隨著資訊科技的進步，資訊的量隨著逐漸增加，而使用者所面對的資訊也就越來越多。因此，若缺少幫助我們搜尋資訊的技術，資料的搜尋將會相當困難。為了解決這個問題，產生了資訊檢索 (information retrieval) 這個技術。在這種技術之下，使用者所期望的是資訊檢索系統能夠將使用者「想要」的內容搜尋出來。
本篇論文將語言模型應用於資訊檢索的領域，採用混合式機率模型(Mixture Model)來描述文件的隨機特性，由多個語言模型及 Expectation-Maximization(EM)[9]演算法經由事後機率的估算，來對每篇文章求得一組特定的參數。另外我們亦加入在資訊檢索上常見的潛在語意索引 (Latent Semantic Index, LSI ) ，將龐大訓練文集中的資訊以低維度的矩陣保存，在測試時即運用此矩陣內容來獲得長距離資訊及潛在的語意特徵。經由實驗的結果，我們發現將語言模型也視為參數，亦即以不同類別的語料庫來取代平衡語料庫並經由EM的參數估測，能更有效的提昇檢索的正確率；另將LSI的資訊加入Mixture Model中，亦能提高檢索的正確性。

Due to the rapid development of information technology, we need to explore more methodologies to resolve more difficult problem. Accordingly, it would be very important to build effective information retrieval system to obtain useful information. When using this technology, users expect this technology is helpful to search for what they really need.
Language Model is a very important technique in speech recognition and natural language processing. In this thesis, we apply the language model in the application of news information retrieval. We use the probabilistic mixture mode to characterize the documents and use and Expectation-Maximization (EM) algorithm to estimate the model parameters for each individual document. Furthermore, we combine the Latent Semantics Indexing (LSI) in the proposed model retrieval model. Using LSI, the huge training data are reduced to a low dimension vector. As a result, we can obtain long distance information and latent semantics. In the experiments, we find that the parameter for each documents achieve better performance. Also the LSI information is feasible to improve the retrieval precision accuracy.

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