Learning to Use Normalization Techniques for Preprocessing and Classification of Text Documents

Text classification is the most substantial area in natural language processing. In this task, the text document is divided into various types according to the researcher’s purpose. In the text classification process, the basic phase is text preprocessing. In text preprocessing, cleaning, and preparing text data are significant tasks. To accomplish these tasks under the text preprocessing, normalization techniques play a major role. Different kinds of normalization techniques are available. In this research, we mainly focus on different normalization techniques and the way of applying them to text preprocessing. Normalization techniques reduce the words of the text files and change the word form to another form. It helps to analyze the unstructured texts and predefine the text into standard form. This causes to improve the efficiency and performance of the text classification process. For text classification, it is important to extract the most reliable and relevant words of the text files, because feature extraction causes successful classification. This study includes the lowercasing, tokenization, stop word removal, and lemmatization as normalization techniques. 200 text documents from two different domains, namely, formal news articles and informal letters obtained from the Internet in the English language were evaluated using these normalization techniques. The experimental results show the effectiveness of the use of normalization techniques for the preprocessing and classification of text documents and for comparison between before and after using normalization techniques to the text files. Based on the comparison, we identified that these normalization techniques help to clean and prepare text data for effective and accurate text document classification. KEYWORDS: Preprocessing, Normalization, Techniques, Cleaning documents, Text classificati

Network problems detection and classification by analyzing syslog data

Network troubleshooting is an important process which has a wide research field. The first step in troubleshooting procedures is to collect information in order to diagnose the problems. Syslog messages which are sent by almost all network devices contain a massive amount of data related to the network problems. It is found that in many studies conducted previously, analyzing syslog data which can be a guideline for network problems and their causes was used. Detecting network problems could be more efficient if the detected problems have been classified in terms of network layers. Classifying syslog data needs to identify the syslog messages that describe the network problems for each layer, taking into account the different formats of various syslog for vendors’ devices. This study provides a method to classify syslog messages that indicates the network problem in terms of network layers. The method used data mining tool to classify the syslog messages while the description part of the syslog message was used for classification process. Related syslog messages were identified; features were then selected to train the classifiers. Six classification algorithms were learned; LibSVM, SMO, KNN, Naïve Bayes, J48, and Random Forest. A real data set which was obtained from the Universiti Utara Malaysia’s (UUM) network devices is used for the prediction stage. Results indicate that SVM shows the best performance during the training and prediction stages. This study contributes to the field of network troubleshooting, and the field of text data classification

On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data

Multilabel classification is often hindered by incompletely labeled training datasets; for some items of such dataset (or even for all of them) some labels may be omitted. In this case, we cannot know if any item is labeled fully and correctly. When we train a classifier directly on incompletely labeled dataset, it performs ineffectively. To overcome the problem, we added an extra step, training set modification, before training a classifier. In this paper, we try two algorithms for training set modification: weighted k-nearest neighbor (WkNN) and soft supervised learning (SoftSL). Both of these approaches are based on similarity measurements between data vectors. We performed the experiments on AgingPortfolio (text dataset) and then rechecked on the Yeast (nontext genetic data). We tried SVM and RF classifiers for the original datasets and then for the modified ones. For each dataset, our experiments demonstrated that both classification algorithms performed considerably better when preceded by the training set modification step

A modular architecture for systematic text categorisation

This work examines and attempts to overcome issues caused by the lack of formal standardisation when defining text categorisation techniques and detailing how they might be appropriately integrated with each other. Despite text categorisation’s long history the concept of automation is relatively new, coinciding with the evolution of computing technology and subsequent increase in quantity and availability of electronic textual data. Nevertheless insufficient descriptions of the diverse algorithms discovered have lead to an acknowledged ambiguity when trying to accurately replicate methods, which has made reliable comparative evaluations impossible. Existing interpretations of general data mining and text categorisation methodologies are analysed in the first half of the thesis and common elements are extracted to create a distinct set of significant stages. Their possible interactions are logically determined and a unique universal architecture is generated that encapsulates all complexities and highlights the critical components. A variety of text related algorithms are also comprehensively surveyed and grouped according to which stage they belong in order to demonstrate how they can be mapped. The second part reviews several open-source data mining applications, placing an emphasis on their ability to handle the proposed architecture, potential for expansion and text processing capabilities. Finding these inflexible and too elaborate to be readily adapted, designs for a novel framework are introduced that focus on rapid prototyping through lightweight customisations and reusable atomic components. Being a consequence of inadequacies with existing options, a rudimentary implementation is realised along with a selection of text categorisation modules. Finally a series of experiments are conducted that validate the feasibility of the outlined methodology and importance of its composition, whilst also establishing the practicality of the framework for research purposes. The simplicity of experiments and results gathered clearly indicate the potential benefits that can be gained when a formalised approach is utilised

Semi-Automated Text Classification

There is currently a high demand for information systems that automatically analyze textual data, since many organizations, both private and public, need to process large amounts of such data as part of their daily routine, an activity that cannot be performed by means of human work only. One of the answers to this need is text classification (TC), the task of automatically labelling textual documents from a domain D with thematic categories from a predefined set C. Modern text classification systems have reached high efficiency standards, but cannot always guarantee the labelling accuracy that applications demand. When the level of accuracy that can be obtained is insufficient, one may revert to processes in which classification is performed via a combination of automated activity and human effort. One such process is semi-automated text classification (SATC), which we define as the task of ranking a set D of automatically labelled textual documents in such a way that, if a human annotator validates (i.e., inspects and corrects where appropriate) the documents in a top-ranked portion of D with the goal of increasing the overall labelling accuracy of D, the expected such increase is maximized. An obvious strategy is to rank D so that the documents that the classifier has labelled with the lowest confidence are top-ranked. In this dissertation we show that this strategy is suboptimal. We develop new utility-theoretic ranking methods based on the notion of validation gain, defined as the improvement in classification effectiveness that would derive by validating a given automatically labelled document. We also propose new effectiveness measures for SATC-oriented ranking methods, based on the expected reduction in classification error brought about by partially validating a ranked list generated by a given ranking method. We report the results of experiments showing that, with respect to the baseline method above, and according to the proposed measures, our utility-theoretic ranking methods can achieve substantially higher expected reductions in classification error. We therefore explore the task of SATC and the potential of our methods, in multiple text classification contexts. This dissertation is, to the best of our knowledge, the first to systematically address the task of semi-automated text classification