Analysis and implementation of methods for the text categorization

Text Categorization (TC) is the automatic classification of text documents under pre-defined categories, or classes. Popular TC approaches map categories into symbolic labels and use a training set of documents, previously labeled by human experts, to build a classifier which enables the automatic TC of unlabeled documents. Suitable TC methods come from the field of data mining and information retrieval, however the following issues remain unsolved. First, the classifier performance depends heavily on hand-labeled documents that are the only source of knowledge for learning the classifier. Being a labor-intensive and time consuming activity, the manual attribution of documents to categories is extremely costly. This creates a serious limitations when a set of manual labeled data is not available, as it happens in most cases. Second, even a moderately sized text collection often has tens of thousands of terms in that making the classification cost prohibitive for learning algorithms that do not scale well to large problem sizes. Most important, TC should be based on the text content rather than on a set of hand-labeled documents whose categorization depends on the subjective judgment of a human classifier. This thesis aims at facing the above issues by proposing innovative approaches which leverage techniques from data mining and information retrieval. To face problems about both the high dimensionality of the text collection and the large number of terms in a single text, the thesis proposes a hybrid model for term selection which combines and takes advantage of both filter and wrapper approaches. In detail, the proposed model uses a filter to rank the list of terms present in documents to ensure that useful terms are unlikely to be screened out. Next, to limit classification problems due to the correlation among terms, this ranked list is refined by a wrapper that uses a Genetic Algorithm (GA) to retaining the most informative and discriminative terms. Experimental results compare well with some of the top-performing learning algorithms for TC and seems to confirm the effectiveness of the proposed model. To face the issues about the lack and the subjectivity of manually labeled datasets, the basic idea is to use an ontology-based approach which does not depend on the existence of a training set and relies solely on a set of concepts within a given domain and the relationships between concepts. In this regard, the thesis proposes a text categorization approach that applies WordNet for selecting the correct sense of words in a document, and utilizes domain names in WordNet Domains for classification purposes. Experiments show that the proposed approach performs well in classifying a large corpus of documents. This thesis contributes to the area of data mining and information retrieval. Specifically, it introduces and evaluates novel techniques to the field of text categorization. The primary objective of this thesis is to test the hypothesis that: text categorization requires and benefits from techniques designed to exploit document content. hybrid methods from data mining and information retrieval can better support problems about high dimensionality that is the main aspect of large document collections. in absence of manually annotated documents, WordNet domain abstraction can be used that is both useful and general enough to categorize any documents collection. As a final remark, it is important to acknowledge that much of the inspiration and motivation for this work derived from the vision of the future of text categorization processes which are related to specific application domains such as the business area and the industrial sectors, just to cite a few. In the end, it is this vision that provided the guiding framework. However, it is equally important to understand that many of the results and techniques developed in this thesis are not limited to text categorization. For example, the evaluation of disambiguation methods is interesting in its own right and is likely to be relevant to other application fields

Analysis and implementation of methods for the text categorization

Text Categorization (TC) is the automatic classification of text documents under pre-defined categories, or classes. Popular TC approaches map categories into symbolic labels and use a training set of documents, previously labeled by human experts, to build a classifier which enables the automatic TC of unlabeled documents. Suitable TC methods come from the field of data mining and information retrieval, however the following issues remain unsolved. First, the classifier performance depends heavily on hand-labeled documents that are the only source of knowledge for learning the classifier. Being a labor-intensive and time consuming activity, the manual attribution of documents to categories is extremely costly. This creates a serious limitations when a set of manual labeled data is not available, as it happens in most cases. Second, even a moderately sized text collection often has tens of thousands of terms in that making the classification cost prohibitive for learning algorithms that do not scale well to large problem sizes. Most important, TC should be based on the text content rather than on a set of hand-labeled documents whose categorization depends on the subjective judgment of a human classifier. This thesis aims at facing the above issues by proposing innovative approaches which leverage techniques from data mining and information retrieval. To face problems about both the high dimensionality of the text collection and the large number of terms in a single text, the thesis proposes a hybrid model for term selection which combines and takes advantage of both filter and wrapper approaches. In detail, the proposed model uses a filter to rank the list of terms present in documents to ensure that useful terms are unlikely to be screened out. Next, to limit classification problems due to the correlation among terms, this ranked list is refined by a wrapper that uses a Genetic Algorithm (GA) to retaining the most informative and discriminative terms. Experimental results compare well with some of the top-performing learning algorithms for TC and seems to confirm the effectiveness of the proposed model. To face the issues about the lack and the subjectivity of manually labeled datasets, the basic idea is to use an ontology-based approach which does not depend on the existence of a training set and relies solely on a set of concepts within a given domain and the relationships between concepts. In this regard, the thesis proposes a text categorization approach that applies WordNet for selecting the correct sense of words in a document, and utilizes domain names in WordNet Domains for classification purposes. Experiments show that the proposed approach performs well in classifying a large corpus of documents. This thesis contributes to the area of data mining and information retrieval. Specifically, it introduces and evaluates novel techniques to the field of text categorization. The primary objective of this thesis is to test the hypothesis that: text categorization requires and benefits from techniques designed to exploit document content. hybrid methods from data mining and information retrieval can better support problems about high dimensionality that is the main aspect of large document collections. in absence of manually annotated documents, WordNet domain abstraction can be used that is both useful and general enough to categorize any documents collection. As a final remark, it is important to acknowledge that much of the inspiration and motivation for this work derived from the vision of the future of text categorization processes which are related to specific application domains such as the business area and the industrial sectors, just to cite a few. In the end, it is this vision that provided the guiding framework. However, it is equally important to understand that many of the results and techniques developed in this thesis are not limited to text categorization. For example, the evaluation of disambiguation methods is interesting in its own right and is likely to be relevant to other application fields

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

Automatic categorization of diverse experimental information in the bioscience literature

Background: Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. Results: We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction. Conclusions: Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems