Combining Textual and Visual Information for Image Retrieval in the Medical Domain

In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP)

Using clustering to enhance text classification

This paper addresses the problem of learning to classify texts by exploiting information derived from clustering both train-ing and testing sets. The incorporation of knowledge result-ing from clustering into the feature space representation of the texts is expected to boost the performance of a classi-fier. Experiments conducted on several widely used datasets demonstrate the effectiveness of the proposed algorithm es-pecially for small training sets

Text Classification Using Clustering

This paper addresses the problem of learning to classify texts by exploiting information derived from both training and testing sets. To accomplish this, clustering is used as a complementary step to text classification, and is applied not only to the training set but also to the testing set. This approach allows us to estimate the location of the testing examples and the structure of the whole dataset, which is not possible for an inductive learner. The incorporation of the knowledge resulting from clustering to the simple BOW representation of the texts is expected to boost the performance of a classifier. Experiments conducted on tasks and datasets provided in the framework of the ECDL/PKDD 2006 Challenge Discovery on personalized spam filtering, demonstrate the e#ectiveness of the proposed approach. The experiments show substantial improvements on classification performance especially for small training sets

Using Hierarchical Clustering to Enhance Classification Accuracy

Abstract. A new approach to classification is presented based on COBWEB, an unsupervised conceptual clustering algorithm. The modifications proposed improved the classification accuracy by 2.32 % and up to 7.25 % in the Period Disambiguation system that was built in order to test the efficiency of the approach. The system can be trained across different domains and languages. It has been tested on the Brown Corpus and on a collection of articles from Greek financial newspapers achieving accuracy 99.18 % and 99.35 % respectively.

MITOS: An Integrated Web-based System for Information Management

The wide availability and accessibility of information have made its management and deployment even more difficult. To this end, remarkable effort has been made for the development of information systems that handle the processing, analysis and management of information. However, the success of these systems does not only depend on the quality of information handling, but also on the appropriate presentation of information to the end-user. MITOS 1 system analyses financial news by employing techniques from the areas of Natural Language Processing, Information Filtering and Information Extraction. Moreover, by acknowledging the importance of the presentation of information, MITOS has also incorporated User Modelling techniques, which enable the provision of personalized content adapted to each user’s profile.