Event recognition and event type classification are among the important areas in text mining. A state-of-the-art approach utilizing deep-level lexical semantics and syntactic dependencies suffers from a limitation of requiring too large feature space. In this paper, we propose a novel feature selection method using a semantic hierarchy of features based on WordNet relations and syntactic depend...

In this paper a preliminary study of perceptual judgements of Polish dialogue utterances using a two-dimensional (activation-valence), continuous feature space is described. The speech material consists of utterances selected from a corpus of task oriented dialogues, realized before and after a (fake) negative evaluation of the speakers' performance. The group of the test participants includes:...

This paper considers the problem of feature selection in text categorization. Previous works in feature selection often used a filter model in which features, after ranked by a measure, are selected based on a given threshold. In this paper, we present a novel approach to feature selection based on multi-criteria decision making of each feature. Instead of only one criterion, multi-criteria of ...

Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, we first give empirical evidence that feature selection methods can improve the efficiency and performance of text clustering algorithm. Then we propose a new feature selection method called “Term Contribution ...

This comprehensive review of literature includes research studies dating from 1980 to 2005 on expository text structure awareness as well as studies on the effectiveness of expository text structure instruction. The purpose of this literature review is two-fold: 1) To determine the extent to which students’ expository text structure awareness facilitates reading comprehension; and 2) Whether it...

In this paper, we propose a text detection method based on a feature vector generated from connected components produced via the stroke width transform. Several properties, such as variant directionality of gradient of text edges, high contrast with background, and geometric properties of text components jointly with the properties found by the stroke width transform are considered in the forma...

Text mining has been employed in a wide range of applications such as text summarisation, text categorization, named entity extraction, and opinion and sentimental analysis. Text classification is the task of assigning predefined categories to free-text documents. That is, it is a supervised learning technique. While in text clustering (sometimes called document clustering) the possible categor...

The rapid growth of World Wide Web has led to explosive growth of information. As most of information is stored in the form of texts, text mining has gained paramount importance. With the high availability of information from diverse sources, the task of automatic categorization of documents has become a vital method for managing, organizing vast amount of information and knowledge discovery. T...

Today, the number of text documents in digital form is progressively increasing and text categorization becomes the key technology of dealing with organizing text data. A major problem of text categorization is a huge-scale number of features. Most of those are useless, irrelevant or redundant for text categorization. Therefore, these features can decrease the classification performance. In ord...

Recently deep learning has been successfully used in speech recognition, however it has not been carefully explored and widely accepted for speaker verification. To incorporate deep learning into speaker verification, this paper proposes novel approaches of extracting and using features from deep learning models for text-dependent speaker verification. In contrast to the traditional short-term ...