本文選題：語義依存分析 + 語義搜索�。� 參考：《哈爾濱工業(yè)大學(xué)》2012年碩士論文

【摘要】：互聯(lián)網(wǎng)的飛速發(fā)展使得信息以前所未有的速度產(chǎn)生和傳播，面對信息呈指數(shù)式增長、垃圾信息泛濫成災(zāi)的困境，搜索引擎如何找到對用戶真正有用的信息遇到了很大的挑戰(zhàn)。在傳統(tǒng)的搜索引擎中，用戶輸入查詢（query），搜索引擎返回一個(gè)很長的網(wǎng)頁列表。它不知道用戶在問什么，不知道用戶想找什么，只是通過基于關(guān)鍵字匹配的檢索方式，，把包含有關(guān)鍵詞的網(wǎng)頁找到；再通過網(wǎng)頁排序的算法，將結(jié)果列表進(jìn)行排序以后展示給用戶，用戶需要在很長的列表中，自己篩選出真正想要的信息。Query語義依存分析技術(shù)首先可以改善傳統(tǒng)搜索引擎中的網(wǎng)頁排序，它能夠?qū)uery進(jìn)行深層語義理解，從而更準(zhǔn)確的理解用戶的需求，減輕用戶篩選信息的負(fù)擔(dān)。 另一方面，相對于傳統(tǒng)搜索引擎，語義搜索近來受到工業(yè)界和學(xué)術(shù)界的廣泛關(guān)注。和傳統(tǒng)搜索引擎給出信息列表不同，語義搜索將所有信息組織成一個(gè)龐大的知識(shí)庫，面對用戶的query，它直接從知識(shí)庫中檢索并返回答案。從而用戶省去了自己篩選信息的步驟，更快速更直接地達(dá)到搜索的目的。Query語義依存分析技術(shù)可以幫助語義搜索引擎更深刻的理解用戶需求，更準(zhǔn)確的在知識(shí)庫中進(jìn)行答案的查找。除此之外，query語義依存分析技術(shù)還在自動(dòng)問答、智能個(gè)人助手、信息檢索、信息抽取等方向有著廣闊的應(yīng)用前景。 本文提出了基于規(guī)則和基于統(tǒng)計(jì)的兩個(gè)語義依存分析技術(shù)，主要研究內(nèi)容包括： （1）Query語義依存分析和普通句子上的語義依存分析的異同。相對普通句子來說，query具有長度較短且結(jié)構(gòu)松散的特點(diǎn)，因而和普通句子上的語義依存分析技術(shù)有很大的差別。 （2）Query語義依存分析的依存關(guān)系體系的確定，即根據(jù)query的特點(diǎn)，以及應(yīng)用的需求，確定一個(gè)合適的依存關(guān)系體系。依存關(guān)系體系的確定，首先要考慮體系的完整性，是否能把主要的語義現(xiàn)象覆蓋住。其次也要考慮技術(shù)上的成本、應(yīng)用的需求等。本文確定了五類語義依存關(guān)系，分別是屬性、限定、施事、受事、需求。其中限定關(guān)系又分了六個(gè)子類別，分別是時(shí)間限定、地點(diǎn)限定、數(shù)字限定、型號(hào)限定、疑問限定、否定限定。 （3）針對六類特殊限定定義明確簡單的特點(diǎn)，提出了基于規(guī)則的query語義依存分析技術(shù)，包括規(guī)則的定義、規(guī)則的編制、規(guī)則的應(yīng)用。 （4）將語義依存分析問題轉(zhuǎn)換為分類問題，提出了基于統(tǒng)計(jì)的query語義依存分析技術(shù)，包括語義資源的挖掘、分類特征的設(shè)計(jì)和選擇。 最終通過對比和實(shí)驗(yàn)說明了規(guī)則和統(tǒng)計(jì)兩種方法的有效性。
[Abstract]:With the rapid development of the Internet, information is produced and spread at an unprecedented speed. In the face of the exponential growth of information and the flood of junk information, the search engine has encountered a great challenge how to find the information that is really useful to users. In traditional search engines, users type queries and search engines return a long list of pages. It doesn't know what the user is asking or what the user is looking for. It just finds the page with the keywords in the search method based on keyword matching. After sorting the result list to the user, the user needs to filter out the information. Query semantic dependency analysis technology can improve the web page sort in the traditional search engine. It can deeply understand the semantics of query, so as to understand the needs of users more accurately and lighten the burden of filtering information. On the other hand, compared with traditional search engines, semantic search has attracted extensive attention from industry and academia recently. Different from the traditional search engine, semantic search organizes all the information into a huge knowledge base. In the face of the user's query, it directly retrieves the answers from the knowledge base and returns the answer. Thus, users can save themselves the steps of filtering information, and achieve the purpose of searching more quickly and directly. Query semantic dependency analysis technology can help semantic search engines to understand user needs more deeply. More accurate search for answers in the knowledge base. In addition, query semantic dependency analysis technology also has a broad application prospect in automatic question answering, intelligent personal assistant, information retrieval, information extraction and so on. In this paper, two semantic dependency analysis techniques based on rules and statistics are proposed. The main research contents are as follows: There are similarities and differences between semantic dependency analysis and semantic dependency analysis in general sentences. Compared with ordinary sentences, the query is short in length and loose in structure, so it is quite different from the semantic dependency analysis techniques in common sentences. According to the characteristics of query and the requirements of its application, a suitable dependency system is determined. In determining the dependency system, the integrity of the system should be considered first, and whether the main semantic phenomena can be covered. Second, we should also consider the technical costs, application requirements and so on. In this paper, five kinds of semantic dependencies are defined, which are attribute, limitation, agent, patient and requirement. The limited relation is divided into six subcategories, namely, time limit, place limit, number limit, model limit, question limit and negative limitation. 3) aiming at the clear and simple characteristics of six kinds of special defined definitions, a rule-based query semantic dependency analysis technique is proposed, including the definition of rules, the compilation of rules, and the application of rules. 4) the semantic dependency analysis problem is transformed into the classification problem, and the query semantic dependency analysis technology based on statistics is proposed, including the mining of semantic resources, the design and selection of classification features. Finally, the effectiveness of the two methods is proved by comparison and experiment.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 郭江;車萬翔;劉挺;;漢語語義依存分析[J];智能計(jì)算機(jī)與應(yīng)用;2011年04期

