Document categorization

This paper describes a study aimed at identifying different profiles of students' project development processes. Specifically, we assessed the use of abstract data types for the development of knowledge-based projects. The concept of abstract data types was introduced to high school students who took the course "Computer Science-Logic Programming". During their studies the students learned and practiced various tools and

Document image classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. There is great diversity in document image classifiers: they differ in the problems they solve, in the use of training data to construct class models, and in the choice of document features and classification algorithms. We survey this diverse literature using three components: the problem statement, the classifier architecture, and performance evaluation. This brings to light important issues in designing a document classifier, including the definition of document classes, the choice of document features and feature representation, and the choice of classification algorithm and learning mechanism. We emphasize techniques that classify single-page typeset document images without using OCR results. Developing a general, adaptable, high-performance classifier is challenging due to the great variety of documents, the diverse criteria used to define document classes, and the ambiguity that arises due to ill-defined or fuzzy document classes.

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.

Document categorization is a technique through which the category of a document is determined. This paper deals with the automatic classification of Bangla documents. In this proposed categorization system, a support vector machine is used for classifying a document in predefine twelve categories. In this classification model TFIDF (term frequency-inverse document frequency) weighting with length normalization is used for feature selection after the preprocessing of data set is complete. It is shown that the results achieved by applying SVM to classify the category of a Bangla document are very promising as compared to conventional methods where features are chosen on the basis of bag-of-words. The accuracy of this proposed methodology is 92.57% for twelve categories.

Automatic text categorization is a primary step in information retrieval where it is necessary to find the most relevant documents in an enormous volume. It is also useful in a wide range of web domains, such as from portal sites to news indexing, or from spam filtering to genre tagging. A significant amount of research works has been carried out in this field, and they are mostly dominated by Support Vector Machines (SVMs) models. Although these models have been very successful, but they require careful feature engineering to achieve optimum results. In this paper, we propose a model for Bengali text categorization that doesn't require feature engineering and is able to capture nonlinearity in data. We had first found a lower dimensional representation for the tf-idf vectors of each document using denoising autoencoders, and then we fed this transformed domain data vector into a deep feedforward network to find its most plausible category. We also show empirically that our model achieves 94.05% accuracy for 12 categories that surmounts the best existing models on Bengali text categorization.

Named entity recognition is a preprocessing tool to many natural language processing tasks, such as text summarization, speech translation, and document categorization. Many systems for named entity recognition have been developed over the past years with substantial success save for the problem of being domain specific and making it difficult to use the different systems across domains. This work attempts to surmount the problem by proposing the use of domain independent features with a maximum entropy model and a multiobjective genetic algorithm (MOGA) to select the best features. The methods used in this work are backed up by experiments of which the classifications are evaluated using two diverse domains. Conclusions are finally drawn and the outlook for future work is considered.

Word fragments or n-grams have been widely used to perform different Natural Language Processing tasks such as information retrieval [1] [2], document categorization [3], automatic summarization [4] or, even, genetic classification of languages [5]. All these techniques share some common aspects such as: (1) documents are mapped to a vector space where n-grams are used as coordinates and their relative frequencies as vector weights, (2) many of them compute a context which plays a role similar to stop-word lists, and (3) cosine distance is commonly used for document-to-document and query-to-document comparisons. blindLight is a new approach related to these classical n-gram techniques although it introduces two major differences: (1) Relative frequencies are no more used as vector weights but replaced by n-gram significances, and (2) cosine distance is abandoned in favor of a new metric inspired by sequence alignment techniques although not so computationally expensive. This new approach can be simultaneously used to perform document categorization and clustering, information retrieval, and text summarization. In this paper we will describe the foundations of such a technique and its application to both a particular categorization problem (i.e., language identification) and information retrieval tasks.

