本文選題：表示學習 + 多特征融合��； 參考：《北京郵電大學》2016年碩士論文

【摘要】：隨著神經網絡算法和分布式并行計算技術的迅速發(fā)展,文本的表示問題又重新回歸到人們的視野中。作為自然語言處理的基本問題,如何有效表征人類抽象復雜的語言一直是不可逃避的難題。近年來,隨著互聯(lián)網數據呈指數形式增長,令這一問題更加凸顯�；谏窠浘W絡的詞的表示學習旨在以詞為最小單位來解決該問題,該類模型不僅充分利用大語料的信息,還通過各種優(yōu)化手段降低訓練時間復雜度,使人們能夠方便地獲得保留語義語法信息的表示向量,為自然語言處理的其他任務建立了良好的特征基礎。詞向量在信息檢索,情感分析,機器翻譯等任務取得了不錯的成績,但是仍然有提升空間�；诖吮尘�,本文進行了一下工作:第一,本文研究了詞的表示學習方法及優(yōu)化策略,提出了多特性融合的詞向量的優(yōu)化方法,實現(xiàn)了先驗的詞性信息,位置權重因子,段落向量相融合,在詞類比測試比原模型準確率提高了兩個百分點。第二,本文還發(fā)現(xiàn)了詞向量反義詞區(qū)分能力上的不足,調研分析了區(qū)分因素,在同反義詞集合上驗證了模型的區(qū)分能力。第三,本文在Skip-gram模型的基礎上提出和實現(xiàn)了在線學習多義詞模型,對詞學習多個語義對應的向量,并且再次融合多特性以進一步提升的多義詞模型的效果,獲得了與當前最優(yōu)結果比肩的效果。
[Abstract]:With the rapid development of neural network algorithm and distributed parallel computing technology, the problem of text representation is returning to people's vision. As a basic problem of natural language processing, how to effectively represent human abstract and complex language has always been an unavoidable problem. In recent years, with the exponential growth of Internet data, this problem has become more prominent. The representation learning of words based on neural network aims to solve the problem by taking words as the smallest unit. This kind of model not only makes full use of the information of large corpus, but also reduces the complexity of training time by various optimization methods. It makes it easy to obtain the representation vector of preserving semantic grammar information, and establishes a good feature foundation for other tasks of natural language processing. Word vectors have achieved good results in information retrieval, affective analysis, machine translation and other tasks, but there is still room for improvement. Based on this background, this paper does some work: first, this paper studies the representation learning method and optimization strategy of words, proposes a multi-feature fusion word vector optimization method, realizes the prior part of speech information, position weight factor, Paragraph vector fusion improves the accuracy of part of speech test by two percentage points compared with the original model. Secondly, this paper also finds out the deficiency of lexical vector antonym distinguishing ability, investigates and analyzes the distinguishing factors, and verifies the model's distinguishing ability on the same antonym set. Thirdly, based on the Skip-gram model, this paper proposes and implements an online learning polysemous word model, learning multiple semantic corresponding vectors for words, and again fusing multiple features to further improve the effectiveness of the polysemous word model. The results are compared with the current optimal results.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.1;TP18

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