本文選題：問答系統(tǒng)　切入點：知識庫擴充　出處：《哈爾濱工業(yè)大學》2013年碩士論文

【摘要】：隨著Internet的不斷發(fā)展，人們已經(jīng)不滿足于僅僅利用搜索引擎搜索需要的信息。如何快速方便的為用戶提供需要的信息成為人們努力研究的焦點。自動問答系統(tǒng)剛好具有既能滿足用戶對信息的需求，也能滿足獲取人性化回復這兩方面的特點，因此能夠很好的解決這一問題。但是傳統(tǒng)的問答系統(tǒng)沒有對已經(jīng)存在的那些回復答案不理想的問題自動識別的機制，這對問答系統(tǒng)進行改進或知識庫更新都是一個挑戰(zhàn)。 為了彌補傳統(tǒng)問答系統(tǒng)缺乏對回復不好的問題進行識別的缺點，本課題對交互式問答系統(tǒng)中存在的待改進問題的自動識別方法進行研究， 本課題提出了一種交互式問答系統(tǒng)中的待改進問題自動識別方法，對基于用戶情感、意圖和混合特征的待改進問題識別效果進行分析，，將需要通過人工審核方式識別待改進問題的工作轉換為使用自動識別方法對其進行識別，省去了人工審核的工作，提高識別效率。 為了更好地識別系統(tǒng)中的待改進問題，本課題設計了一種面向混合特征的知識庫擴充方法，采用網(wǎng)絡爬蟲工具，將知識庫語料擴充為39161條，這些設計多領域多方面的問答語料基本滿足了用戶的會話需求。 在此研究基礎上改進了問答系統(tǒng)架構和運行平臺的可移植性，現(xiàn)在的比特機器人問答系統(tǒng)能夠運行于微信、QQ和網(wǎng)頁三種平臺。這種多平臺的運行模式為問答系統(tǒng)吸引大量使用用戶。 識別出這些待改進問題后，將通過人工審核的方式獲取正確答案，最后將這些改進后的問題和改進后的答案更新至系統(tǒng)知識庫，從而實現(xiàn)問答系統(tǒng)知識庫的更新。 本課題實驗過程的數(shù)據(jù)來源是問答系統(tǒng)微信平臺獲取的真實問答語料，共計3119條問答對。通過對這些真實會話語料的標注和分析，確定待改進問題的識別方法。最終對問答系統(tǒng)中待改進問題的識別準確率達到76.77%。最后的實驗結果和系統(tǒng)實際運行效果證明了本課題提出的問答系統(tǒng)中待改進問題的自動識別方法的可行性。
[Abstract]:With the development of Internet, people are not satisfied with the information that search engine needs.How to provide information for users quickly and conveniently has become the focus of research.The automatic Q & A system has the characteristics of not only meeting the information needs of users, but also meeting the two characteristics of obtaining humanized reply, so it can solve this problem very well.But the traditional question answering system does not have the mechanism to automatically identify the questions which are not well answered, which is a challenge to the improvement of the question answering system or the updating of the knowledge base.In order to make up for the shortcoming of the traditional question answering system, this paper studies the automatic recognition method of the problem in the interactive question answering system.In this paper, an improved problem recognition method in interactive question answering system is proposed, and the effect of problem recognition based on user emotion, intention and mixed features is analyzed.The work needed to identify the problems to be improved by means of manual auditing is transformed into the identification of the problems by automatic identification, which saves the work of manual auditing and improves the efficiency of identification.In order to better identify the problems to be improved in the system, a hybrid feature oriented knowledge base expansion method is designed in this paper. The knowledge base corpus is expanded to 39161 by using the web crawler tool.These design multi-domain and multi-faceted question and answer corpus basically satisfy the user's conversation demand.On the basis of this research, the architecture of Q & A system and the portability of running platform are improved. Now, the quizzing system of bit robot can run on three kinds of platforms: WeChat QQ and web page.This multi-platform mode of operation attracts a large number of users for the Q & A system.After identifying these questions to be improved, the correct answers will be obtained by manual examination. Finally, the improved questions and the improved answers will be updated to the system knowledge base, thus the updating of the question answering system knowledge base will be realized.The data source of the experiment process is the real question and answer corpus obtained by the Question-answering system WeChat platform, with a total of 3119 question-and-answer pairs.Through the annotation and analysis of these real conversational data, the identification method of the problem to be improved is determined.Finally, the accuracy of problem recognition in question answering system is 76.77.Finally, the experimental results and the actual operation results of the system prove the feasibility of the automatic identification method of the problem to be improved in the question and answer system proposed in this paper.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP393.09

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