2 趙江江;秦兵;;基于BootStrapping的中文事件元素抽取系統(tǒng)設(shè)計(jì)與實(shí)現(xiàn)[J];智能計(jì)算機(jī)與應(yīng)用;2012年01期

3 李超;劉鐵根;劉宏利;江俊峰;姚曉天;;融合SVM和AdaBoost的近紅外人臉識(shí)別方法[J];激光與紅外;2012年02期

4 劉志剛,李德仁,秦前清,史文中;支持向量機(jī)在多類分類問題中的推廣[J];計(jì)算機(jī)工程與應(yīng)用;2004年07期

5 張輝,徐菁,陸汝占;利用數(shù)據(jù)挖掘擴(kuò)充量詞名詞詞典的方法[J];計(jì)算機(jī)工程;2003年13期

6 丁文軍;薛安榮;;基于SVM的Web文本快速增量分類算法[J];計(jì)算機(jī)應(yīng)用研究;2012年04期

7 鄭實(shí)福,劉挺,秦兵,李生;自動(dòng)問答綜述[J];中文信息學(xué)報(bào);2002年06期

8 趙夷平;;傳統(tǒng)搜索引擎與語義搜索引擎服務(wù)比較研究[J];情報(bào)科學(xué);2010年02期

9 楊歡;許威;趙克;陳余;;動(dòng)詞屬性在自然語言處理當(dāng)中的研究與應(yīng)用[J];計(jì)算機(jī)技術(shù)與發(fā)展;2008年07期

本文編號(hào)：1928689