摘要
信息社会的重要特征之一是信息检索,各种搜索引擎为人们检索信息提供了很大帮助。如何使搜索引擎理解检索需求,以获得更加精确的检索结果,这正是问答系统追求的目标。问答系统是信息检索的分支,属于精确检索。本文系统地介绍了问答系统的研究内容、中文问答系统的相关技术及研究现状,将Agent联盟和流形学习引入中文问答系统的研究和实现中。实质性工作和创新点如下:
1、采用流形学习方法提高问句分类精度
问句分类在问答系统中起着至关重要的作用,其结果对答案提取具有很好的指导作用,直接影响到系统回答问题的准确性。本文将流形学习引入中文问句分类中,结合中文问句的多类别特点,设计了基于局部线性嵌入的中文问句分类算法,实验表明问句分类精度得到明显提高。
2、采用元搜索技术提高问答系统的准确性
针对单一通用搜索引擎存在的信息资源覆盖能力低、检索效率较低等不足,本文采用元搜索技术为每个问题寻找最适合的搜索方式。尤其知识搜索引擎的运用、专用问答系统的调用在问答系统中都是首次引入,大大提高了系统的语义检索能力和检索效率,因而提高了问答系统的准确性。
3、采用多Agent技术提高系统整体性能
中文问答系统的各个步骤都有诸多方法,这些方法有各自的特点和适应性。由于开放域问答系统中问题的多样性(无论问句类别还是涉及领域),任何一种方法都不是普遍适用的。为此,本文将多Agent技术引入中文问答系统,结合中文问答系统的特点,提出了基于多Agent的中文问答系统模型;并将该模型的问答求解转变为Agent联盟求解,定义了中文问答系统的Agent联盟问题;分别采用蚁群算法、改进蚁群算法、遗传算法、遗传蚁群融合算法等智能优化算法来优化求解,提高了系统的整体性能。
One of the important features of information society is information retrieval. Various searching engines are useful to people. But how to make searching engines understand the needs of users’search to get more exact result is the goal of question answering system (QA). Question answering system is a branch of information retrieval, it belongs to accurate retrieval. This thesis describes content of question answering system, relevant technologies and status of Chinese question answering system (Chinese QA) systematically, introduces multi-agent technology and manifold learning into research and implementation of Chinese question answering system. The main works in this thesis are as follows:
1) Manifold learning is applied to improve precision of question classification.
Question classification plays a very important role in question answering system, its result is a very good guide to answer extract, and affects the precision of answering question. This thesis introduces manifold learning into question classification. Combining with multi-class characteristic of Chinese questions, we designed an algorithm for Chinese question classification based on Local Linear Embedding (LLE), and the experience showed that the precision of question classification was significantly improved.
2) Meta search technology is applied to improve precision of question answering.
Aiming at the shortages of lower searching efficiency and lower ability in information resource covering of single general search engine, meta search technology is applied to find the most appropriate search method for each question. Especially utilization of knowledge search engine and call of special question answering system are introduced for the first time in question answering system. It can improve semantic search ability and searching efficiency of the system, so the precision of question answering can be improved.
3) Multi-agent technology is applied to improve overall performance of the system.
There are many methods at each step of the Chinese question answering system. Every method has its characteristic and suitability. Because of the diversity of questions of open field question answering system (whether question class or related field), no single method is universal. This thesis introduces multi-agent technology into Chinese question answering system, put forward Chinese question answering system model based on multi-agent, in which each step uses agent technology, and each step has many agents of the same kind but different abilities. We transformed solution of question answering to solution of multi-agent coalition, described the agent coalition question of Chinese question answering system model, then we used ant colony algorithm, improved ant colony algorithm, genetic algorithm, genetic algorithm and ant colony algorithm to optimize the solution, thus able to improve the overall performance of the system.
引文
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