In the process of preparing learning material for Computer Supported Learning Systems (CSLSs), one of the first steps involves finding documents relevant to the topics and to the students. This requires documents to be categorized according to some criteria. In this paper we analyze the behaviour of classification techniques such as Naı̈ve Bayes, Winnow, SVMs and k-NN, together with lemmatization and noun selection, in the categorization of documents written in Basque. In a second experiment, we study the effect of applying the Singular Value Decomposition (SVD) dimensionality reduction technique before using the mentioned classification techniques. The results obtained show that the approach which combines SVD and k-NN for a lemmatized corpus gives the best categorization of all with a remarkable difference. The final aim pursued in this project is to facilitate the semiautomatic construction of the domain module of a CSLS.

Automatic document classification due to its various applications in data mining and information technology is one of the important topics in computer science. Classification plays a vital role in many information management and retrieval tasks. Document classification, also known as document categorization, is the process of assigning a document to one or more predefined category labels. Classification is often posed as a supervised learning problem in which a set of labeled data is used to train a classifier which can be applied to label future examples [1]. Document classification includes different parts such as text processing, feature extraction, feature vector construction and final classification. Thus improvement in each part should lead to better results in document classification. In this paper, we apply machine learning methods for automatic Persian news classification. In this regard, we first try to exert some language preprocess in Hamshahri dataset [2], and then we e...

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers perform adequately on constrained handwritten documents which typically use a restricted vocabulary (lexicon). But in the case of unconstrained handwritten documents, state-of-the-art word recognition accuracy is still below the acceptable limits. The objective of this research is to improve word recognition accuracy on unconstrained handwritten documents by applying a post-processing or OCR correction technique to the word recognition output. In this paper, we present two different methods for this purpose. First, we describe a lexicon reduction-based method by topic categorization of handwritten documents which is used to generate smaller topic-specific lexicons for improving the recognition accuracy. Second, we describe a method which uses topic-specific language models and a maximum-entropy based topic categorization model to refine the recognition output. We present the relative merits of each of these methods and report results on the publicly available IAM database.

Information about the category (type) of a WWW page can be helpful for the user within search, filtering, as well as navigation tasks. We propose a multidimensional categorisation scheme, with bibliographic dimension as the primary one. We examine the possibilities and limits of performing such categorisation based on information extracted from URL, which is particularly useful for certain on-line applications such as meta-search or navigation support. In addition, we describe the problem of ambiguity of URL terms, and suggest a method for its partial overcoming by means of machine learning. As a side--effect, we show that general purpose WWW search engines can be used for providing input data for both human and computational analysis of the web. 1 Introduction The task of document categorisation is common within web applications, in particular for navigational, search and filtering systems, which give access to large amounts of documents. The aim of the categorisation may be . to e...

Experiments were conducted to test several hypotheses on methods for improving document classification for the malicious insider threat problem within the Intelligence Community. Bag-of-words (BOW) representations of documents were compared to Natural Language Processing (NLP) based representations in both the typical and one-class classification problems using the Support Vector Machine algorithm. Results show that the NLP features significantly improved classifier performance over the BOW approach both in terms of precision and recall, while using many fewer features. The one-class algorithm using NLP features demonstrated robustness when tested on new domains. 1

Experiments were conducted to test several hypotheses on methods for improving document categorization for the malicious insider threat problem within the Intelligence Community. Bag-of-words (BOW) representations of documents were compared to Natural Language Processing (NLP) based representations in both the typical and one-class categorization problems using the Support Vector Machine algorithm. Results from our Semantic Anomaly Monitoring (SAM) system show that the NLP features significantly improved classifier performance over the BOW approach both in terms of precision and recall, while using many fewer features. The oneclass algorithm using NLP features demonstrated robustness when tested on new domains.

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.

Introduction The traditional approach to document categorization is categorization by content, since information for categorizing a document is extracted from the document itself. In a hypertext environment like the Web, the structure of documents and the link topology can be exploited to perform what we call categorization by context [Attardi 98]: the context surrounding a link in an HTML document

Today, there is an increasing demand of efficient archival and retrieval methods for online handwritten data. For such tasks, text categorization is of particular interest. The textual data available in online documents can be extracted through online handwriting recognition; however, this process produces errors in the resulting text. This work reports experiments on the categorization of online handwritten documents based