHAN Chendi, LING Zhenhua, DAI Lirong. Learning Word Embeddings for Paraphrase Scoring in Knowledge Base Based Question Answering. , 2016, 29(9): 825-831.
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