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多层感知器三种学习算法的比较
多层感知器是一种多层前馈神经网络 ,常用的快速训练算法有共轭梯度法、拟牛顿法。通
过模式分类实验对这两种算法和 BP算法进行比较 ,并由试验数据得出这几种算法的复杂性、可靠
性 ,以及由算法产生的多层感知器的泛化能力。
2018-04-26
基于知识图谱的问答系统
Question answering (QA) has become a popular way for humans to
access billion-scale knowledge bases. Unlike web search, QA over
a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and mapped
precisely to structured queries over the knowledge base. The challenge, however, is that a human can ask one question in many different ways. Previous approaches have natural limits due to their
representations: rule based approaches only understand a small set
of “canned” questions, while keyword based or synonym based approaches cannot fully understand the questions. In this paper, we
design a new kind of question representation: templates, over a
billion scale knowledge base and a million scale QA corpora. For
example, for questions about a city’s population, we learn templates such as What’s the population of $city?, How
many people are there in $city?. We learned 27 million templates for 2782 intents. Based on these templates, our QA
system KBQA effectively supports binary factoid questions, as well
as complex questions which are composed of a series of binary factoid questions. Furthermore, we expand predicates in RDF knowledge base, which boosts the coverage of knowledge base by 57
times. Our QA system beats all other state-of-art works on both
effectiveness and efficiency over QALD benchmarks.
2018-04-26
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