本文選題：用戶交互式問答系統(tǒng) + 問題推薦機制�。� 參考：《中國科學(xué)技術(shù)大學(xué)》2012年博士論文

【摘要】：在計算機網(wǎng)絡(luò)技術(shù)迅速發(fā)展的今天,互聯(lián)網(wǎng)應(yīng)用得到迅速普及。用戶交互式問答系統(tǒng)作為Web2.0時代的典型應(yīng)用已經(jīng)成為現(xiàn)今最流行的社交網(wǎng)絡(luò)應(yīng)用之一,它為互聯(lián)網(wǎng)用戶提供了一個搜索信息和共享知識的平臺。相較于從搜索引擎獲取信息的方式,用戶在交互式問答系統(tǒng)中通過簡單的提問和回答方式快速準確的獲取所需信息,而不是從搜索引擎返回的大量相關(guān)文檔中繁瑣地去查找信息。用戶交互式問答系統(tǒng)雖然為人們提供了獲取信息的便捷服務(wù),但是依然存在著各種各樣的問題,用戶等待答案時間長和答案質(zhì)量差是其中最顯著的兩個問題。在用戶交互式問答系統(tǒng)中,提問者有時候需要等待幾個小時甚至是幾天的時間來等待其他用戶提供答案。此外,一些用戶為了獲取交互式問答系統(tǒng)中的積分等級,提供很多不相關(guān)答案甚至是垃圾答案,這些問題都大大降低了用戶獲取所需信息的效率。 為了提高交用戶互式問答系統(tǒng)的性能,解決系統(tǒng)中存在大量零回答問題和低質(zhì)量答案的問題,本文提出了在用戶交互式問答系統(tǒng)中建立問題推薦的機制。將系統(tǒng)中尚未被人回答的問題,通過推薦機制將其推送給合適的專家用戶去回答,以提高回答效率和答案質(zhì)量。本文首先對于用戶交互式問答系統(tǒng)中的問題推薦機制給出了定義并詳細描述了問題推薦的模型。在此基礎(chǔ)上,本文隨后提出了兩種不同的問題推薦策略,分別是將未解決的問題推薦給領(lǐng)域問答專家和將問題推薦給特定的問答專家來回答。 在第一種問題推薦策略中,同一類別下的問題將會被推薦給該類別領(lǐng)域中的問答專家用戶。本文分別提出了語義鏈接分析方法和語義語言模型方法來發(fā)現(xiàn)領(lǐng)域問答專家。在語義鏈接分析方法中,我們首先根據(jù)在用戶交互式問答系統(tǒng)中各個用戶之間的問答關(guān)系構(gòu)建用戶問答關(guān)系圖。在這個關(guān)系圖中,每一個結(jié)點代表一個用戶,結(jié)點之間的每一條連接邊代表用戶之間的問答關(guān)系。其次,我們從用戶所參與問題會話的交互行為和問答內(nèi)容中抽取出不同類型的語義信息,并將這些語義信息結(jié)合到傳統(tǒng)鏈接分析方法中,衍生出新的語義鏈接分析方法。在該新方法中,用戶之間的問答鏈接關(guān)系融入了諸如問題難度、答案相關(guān)性、答案質(zhì)量、隱性鏈接等語義信息,從而產(chǎn)生出具有不同權(quán)重的鏈接關(guān)系。最后,我們在帶有語義信息的用戶問答關(guān)系圖上執(zhí)行一個名為繁殖計算的鏈接分析方法,來計算每一個用戶的專家程度值,用戶獲得較高計算值的將會被認為更加專家,獲得最高值的前1%用戶將會被認為是領(lǐng)域問答專家。在語義語言模型方法中,我們在傳統(tǒng)語言模型的方法中融入抽取出的各種語義信息,將其作為權(quán)重因素考慮到傳統(tǒng)語言模型中。通過計算用戶在某一問題類別下是否為問答專家的條件概率來查找出領(lǐng)域問答專家。通過在用戶交互式問答系統(tǒng)Yahoo! Answers中獲取的數(shù)據(jù)上進行的實驗,驗證了我們提出的語義鏈接分析方法和語義語言模型方法在領(lǐng)域問題專家發(fā)現(xiàn)問題上較傳統(tǒng)方法有了顯著的提高。此外,實驗也同樣驗證了抽取出的語義信息對于提高領(lǐng)域?qū)＜野l(fā)現(xiàn)方法性能的有效性。 相較于第一種問題推薦策略的粗粒度性,第二種問題推薦策略旨在發(fā)現(xiàn)能回答某一未解決問題的特定專家用戶,并將該問題推薦給特定問答專家回答。在此問題推薦策略中,我們首先根據(jù)用戶回答過的問題信息建立用戶問答檔案文件,在此基礎(chǔ)上建立起一個基于主題的用戶興趣模型,在該模型中用戶問答檔案被認為是在不同主題上的一個分布,通過吉布斯抽樣的方法,我們可以有效的獲得用戶興趣在主題上的準確分布。然后,根據(jù)已經(jīng)獲取的用戶興趣主題模型,我們可以有效地計算出用戶是否為某一問題的特定問答專家的概率值。概率值計算結(jié)果越高的用戶將會被認為在回答該問題上專家程度越高。通過在用戶交互式問答系統(tǒng)Yahoo! Answers中獲取的數(shù)據(jù)上進行的實驗,驗證了我們所提出的兩種不同問題推薦機制的高效性。進一步地,根據(jù)實驗結(jié)果我們對比了兩種不同問題推薦策略的性能。從實驗結(jié)果中,我們可以發(fā)現(xiàn)第一種問題推薦策略較優(yōu)于第二種策略。出現(xiàn)該現(xiàn)象的原因可能是,在第一種發(fā)現(xiàn)領(lǐng)域?qū)＜业膯栴}推薦策略中覆蓋了絕大部分的問答專家用戶,而第二種發(fā)現(xiàn)特定問題問答專家方法只能查找出部分問答專家,這導(dǎo)致了第二種問題推薦策略在性能上略遜于第一種策略。該對比實驗結(jié)果為用戶交互式問答系統(tǒng)中問題推薦機制的策略選擇提供了重要的參考。
[Abstract]:With the rapid development of computer network technology, the Internet application has been popularized rapidly. The user interactive question answering system, as a typical application of the Web2.0 era, has become one of the most popular social network applications. It provides a platform for Internet users to search information and share knowledge. In the way of information, users can get the information quickly and accurately through simple questions and answers in interactive question answering system, instead of searching for information from a large number of relevant documents returned by the search engine. The user interactive question answering system still has a convenient service for people to obtain information. In a variety of questions, the user waiting for a long answer and poor answer is the two most significant problem. In the user interactive question answering system, the questioner sometimes has to wait a few hours or even a few days to wait for the other users to provide answers. In addition, some users have to get the points in the interactive Q & a system. Level, which provides many irrelevant answers or even garbage answers, greatly reduces the efficiency of users getting the information they need.
In order to improve the performance of interuser reciprocal question answering system and solve the problem of zero answer and low quality answer in the system, this paper proposes a mechanism to establish a problem recommendation in the user interactive question answering system. The problem that has not been answered in the system is pushed to the appropriate expert user to answer by the recommendation mechanism. In order to improve the answer efficiency and answer quality, this paper first defines the problem recommendation mechanism in the user interactive question answering system and describes the recommended model in detail. On the basis of this, this paper proposes two different recommendation strategies, which are to recommend the unresolved question to the domain question and answer expert and the question. The question is recommended to a particular question and answer expert to answer.
