文本知识的自动分类方法初探

本文结合运用信息管理和人工智能的原理与技术，探讨了文本知识的自动分类方法，包括：自动归类与聚类方法、基于实例的学习分类方法和基于特征值的元学习方法。     

模糊综合评价法在高校内部审计风险评价中的应用

首先分析了应用模糊综合评价法的可行性，然后利用模糊综合评价方法对高校内部审计风险进行评价，并予以实例详细说明，以达到有效控制内部审计风险的目的。     

Application of machine learning techniques to assess the trends and alignment of the funded research output

Research and development activities are regarded as one of the most influencing factors of the future of a country. Large investments in research can yield a tremendous outcome in terms of a country’s overall wealth and strength. However, public financial resources of countries are often limited which calls for a wise and targeted investment. Scientific publications are considered as one of the main outputs of research investment. Although the general trend of scientific publications is increasing, a detailed analysis is required to monitor the research trends and assess whether they are in line with the top research priorities of the country. Such focused monitoring can shed light on scientific activities evolution as well as the formation of new research areas, thus helping governments to adjust priorities, if required. But monitoring the output of the funded research manually is not only very expensive and difficult, it is also subjective. Using structural topic models, in this paper we evaluated the trends in academic research performed by federally funded Canadian researchers during the time-frame of 2000–2018, covering more than 140,000 research publications. The proposed approach makes it possible to objectively and systematically monitor research projects, or any other set of documents related to research activities such as funding proposals, at large-scale. Our results confirm the accordance between the performed federally funded research projects and the top research priorities of Canada.     

A new unsupervised method for document clustering by using WordNet lexical and conceptual relations

Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step we have chosen the bisecting k-means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and speed.

Extending WHIRL with background knowledge for improved text classification

Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular problem on the World Wide Web.

寓于语境的听力教学

语言的理解与语境是密不可分的。在英语教学中,听力理解作为一项输入性技能,它是实现用英语进行交际的基础,听力水平也成了衡量外语学习者总体语言能力的一个重要因素。然而在实际的课堂教学中,许多老师会发现尽管听力材料简单,语法、单词都没有障碍,但是学生却只能获得一些零散的信息,无法利用这些零散的语素组合出完整的意义。由于不知如何解读组合所得的信息,而常常使学生听力结束后陷入一种＂混沌＂的状态。为了解决这一问题,在研究听力教学及语境理论的基础上,教学者应结合自身教学经验,将听力教学巧妙地寓于语境之中。     

马克思主义哲学视角下的语篇语言学

语篇语言学又称话语语言学,兴起于20世纪70年代,是现代语言学的重要分支之一,它的诞生与发展,使语言学的研究脱离了形式一逻辑的固有模式,从语言的本质出发去研究语言的基本规律。语言学的这个进步,反映了语言学家们从哲学层面对语言的重新审视,不再把语言看作是一成不变的形式化的内容,而是从语言作为人类的交际手段这一基本属性的角度来研究语言本身。从马克思主义哲学的视角来审视语篇语言学的诞生、研究对象和研究方法,无疑能够更好地探究语篇语言学的哲学基础。     

Social media opinion summarization using emotion cognition and convolutional neural networks

Quickly and accurately summarizing representative opinions is a key step for assessing microblog sentiments. The Ortony-Clore-Collins (OCC) model of emotion can offer a rule-based emotion export mechanism. In this paper, we propose an OCC model and a Convolutional Neural Network (CNN) based opinion summarization method for Chinese microblogging systems. We test the proposed method using real world microblog data. We then compare the accuracy of manual sentiment annotation to the accuracy using our OCC-based sentiment classification rule library. Experimental results from analyzing three real-world microblog datasets demonstrate the efficacy of our proposed method. Our study highlights the potential of combining emotion cognition with deep learning in sentiment analysis of social media data.     

Authorship attribution based on a probabilistic topic model

This paper describes, evaluates and compares the use of Latent Dirichlet allocation (LDA) as an approach to authorship attribution. Based on this generative probabilistic topic model, we can model each document as a mixture of topic distributions with each topic specifying a distribution over words. Based on author profiles (aggregation of all texts written by the same writer) we suggest computing the distance with a disputed text to determine its possible writer. This distance is based on the difference between the two topic distributions. To evaluate different attribution schemes, we carried out an experiment based on 5408 newspaper articles (Glasgow Herald) written by 20 distinct authors. To complement this experiment, we used 4326 articles extracted from the Italian newspaper La Stampa and written by 20 journalists. This research demonstrates that the LDA-based classification scheme tends to outperform the Delta rule, and the χ² distance, two classical approaches in authorship attribution based on a restricted number of terms. Compared to the Kullback–Leibler divergence, the LDA-based scheme can provide better effectiveness when considering a larger number of terms.     

基于粗集的支持向量机文本分类方法研究

本文提出了一种基于粗糙集的支持向量机文本分类方法。该方法利用粗糙集的约简理论降低了支持向量的维数,同时保证了分类性能。实验表明该方法能获得较好的分类效果。