Text Categorization

Learning problems in the text processing domain often map the text to a space whose dimensions are the measured fea- tures of the text, e.g., its words. Three characteristic properties …...

In this paper) we propose a method for text categoriza- Lion task using term weight learning. In our approach, learning is to learn true keywords from the error of clus- …...

We consider the problem of assigning level numbers (weights) to hierarchically organized categories during text categorization. These levels control the ability of the categories to attract documents during the catego- …...

A maximum entropy (ME) model is usu- ally estimated so that it conforms to equal- ity constraints on feature expectations. However, the equality constraint is inap- propriate for sparse and …...

Today, corpus plays an important role in development and evaluation language and speech technologies, such as part of speech tagging, parsing, word sense disambiguation, text categorization, named entity classification, information …...

In Chinese, anaphors are frequently omitted, termed zero anaphor (ZA), from text due to their prominence. Thus the information carried by ZAs in text can not be used to contribute …...

Sentiment analysis seeks to identify the view- point(s) underlying a text span; an example appli- cation is classifying a movie review as “thumbs up” or “thumbs down”. To determine this …...

A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, …...

The identification of authorship falls into the category of style classification, an interesting sub-field of text categorization that deals with properties of the form of linguistic expression as opposed to …...

We propose one type of TOP (Tangent vector Of the Posterior log-odds) kernel and apply it to text categorization. In a number of categoriza- tion tasks including text categorization, nega- …...

This paper investigates the use of concept- based representations for text categoriza- tion. We introduce a new approach to cre- ate concept-based text representations, and apply it to a standard …...

In this paper we propose and evaluate a technique to perform semi-supervised learning for Text Categorization. particular we defined a kernel function,In namely the Domain Kernel, that allowed us to …...

AbstractSentiment analysis dealing with the identification and evaluation of opinions towards a topic, a company, or a product is an essential task within media analysis. It is used to study …...

This paper presents a study on if and how automatically extracted keywords can be used to improve text categorization. In summary we show that a higher perfor- mance — as …...

. VSM is a mature model of text representation for categorization. Words are commonly used as dimensions of feature space of VSM, but words only provide little semantic information. Sentence …...

In text categorization, term selection is an important step for the sake of both cate- gorization accuracy and computational ef- ficiency. Different dimensionalities are ex- pected under different practical resource …...

. We present in this paper a supervised topic model for multi-class classification problems. To incorporate supervisory information, we jointly model documents and their labels in a graphical model called …...

AbstractDelivering linguistic resources and easy-to-use methods to a broad public in the humanities is a challenging task. On the one hand users rightly demand easy to use interfaces but on …...

AbstractWe introduce a modular rule-based approach to text categorisation which is more flexible and less time consuming to build than a standard rule-based system because it works with a hierarchical …...

A common approach in text mining tasks such as text categorization, authorship identification or plagiarism detection is to rely on features like words, part-of-speech tags, stems, or some other high-level …...

We introduce three linguistically moti- vated structured regularizers based on parse trees, topics, and hierarchical word clusters for text categorization. These regularizers impose linguistic bias in fea- ture weights, enabling …...

AbstractIn this paper we present SenTube – a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers …...

Marketing materials such as flyers and other infographics are a vast online resource. In a number of industries, such as the com- mercial real estate industry, they are in fact …...

This paper addresses text categorization problem that training data may derive from a different time period from the test data. We present a learning framework which extends a boosting technique …...

AbstractSarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively …...

AbstractThere is a rich flora of word space models that have proven their efficiency in many different applications including information retrieval (Dumais, 1988), word sense disambiguation (Schutze, 1992), various semantic …...

AbstractWe show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a …...

AbstractDistributions of the senses of words are often highly skewed and give a strong influence of the domain of a document. This paper follows the assumption and presents a method …...

AbstractRomanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large …...