Rcv1: A new benchmark collection for text categorization research

Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which the data was produced. Drawing on interviews with Reuters personnel and access to Reuters documentation, we describe the coding policy and quality control procedures used in producing the RCV1 data, the intended semantics of the hierarchical category taxonomies, and the corrections necessary to remove errorful data. We refer to the original data as RCV1-v1, and the corrected data as RCV1-v2. We benchmark several widely used supervised learning methods on RCV1-v2, illustrating the collection’s properties, suggesting new directions for research, and providing baseline results for future studies. We make available detailed, per-category experimental results, as well a

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

N-gram Based Text Categorization Method for Improved Data Mining

Though naïve Bayes text classifiers are widely used because of its simplicity and effectiveness, the techniques for improving performances of these classifiers have been rarely studied. Naïve Bayes classifiers which are widely used for text classification in machine learning are based on the conditional probability of features belonging to a class, which the features are selected by feature selection methods. However, its performance is often imperfect because it does not model text well, and by inappropriate feature selection and some disadvantages of the Naive Bayes itself. Sentiment Classification or Text Classification is the act of taking a set of labeled text documents, learning a correlation between a document’s contents and its corresponding labels and then predicting the labels of a set of unlabeled test documents as best as possible. Text Classification is also sometimes called Text Categorization. Text classification has many applications in natural language processing tasks such as E-mail filtering, Intrusion detection systems, news filtering, prediction of user preferences, and organization of documents. The Naive Bayes model makes strong assumptions about the data: it assumes that words in a document are independent. This assumption is clearly violated in natural language text: there are various types of dependences between words induced by the syntactic, semantic, pragmatic and conversational structure of a text. Also, the particular form of the probabilistic model makes assumptions about the distribution of words in documents that are violated in practice. We address this problem and show that it can be solved by modeling text data differently using N-Grams. N-gram Based Text Categorization is a simple method based on statistical information about the usage of sequences of words. We conducted an experiment to demonstrate that our simple modification is able to improve the performance of Naive Bayes for text classification significantly. Keywords: Data Mining, Text Classification, Text Categorization, Naïve Bayes, N-Grams

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

An empirical analysis of information filtering methods

The growth in the the number of news articles, blogs, images, and videos available on the Web is making if more challenging for people to find potentially useful information People have relied on search engines to satisfy their short-term needs, such as finding the telephone number for a restaurant; however, these systems have not been designed to support long-term needs, such as the research interests of academics. One approach to supporting long-term needs is to use an Information Filtering system to select potentially useful information from the vast amount being produced everyday. The similarities between Information Retrieval systems and Information Filtering systems are well-established. They have prompted the use of retrieval models and methods in filtering systems, which has had some success but has been criticised as a limiting factor due to the unique challenges of document filtering. A significant difference between these systems is the use case: a filtering system is intended to push information to the user over a period of time, whereas a retrieval system is intended for the user to pull information to themselves for immediate use. The main challenge that needs to be addressed by a filtering system is the transient nature of the information published on the Web and the drifting nature of information needs. These factors lead to an uncertain interplay between the components comprising a filtering system and this thesis presents an empirical analysis of how the main system components affect performance. The analysis explores the role of each system component independently and in conjunction with other components. The main contribution of this thesis is a deeper understanding of how different components affect performance and the interplay between these components. The outcome of this thesis intends to act as a guide for both practitioners and researchers interested in overcoming some of the challenges of building filtering system

Machine learning on Web documents

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 111-115).The Web is a tremendous source of information: so tremendous that it becomes difficult for human beings to select meaningful information without support. We discuss tools that help people deal with web information, by, for example, blocking advertisements, recommending interesting news, and automatically sorting and compiling documents. We adapt and create machine learning algorithms for use with the Web's distinctive structures: large-scale, noisy, varied data with potentially rich, human-oriented features. We adapt two standard classification algorithms, the slow but powerful support vector machine and the fast but inaccurate Naive Bayes, to make them more effective for the Web. The support vector machine, which cannot currently handle the large amount of Web data potentially available, is sped up by "bundling" the classifier inputs to reduce the input size. The Naive Bayes classifier is improved through a series of three techniques aimed at fixing some of the severe, inaccurate assumptions Naive Bayes makes. Classification can also be improved by exploiting the Web's rich, human-oriented structure, including the visual layout of links on a page and the URL of a document. These "tree-shaped features" are placed in a Bayesian mutation model and learning is accomplished with a fast, online learning algorithm for the model. These new methods are applied to a personalized news recommendation tool, "the Daily You." The results of a 176 person user-study of news preferences indicate that the new Web-centric techniques out-perform classifiers that use traditional text algorithms and features. We also show that our methods produce an automated ad-blocker that performs as well as a hand-coded commercial ad-blocker.by Lawrence Kai Shih.Ph.D

Automatic text categorisation of racist webpages

Automatic Text Categorisation (TC) involves the assignment of one or more predefined categories to text documents in order that they can be effectively managed. In this thesis we examine the possibility of applying automatic text categorisation to the problem of categorising texts (web pages) based on whether or not they are racist. TC has proven successful for topic-based problems such as news story categorisation. However, the problem of detecting racism is dissimilar to topic-based problems in that lexical items present in racist documents can also appear in anti-racist documents or indeed potentially any document. The mere presence of a potentially racist term does not necessarily mean the document is racist. The difficulty is finding what discerns racist documents from non-racist. We use a machine learning method called Support Vector Machines (SVM) to automatically learn features of racism in order to be capable of making a decision about the target class of unseen documents. We examine various representations within an SVM so as to identify the most effective method for handling this problem. Our work shows that it is possible to develop automatic categorisation of web pages, based on these approache