definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We

Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison

Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from

methods in conjunction with a notion of differential misclassification cost to produce filters Which are especially appropriate for the nuances of this task. While this may appear, at first, to be a straight-forward text classification problem, we show that by considering domain-specific features

Much work has been done on the statistical analysis of text. In some cases reported in the literature, inappropriate statistical methods have been used, and statistical significance of results have not been addressed. In particular, asymptotic normality assumptions have often been used

Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized

agents that communi-cate by placing, checking and instantiating constraints on shared variables. Such a view of computation is in-teresting in the context of programming languages be-cause of the ability to represent and manipulate partial information about the domain of discourse, in the con-text

. A wealth of on-line text information can be made available to automatic processing by information extraction (IE) systems. Each IE application needs a separate set of rules tuned to the domain and writing style. WHISK helps to overcome this knowledge-engineering bottleneck by learning text

Having seen a news title “Alba denies wedding reports”, how do we infer that it is primarily about Jessica Alba, rather than about weddings or reports? We probably realize that, in a randomly driven sentence, the word “Alba ” is less anticipated than “wedding ” or “reports”, which adds value to the word “Alba ” if used. Such anticipation can be modeled as a ratio between an empirical probability of the word (in a given corpus) and its estimated probability in general English. Aggregated over all words in a document, this ratio may be used as a measure of the document’s topicality. Assuming that the corpus consists of on-topic and off-topic documents (we call them the core and the noise), our goal is to determine which documents belong to the core. We propose two unsupervised methods for doing this. First, we assume that words are sampled i.i.d., and propose an information-theoretic framework for determining the core. Second, we relax the independence assumption and use a simple graphical model to rank documents according to their likelihood of belonging to the core. We discuss theoretical guarantees of the proposed methods and show their usefulness

Scientific texts domain keyword is one of the basic elements of the text high-level semantics acquisition, domain ontology building and the knowledge representation in semantic grid, knowledge grid and escience environment. It is also the indispensable foundation and prerequisite work of Web