本文說的是基於freebase知識庫的問答系統，具體的說就是給出一句話，要從freebase中找出答案entity。其實思路也不複雜，就是對問題句子，還有候選答案節點的各個屬性，各自做嵌入，然後點乘作為他們匹配的分數，把分數最高的作為答案返回。作者是北航畢業的牛人董力，現在在北京微軟當研究員了。
Question Answering over Freebase with Multi-ColumnConvolutional Neural Networks
Li Dong, Furu Wei, Ming Zhou, Ke Xu
Proceedings of the 53rd Annual Meeting of the Associationfor Computational Linguistics and the 7th International Joint Conference onNatural Language Processing, pages 260–269, Beijing, China, July 26-31, 2015.
Answering natural language questions over a knowledge base is an important and challenging task. Most of existing systems typically rely on hand-crafted features and rules to conduct question understanding and/or answer ranking. In this paper, we introduce multi-column convolutional neural networks (MCCNNs) to understand questions from three different aspects (namely, answer path, answer context, and answer type) and learn their distributed representations. Meanwhile, we jointly learn low-dimensional embeddings of entities and relations in the knowledge base. Question-answer pairs are used to train the model to rank candidate answers. We also leverage question paraphrases to train the column networks in a multi-task learning manner. We use FREEBASE as the knowledge base and conduct extensive experiments on the WEBQUESTIONS dataset. Experimental results show that our method achieves better or comparable performance compared with baseline systems. In addition, we develop a method to compute the salience scores of question words in different column networks. The results help us intuitively understand what MCCNNs learn.
Answering natural language questions over a knowledge base is an important and challenging task. Most of existing systems typically rely on hand-crafted features and rules to conduct question understanding and/or answer ranking. In this paper, we introduce multi-column convolutional neural networks (MCCNNs) to understand questions from three different aspects (namely, answer path, answer context, and answer type) and learn their distributed representations. Meanwhile, we jointly learn low-dimensional embeddings of entities and relations in the knowledge base. Question-answer pairs are used to train the model to rank candidate answers. We also leverage question paraphrases to train the column networks in a multi-task learning manner. We use FREEBASE as the knowledge base and conduct extensive experiments on the WEBQUESTIONS dataset. Experimental results show that our method achieves better or comparable performance compared with baseline systems. In addition, we develop a method to compute the salience scores of question words in different column networks. The results help us intuitively understand what MCCNNs learn.