A Natural User Interface (NUI), where a user can type or speak a request, is a good complement to the well-known Graphical User Interface (GUI). Accurately extracting user intent from such typed or spoken queries is a very difficult challenge. Statistical and knowledge-based are the two opposite kinds of possible approaches. Both of them have advantages and disadvantages. This paper presents a mixed approach to spoken language understanding that tries to make best use of the both algorithms. The method was test with real data from users, and resulted in a task error rate of 1.94% and a semantic concept error rate of 5.73%.

In this paper, we introduced language network and described three kinds of networks. Keyword extraction is an important technology in many areas of document processing. In particularly, a keyword extraction algorithm based on language network and PageRank is proposed. Firstly a semantic network for a single document is build, then Pagerank is applied in the network to decide on the importance of a word, finally top-ranked words are selected as keywords of the document. The algorithm is tested on the corpus of CISTR, and the experiment result proves practical and effective.

This paper proposes a hypothesis reordering technique, based on a newly established theory, namely Comprehensive Information Theory, to improve the accuracy of speech recognition in a man-machine dialog system. For each hypothesis, we calculate the amount of comprehensive information that hypothesis provided and then reorder N-best hypothesis according to the amount of comprehensive information. Results of experiments have shown its effectiveness.

New word identification is a difficult point in Chinese word segmentation processing. In the automatic word segmentation processing of large Chinese texts, new word can cause segmentation mistakes. The paper defines new word identification as a binary classification problem: whether a character sequence in certain context is a new word or not, and use two statistical learning approaches based on support vector machine (SVM) and C4.5. We then investigate various linguistic and statistical features including Independent Word Probability of former character, Independent Word Probability of latter character, front position In-word probability of former character, back position In-word probability of latter character, Mutual Information and frequency. In PK-close test of the 1st Special Interest Group for Chinese Language Processing (SIGHAN) bakeoff, this approach achieves the high precision and recall.

Text representation is the basis of text processing. Most current text representation model didn’t consider of the words’ relations and result in the loss of text’s structure information, which is important to understand the text. This paper proposed a novel text representation model, which uses lexical network to represent the text and retains the text’s structure. According to the different levels of words’ relations, co-occurrence network, syntactic network and semantic network are introduced. The text network representation was applied into text classification to measure the representation ability of this model. The experiment result shows that our text network representation is prior to vector space model.

In this paper, we present a spoken dialog system for sports event guide, which could provide users with the information on such items as schedule, venue, team, as well as athlete according to users’ favor. It is one of the key applications in the ongoing project “Multi-lingual Intelligent Information Services Network System”, which will serve the Beijing 2008 Olympic Game. In addition to the descriptions of the system architecture and individual modules, we focus mainly on two important functions: natural language understanding (NLU) and dialog management (DM). The system was evaluated on 884 dialogues and achieved 85.9% transaction success.

本文设计了一个高性能的电子邮件过滤系统、该系统采用基于全信息的自然语言理解方法论、对邮件从语法（关键词过滤）、语义（主题过滤）、语用（倾向过滤）三个层次上进行过滤、从而尽量避免对正常邮件的误判和对非法邮件的漏判。

口语自然语言理解是口语对话系统中最重要的组成部分。本文提出了一种基于语义语法的语义分析和统计分类器相结合的口语理解方法。该方法分为两步：首先用语义分析得到句子的语义概念、然后用统计分类器得到句子的任务。将该方法应用于智能公交系统中、取得了很好的实验效果。

提出了一个基于统计的从未标注语料库中半自动获取语义语法算法。该算法对特定领域的语料库进行反复的时间聚类和空间聚类，通过时间聚类发现语言片段的语法结构，通过空间聚类发现语言片段的语义类别；循环迭代，可以生成一个粗糙的文法。最后，将这些抽取出来的粗糙文法经过人工校对，从而得到新领域的语义语法。实验结果表明，该方法是有效和切实可行的。

随着网络信息的日益增加和广泛传播，口语对话系统已经为越来越多的研究者所关注。本文主要介绍了一个天气预报口语对话系统的设计与实现，该系统可以实现对国内外150 个城市的天气情况的查询。在实验室环境下，该系统表现了良好的性能。