Harmonizing String and Unification-based Methodology with Machine Learning for Text Mining and Processing

本研究计划旨在从三个层面推进自然语言处理的最新技术，以满足更好的信息处理需求。在最低层次，即字符和单词n-gram级处理，我们的目标是通过使用变长n-gram配置文件来改进基于n-gram的文本挖掘，通过n-gram配置文件和相应的欧拉图的可视化来改进基于n-gram的可视化文本分析，在更深的模型层次上比较当前的CNG距离度量与其他度量（例如，Jaccard, Dice），使用谷歌n-gram数据来改进标准语言n-gram配置文件，适配归一化谷歌距离，实现脱机距离。在处理的中层（基于RegEx），我们将推进用于定向情感分析和噪声文本解析的正则表达式模式的开发，研究生成基于RegEx的模式的方法，从谷歌N-grams数据生成***模式，并扩展用于文本嵌入式处理的Starfish系统。在第三层，即统一层，我们的目标是：将子图同构技术从生物医学科学领域的分析转移到社交媒体的信息收集、维基百科数据的概念语义关系生成以及基于语义的流文本数据可视化，如电子邮件流的可视化。******我们的方法基于以下三个层次的语言处理的先前工作：(1)通用N-Gram分析（CNG），其中文本数据使用字符N-Gram配置文件建模；(2)基于正则表达式的文本数据处理，在应用RegEx重写模式的基础上，将数据与相似的模式进行匹配；(3)在统一层，采用统一或子图同构的方式进行信息提取和匹配，使用基于随机统一的语法进行解析，生成结构化数据本身。******该方法的新颖性和预期意义是基于改进方法来提供可视化文本分析，即可视化和与用户更紧密的交互，以及更好地适应和开发方法，以适应互联网数据和社交媒体的扩展所带来的新型文本数据和新颖应用。工业合作伙伴对社会媒体数据总结分析领域的强烈兴趣支持了这些方法的重要性。*****

This research proposal aims at advancing the state of the art in the natural language processing at three levels in order to meet demands for better information processing. At the lowest level, the character and word n-gram level processing, our objectives are to improve n-gram based text mining through the use of variable-length n-gram profiles, n-gram based visual text analytics through visualization of n-gram profiles and corresponding Eulerian graphs, comparison of current CNG distance measure with other measures (e.g., Jaccard, Dice) at a deeper model level, use of Google N-grams data in improving the standard language n-gram profiles, and adaptation of Normalized google Distance to achieve an off-line distance. At the middle level of processing (RegEx based), we will advance development of regular expression patterns for directed sentiment analysis and parsing of noisy text, examining the ways to generate RegEx-based patterns, generating***patterns from Google N-grams data, and extending the Starfish system for text-embedded processing. At the third level, the unification level, our bojectives are: to transfer sub-graph isomorphism technique from analysis in biomedical scientific domain to information gathering from social media, concept semantic relationship generation from Wikipedia data, and semantic-based visualization of stream textual data, such as visualization of e-mail streams.******Our Approach is based on the previous work ot these three levels of language processing: (1) Common N-Gram analysis (CNG), where the text data is modelled using character n-gram profiles; (2) Regular Expression based processing of textual data, based on applying RegEx rewriting patterns, and matching the data with similar patterns, and (3) at the Unification level, we apply information extraction and matching using unification or sub-graph isomorphism, and the structural data itself is generated by parsing using the stochastic unification-based grammars.******Novelty and Expected Significance of the approach is based on improving methodology to provide for visual text analysis, i.e., visualization and closer interaction with the user, and for better***adaptation and development of methodology for new kind of textual data and novel applications coming from the expansion of Internet data and social media. The significance of the approaches is supported by strong interest coming from industrial partners in the area of summarized analysis of social media data.*****