【摘要】：近年來(lái),突發(fā)事件頻頻發(fā)生。應(yīng)急管理的重要性越來(lái)越突出。應(yīng)急管理的過(guò)程中涉及多方面數(shù)據(jù)的融合。如何快速、準(zhǔn)確的提供相關(guān)的數(shù)據(jù)是急需研究的問(wèn)題。隨著互聯(lián)網(wǎng)的發(fā)展,網(wǎng)絡(luò)上的數(shù)據(jù)呈指數(shù)級(jí)增長(zhǎng),這些數(shù)據(jù)中包含了很多應(yīng)急管理需要的信息。地名信息是應(yīng)急信息的核心支撐點(diǎn)。本文進(jìn)行地名本體實(shí)體和關(guān)系抽取研究,抽取地名相關(guān)的實(shí)體和實(shí)體間的關(guān)系,為應(yīng)急數(shù)據(jù)的抽取和語(yǔ)義化奠定核心基礎(chǔ)。 實(shí)體和關(guān)系的抽取屬于自然語(yǔ)言處理中的命名實(shí)體識(shí)別和關(guān)系抽取。目前主流的方法有基于規(guī)則的方法和基于機(jī)器學(xué)習(xí)的方法。本文在抽取的過(guò)程中根據(jù)原始文本中實(shí)體和關(guān)系的特點(diǎn)分別因地制宜地采取了基于規(guī)則和基于機(jī)器學(xué)習(xí)的方法。 由于業(yè)界沒(méi)有建立好的地名領(lǐng)域抽取的語(yǔ)料庫(kù),本文首先建立了地名本體抽取的實(shí)體體系和關(guān)系體系,然后根據(jù)抽取過(guò)程中關(guān)注的特征建立實(shí)體抽取和關(guān)系抽取所需要的語(yǔ)料,詳細(xì)介紹了語(yǔ)料庫(kù)構(gòu)建的過(guò)程。對(duì)地名本體實(shí)體根據(jù)其在原始文本中出現(xiàn)的規(guī)律進(jìn)行了分類(lèi),分別采用基于規(guī)則的方法和利用最大熵進(jìn)行機(jī)器學(xué)習(xí)的方法。首先總結(jié)了四類(lèi)地名本體實(shí)體的抽取規(guī)則,然后對(duì)于其他的幾類(lèi)地名本體實(shí)體,首先對(duì)機(jī)器學(xué)習(xí)過(guò)程中使用的特征進(jìn)行了分析,基于標(biāo)注的語(yǔ)料,利用最大熵進(jìn)行了地名實(shí)體的抽取。對(duì)于關(guān)系的抽取,首先分析了關(guān)系的特點(diǎn),采用基于特征向量的方法,利用SVM進(jìn)行關(guān)系的抽取。根據(jù)語(yǔ)料的特點(diǎn),提出了基于規(guī)則的方法抽取地名本體的關(guān)系。同時(shí),分析了關(guān)系的特點(diǎn),制定了相關(guān)的規(guī)則,從已有的關(guān)系出發(fā),推導(dǎo)出隱含的關(guān)系,進(jìn)一步豐富地名本體關(guān)系庫(kù)。 最后,設(shè)計(jì)和實(shí)現(xiàn)了地名本體實(shí)體和關(guān)系抽取平臺(tái),并將抽取的數(shù)據(jù)應(yīng)用到了實(shí)際的語(yǔ)義地名搜索引擎中,實(shí)踐證明,抽取的實(shí)體和關(guān)系數(shù)據(jù)很大程度上提升了用戶(hù)體驗(yàn),幫助了用戶(hù)更方便、更迅速、更準(zhǔn)確的地名相關(guān)數(shù)據(jù)。
[Abstract]:In recent years, emergencies occur frequently. The importance of emergency management is becoming more and more prominent. The process of emergency management involves the fusion of many aspects of data. How to provide relevant data quickly and accurately is an urgent problem. With the development of the Internet, the data on the network increase exponentially, which contains a lot of information needed for emergency management. Toponymic information is the core support of emergency information. In this paper, the ontology and relation extraction of geographical names is carried out to extract the relationship between entities and entities, which lays the core foundation for the extraction and semantics of emergency data. The extraction of entities and relationships belongs to named entity identification and relation extraction in natural language processing. At present, the mainstream methods are rule-based approach and machine-based learning method. According to the characteristics of entities and relationships in the original text, this paper adopts rule-based and machine-learning methods in the process of extraction, respectively. Because there is no good corpus for toponymic domain extraction, this paper first establishes the entity system and relational system of toponymic ontology extraction, and then establishes the corpus needed for entity extraction and relational extraction according to the features concerned in the extraction process. The construction process of corpus is introduced in detail. The ontology entities of geographical names are classified according to their rules in the original text, respectively, which are based on rules and machine learning methods using maximum entropy. Firstly, the extraction rules of four kinds of toponymic ontology entities are summarized, then the features used in the machine learning process are analyzed for several other toponymic ontology entities, which are based on annotated corpus. The maximum entropy is used to extract geographical names. For the extraction of relationships, the characteristics of the relationships are analyzed, and the feature vector based method is used to extract the relationships using SVM. According to the characteristics of corpus, a rule-based method is proposed to extract the relation of geographical names ontology. At the same time, the characteristics of the relationship are analyzed, and the relevant rules are made. Based on the existing relations, the implicit relationship is derived, which further enriches the ontology relation database of geographical names. Finally, the ontology entity and relational extraction platform are designed and implemented, and the extracted data are applied to the actual semantic toponymic search engine. The practice shows that the extracted entity and relational data greatly improve the user experience. Help users to more convenient, faster, more accurate place name related data.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP391.1

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本文編號(hào)：2157790