In the first problem recommendation strategy, the problem under the same category will be recommended to the question and answer expert users in the category domain. The semantic link analysis method and the semantic language model method are proposed to find the domain question and answer expert respectively. In the semantic link analysis, we first based on the user interactive question answering system. In this relationship diagram, each node represents a user, and each connection between nodes represents a question and answer relationship between the users. Secondly, we extract different types of semantic information from the interactive behavior and question and answer content of the user's participation in the question session. And combining these semantic information into the traditional link analysis method, a new method of semantic link analysis is derived. In this new method, the question answer link relationship among users is integrated into the semantic information such as problem difficulty, answer relevance, answer quality, recessive link and so on. We execute a link analysis method named reproduction calculation on the user question answering graph with semantic information to calculate the expert degree value of each user. The top 1% users who obtain higher values will be considered to be more experts. The top 1% users will be considered as domain question and answer experts. In the traditional language model, we incorporate the various semantic information extracted from the traditional language model, and consider it as the weight factor in the traditional language model. By calculating the conditional probability of a question answering expert under a certain problem category, we find out the domain question and answer expert. Through the user interactive question answering system Yahoo! Answers The experiments carried out on the obtained data verify that the semantic link analysis and the semantic language model method proposed by us have improved significantly on the problem of expert discovery in domain problems. In addition, the experiment also validates the effectiveness of the extraction of semantic information for improving the performance of domain expert discovery methods.
Compared to the coarse granularity of the first problem recommendation strategy, the second problem recommendation strategy aims to find a specific expert user who can answer a certain unsolved problem and recommend it to a specific question answer expert. In this recommendation strategy, we first establish a user question and answer file based on the question information returned by the user. On this basis, a user interest model based on the theme is established. In this model, the user question and answer file is considered to be a distribution on different topics. Through the Gibbs sampling method, we can effectively obtain the accurate distribution of the user interest on the subject. Then, according to the subject model of the user interest that has been obtained, I We can effectively calculate the probability of whether a user is a particular question answering expert. The higher the probability of a user will be considered to be more expert in answering the question. The two kinds of experiments we have proposed through the experiment on the data obtained in the user interactive question answering system Yahoo! Answers According to the experimental results, we can find that the first problem recommendation strategy is better than the second strategy. The reason for the emergence of this phenomenon may be the problem recommendation strategy in the first field of discovery experts. It covers most of the question answer expert users, and the second kinds of question answering expert method can only find out some questions and answer experts. This leads to the second problem recommendation strategy less than the first strategy in performance. The comparison experiment results provide the strategy selection of the problem recommendation mechanism in the user interactive question answering system. An important reference.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：TP391.3

【引證文獻】

相關(guān)碩士學(xué)位論文 前2條

1 劉曉鳴;社區(qū)問答系統(tǒng)中的專家發(fā)現(xiàn)方法研究[D];大連理工大學(xué);2013年

2 吳瑞紅;互動問答社區(qū)中回答可信性分析[D];北京信息科技大學(xué);2013年

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本文編號：1923549