中英文文本分类系统异同因素的探讨

论文介绍了文本分类技术的研究进展及发展方向.给出了中英文文本分类系统构建的一般模型和过程,分析总结了中英文文本分类过程中的异同,并介绍了针对这些异同因素常用的处理方法.     

Equilibrium and dynamic methods when comparing an English text and its Esperanto translation

A comparison of two English texts written by Lewis Carroll, one (Alice in Wonderland), also translated into Esperanto, the other (Through the Looking Glass) are discussed in order to observe whether natural and artificial languages significantly differ from each other. One dimensional time series like signals are constructed using only word frequencies (FTS) or word lengths (LTS). The data is studied through (i) a Zipf method for sorting out correlations in the FTS and (ii) a Grassberger-Procaccia (GP) technique based method for finding correlations in LTS. The methods correspond to an equilibrium and a dynamic approach respectively to human texts features. There are quantitative statistical differences between the original English text and its Esperanto translation, but the qualitative differences are very minutes. However different power laws are observed with characteristic exponents for the ranking properties, and the phase space attractor dimensionality. The Zipf exponent can take values much less than unity (∼0.50 or 0.30) depending on how a sentence is defined. This variety in exponents can be conjectured to be an intrinsic measure of the book style or purpose, rather than the language or author vocabulary richness, since a similar exponent is obtained whatever the text. Moreover the attractor dimension r is a simple function of the so called phase space dimension n, i.e., r=n^λ, with λ=0.79. Such an exponent could also be conjectured to be a measure of the author style versatility, — here well preserved in the translation.     

Technical structure of the global nanoscience and nanotechnology literature

Text mining was used to extract technical intelligence from the open source global nanotechnology and nanoscience research literature. An extensive nanotechnology/nanoscience-focused query was applied to the Science Citation Index/Social Science Citation Index (SCI/SSCI) databases. The nanotechnology/nanoscience research literature technical structure (taxonomy) was obtained using computational linguistics/document clustering and factor analysis. The infrastructure (prolific authors, key journals/institutions/countries, most cited authors/journals/documents) for each of the clusters generated by the document clustering algorithm was obtained using bibliometrics. Another novel addition was the use of phrase auto-correlation maps to show technical thrust areas based on phrase co-occurrence in Abstracts, and the use of phrase–phrase cross-correlation maps to show technical thrust areas based on phrase relations due to the sharing of common co-occurring phrases. The ∼400 most cited nanotechnology papers since 1991 were grouped, and their characteristics generated. Whereas the main analysis provided technical thrusts of all nanotechnology papers retrieved, analysis of the most cited papers allowed their characteristics to be displayed. Finally, most cited papers from selected time periods were extracted, along with all publications from those time periods, and the institutions and countries were compared based on their representation in the most cited documents list relative to their representation in the most publications list.     

分布式海量文本检索系统研究

文章利用分布式中间件技术和数据库技术实现海量数据高速加载与快速检索系统,针对数据库系统文本索引较慢等问题,采用并行索引方式来实现了一种可线性扩展的分布式海量文本数据检索系统。     

语音芯片CMS32003N的应用

本文章介绍了智能语音芯片CMS32 0 0 3N的功能特点及应用方法和范围 ,从具体应用的角度分析其优缺点 ,科学地阐述了应用的对象和前景     

Identification of key cyberbullies: A text mining and social network analysis approach

Cyberbullying is a major problem in society, and the damage it causes is becoming increasingly significant. Previous studies on cyberbullying focused on detecting and classifying malicious comments. However, our study focuses on a substantive alternative to block malicious comments via identifying key offenders through the application of methods of text mining and social network analysis (SNA). Thus, we propose a practical method of identifying social network users who make high rates of insulting comments and analyzing their resultant influence on the community. We select the Korean online community of Daum Agora to validate our proposed method. We collect over 650,000 posts and comments via web crawling. By applying a text mining method, we calculate the Losada ratio, a ratio of positive-to-negative comments. We then propose a cyberbullying index and calculate it based on text mining. By applying the SNA method, we analyze relationships among users so as to ascertain the influence that the core users have on the community. We validate the proposed method of identifying key cyberbullies through a real-world application and evaluations. The proposed method has implications for managing online communities and reducing cyberbullying.     

基于学生笔记的MOOC智能纲要生成方法

本文通过MOOC平台中自动控制原理在线课程中收集的学生即时笔记为例,分析背景语料作为笔记语料的补充,提出了笔记向量化方法,将即时笔记时间与文本内容结合进行聚类,最后对每一类笔记进行自动摘要,从而完成了整个视频的纲要自动生成任务。     

Conditional random fields for entity extraction and ontological text coding

Previous research suggests that one field with a strong yet unsatisfied need for automatically extracting instances of various entity classes from texts is the analysis of socio-technical systems (Feldstein in Media in Transition MiT5, 2007; Hampe et al. in Netzwerkanalyse und Netzwerktheorie, 2007; Weil et al. in Proceedings of the 2006 Command and Control Research and Technology Symposium, 2006; Diesner and Carley in XXV Sunbelt Social Network Conference, 2005). Traditional as well as non-traditional and customized sets of entity classes and the relationships between them are often specified in ontologies or taxonomies. We present a Conditional Random Fields (CRF)-based approach to distilling a set of entities that are defined in an ontology originating from organization science. CRF, a supervised sequential machine learning technique, facilitates the derivation of relational data from corpora by locating and classifying instances of various entity classes. The classified entities can be used as nodes for the construction of socio-technical networks. We find the outcome sufficiently accurate (82.7 percent accuracy of locating and classifying entities) for future application in the described problem domain. We propose using the presented methodology as a crucial step in the process of advanced modeling and analysis of complex and dynamic networks.