本文選題：情感要素抽取 + 集成學(xué)習(xí)��； 參考：《北京郵電大學(xué)》2015年碩士論文

【摘要】：近年來(lái),在互聯(lián)網(wǎng)的浪潮中,信息逐漸顯示出它巨大的力量。其中,迅速發(fā)展的社交網(wǎng)絡(luò)催生了自媒體的產(chǎn)生,大量帶有主觀情感傾向的信息涌現(xiàn)出來(lái)。那么,人們?nèi)绾卧谶@樣的信息海洋中找到有用信息,成為一個(gè)棘手的問(wèn)題。近幾十年來(lái),隨著自然語(yǔ)言處理(Natural Language Processing)技術(shù)的深入發(fā)展,人們可以借助信息抽取(Information Extraction)的方法來(lái)在海量數(shù)據(jù)中尋找自己感興趣的關(guān)鍵信息。而在信息抽取中,情感要素挖掘是一個(gè)很重要的方向。它關(guān)注于與一些用戶情感相關(guān)的信息,如情感來(lái)源,情感受體及情感的正負(fù)傾向。這些信息由于帶有主觀色彩,往往具有更重要的價(jià)值。特別是在互聯(lián)網(wǎng)時(shí)代,很多大型公司的廣告投放,推薦系統(tǒng)等都需要這些信息。 基于此背景,本文研究了情感要素挖掘近年的發(fā)展方向,設(shè)計(jì)并實(shí)現(xiàn)了一個(gè)基于經(jīng)典信息抽取方法的情感要素挖掘系統(tǒng)；并提出了一種基于條件隨機(jī)場(chǎng)與專家系統(tǒng)相結(jié)合的集成學(xué)習(xí)混合模型；此外,還利用外部語(yǔ)義信息,提升了機(jī)器翻譯模型在情感要素挖掘中的性能。本文的主要工作有如下幾個(gè)方面： 1.基于集成學(xué)習(xí)(Ensemble Learning)思想實(shí)現(xiàn)模型融合(Models Combining),將規(guī)則方法融入到半監(jiān)督學(xué)習(xí)中,尋找一種可行的方式將人工先驗(yàn)知識(shí)建模到機(jī)器學(xué)習(xí)模型中,并取得比僅使用單個(gè)模型更好的效果。 2.通過(guò)機(jī)器翻譯(Machine Translation)模型部分代替句法分析工具,在特定場(chǎng)景中實(shí)現(xiàn)情感要素抽取；并提出一種融合外部語(yǔ)義信息與新詞發(fā)現(xiàn)的提升方法。 3.設(shè)計(jì)并實(shí)現(xiàn)了一個(gè)基于經(jīng)典信息抽取方法的情感要素挖掘系統(tǒng),在中文語(yǔ)料的預(yù)處理中,提出一種針對(duì)中文語(yǔ)料的可擴(kuò)展的新詞發(fā)現(xiàn)方法。
[Abstract]:In recent years, in the tide of the Internet, information gradually shows its great power. Among them, the rapid development of social networks spawned the emergence of self-media, a large number of information with subjective emotional tendencies emerged. So, how people find useful information in such a sea of information becomes a thorny problem. In recent decades, with the development of Natural Language processing (NLP) technology, people can find the key information they are interested in the massive data by the method of information extraction. In information extraction, emotional factor mining is a very important direction. It focuses on information related to some users' emotions, such as emotional sources, emotional receptors and positive and negative tendencies. Because of the subjective color, these information often have more important value. Especially in the Internet era, many large companies advertising, recommendation systems and so on need this information. Based on this background, this paper studies the development direction of emotional factor mining in recent years, and designs and implements an emotional factor mining system based on classical information extraction method. An integrated learning hybrid model based on conditional random field and expert system is proposed. In addition, the performance of machine translation model in emotion element mining is improved by using external semantic information. The main work of this paper is as follows: 1. Based on the idea of Ensemble learning, this paper implements the model fusion of models and combines models into semi-supervised learning, and finds a feasible way to model artificial priori knowledge into machine learning model, and achieves better results than only using a single model. 2. In this paper, the machine translation model is used to partially replace the syntactic analysis tool to extract emotion elements in a specific scene, and a method to improve the performance of the model is proposed, which combines the external semantic information with the discovery of new words. 3. An emotion element mining system based on classical information extraction method is designed and implemented. In the preprocessing of Chinese corpus, an extensible new word discovery method for Chinese corpus is proposed.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.1

【共引文獻(xiàn)】

相關(guān)期刊論文 前10條

1 呂美香;何琳;李s，

本文編號(hào)：1872088