Classification task is an integral part of named entity recognition system to classify a recognized named entity to its corresponding class. This task has not received much attention in the biomedical domain, due to the lack of awareness to differentiate feature sources and strategies in previous studies. In this research, we analyze different sources and strategies of protein name classification, and developed integrated strategies that incorporate advantages from rule-based, dictionary-based and statistical-based method. In rule-based method, terms and knowledge of protein nomenclature that provide strong cue for protein name are used. In dictionary-based method, a set of rules for curating protein name dictionary are used. These terms and dictionaries are combined with our developed features into a statistical-based classifier. Our developed features are comprised of word shape features and unigram & bi-gram features. Our various information sources and integrated strategies are able to achieve state-of-the-art performance to classify protein and non-protein names.

This paper is about the segmentation of Braille words and the transformation from Mandarin Braille to Chinese characters. Braille word segmentation consists of rules base, the signs base of segmentation and knowledge base for disambiguation and mistakes, by using adjacency constraints and bidirectional maximum matching with a dictionary, our system's segmentation precision is better than 99% for the common text. By incorporating a pinyin knowledge dictionary into the system, we perfectly solved the problem of ambiguity in the translation from Braille to pinyin and developed a statistical language model based on the transformation of pinyin into characters. By using a multi-knowledge base to carry out the disambiguation process for each pinyin sentence, we built a multi-level graph and used Viterbi search to find the sequence of Chinese characters with maximum likelihood, and used an N-Best algorithm to get the N most likely character sequences. The experimental results show that the system's overall precision for translation from Braille codes to Chinese characters is 94.38%.

近年来基于隐马尔可夫模型（HMM）的语音识别技术得到很大发展。然而HMM模型有着一定的局限性，如何克服HMM的一阶假设和独立性假设带来的问题一直是研究讨论的热点。在语音识别中引入神经网络的方法是克服HMM局限性的一条途径。该文将循环神经网络应用于汉语语音识别，修改了原网络模型并提出了相应的训练方法。实验结果表明该模型具有良好的连续信号处理性能，与传统的HMM模型效果相当。新的训练策略能够在提高训练速度的同时，使得模型分类性能有明显提高。

本文介绍了智能技术与系统国家重点实验室开发的“北极光”盲人用计算机软件系统中涉及的语音和语言处理技术。该系统能够获取和分析需要反馈的屏幕信息，通过语音合成平台将其内容朗读出来，对用户进行语音提示；与汉语自动分词、语言模型等自然语言处理技术的结合，使系统能够进行汉字和盲文的转换，反馈信息可以通过盲文点显器输出，使用户能够摸读盲文点字来获取所需要的信息，用户也可以采用盲文输入法进行输入，输入结果可转换为汉字文本形式。

该文提出了一种基于反馈的手写体字符识别方法。该方法将人工神经网络结构及学习算法运用于系统反馈机制中，并从理论上证明了该学习方法是可收敛的，保证了算法的有效性。同时给出反馈的可视化约束及反馈的判别准则。试验结果证明了该方法大大降低了高噪音手写体数字的识别率。该方法指出了一条进一步提高手写体字符系统性能的新途径。