【作者】 刘艳芳；

【导师】 丁天昌； 封化民；

【作者基本信息】 燕山大学 ， 信号与信息处理， 2007， 硕士

【摘要】 问答系统(QA)是允许用户输入一个自然语言形式的提问,通过检索,得到能够回答该问句的比较简短而准确的一个句子、摘要或者一个词。文本文档的问答系统研究已经取得了一定的进展。随着网络技术的发展,除了文本,网络上其他多媒体信息变得越来越重要,这对问答系统既是机遇也是一种挑战。视频是我们获捕外界信息的最有效媒体之一,因此本论文主要对新闻视频进行问答系统研究。在视频的各种特征中,脚本是最重要的且是比较容易得到的,况且,视频问答系统输入的是一个纯文本类型的问句,所以系统框架中主要运用的是通过自动语音识别(ASR)而得到的脚本特征。本论文提出了一种中文视频问答系统的框架。整个系统包括6个模块:视频分割、语音识别、问句分类、脚本检索、答案抽取和最后的视频输出。脚本中包含了大量的语音识别错误,我们人为地对部分错误进行了纠错。在问句分类模块,本论文利用知网(HowNet)来提高问句分类的准确率。视频QA是为了得到问句的最准确的视频答案,而不仅仅是一个很长的故事单元,所以对检索得到的故事单元需要进行更详细地答案抽取。本论文根据关键词密度、问句分类时的答案类型等为输出的句子打分,分数最高的句子对应的视频作为输出。本论文的主要创新在于:(1)在问句分类中知网的运用;(2)把文本问答系统扩展到中文视频中,这对问答系统研究是一个突破。对中文CCTV4新闻视频的实验表明,我们提出的方法是可行的。更多还原

【Abstract】 Question Answering is to locate, extract, and represent a specific answer to a user question expressed in natural language, and current question answering systems succeed in many aspects regarding to questions of textual documents. With the development of the internet, In addition to traditional text message, multimedia data has become increasingly important data on the web, which provides both opportunities and challenges for question answering. video is one of the most effective information for capturing the events in the real world. Our framework is based on news video. In all features, transcript is the most important and most readily available video features. Moreover, the input of video question answering(VideoQA) is a short question, so we main employ transcript feature that is gained by ASR.This paper proposes a framework for Chinese Video question answering system. The whole system consists of six modules: video segmentation, speech recognition, question classification, transcript retrieval, answer extraction and video output. But the news transcripts contain numerous speech recognition errors, so we manually correct some errors. In the module of question classification, we employ HowNet to improve the accuracy. VideoQA is to obtain the close video clips, and not just a long story unit, so we need to process and position the close sentences to answer the question. We claim that the best sentence that answers the question should satisfied some conditions which are based on query density, answer type, etc.The main contributions of this paper are: (1) HowNet is employed in QA system; (2)the extension of QA technology to support QA in Chinese news video. Experiments on Chinese news CCTV4 show that our framework is effective.更多还原