We present a framework that combines machine learnt classifiers and taxonomies of topics to enable a more conceptual analysis of a corpus than can be accomplished using Vector Space Models and Latent Dirichlet Allocation based topic models which represent documents purely in terms of words. Given a corpus and a taxonomy of topics, we learn a classifier per topic and annotate each document with ...

The paper is focused on self-contained linguistic problems based on text corpora. We argue that corpus-based problems differ from traditional linguistic problems because they make it possible to represent language variation. Furthermore, they often require basic statistical thinking from the students. The practical value of using data obtained from text corpora for teaching linguistics through ...

This paper will focus on automatic methods for quantifying language similarity. This is achieved by ascribing language similarity to the similarity of text corpora. This corpus similarity will first be determined by the resemblance of the vocabulary of languages. Thereto words or parts of them such as letter n-grams are examined. Extensions like transliteration of the text data will ensure the ...

In today world of internet, it is important to feedback the users based on what they demand. Moreover, one of the important tasks in data mining is classification. Today, there are several classification techniques in order to solve the classification problems like Genetic Algorithm, Decision Tree, Bayesian and others. In this article, it is attempted to classify researchers to “Expert” and “No...

The subject of this paper is the semi-automatic construction of taxonomies over the Web. We address the problem of discovering high-quality resources that belong in a particular node of a taxonomy. We show that minimal additional effort is required to provide relevance feedback in a hyperlinked environment, resulting in significant and consistent improvement in quality. Furthermore, this feedba...

This paper proposes an effective framework for interactive region-based image retrieval. By utilizing fuzzy coarse segmentation and the graph structure for representing each image, the retrieval process was performed by measuring the image similarity according to the graph similarity. To assess the similarity between two graphs, fuzzy inter relations among regions feature vectors and spatial di...

We propose two extensions of the Local Similarity Pattern/Weighted Local Similarity Pattern (LSP/WLSP) image similarity models, proposed by Stejić (2002). The objective is to improve the retrieval precision, by increasing the expressive power of the LSP/WLSP models. We formalize LSP and WLSP as special cases of a general similarity model, which defines image similarity as a weighted mean of the...