In this paper, we are going to propose a technique to find meaning of words using Word Sense Disambiguation using supervised and unsupervised learning. This limitation of information is main flaw of the supervised approach. Our proposed approach focuses to overcome the limitation using learning set which is enriched in dynamic way maintaining new data. We introduce a mixed methodology having “M...

This paper describes the participation of Tel Aviv University Medical Image Processing Laboratory group at the ImageClef 2008 medical retrieval and medical annotation tasks. In both tasks we have used the bag-of-words approach for image representation. We submitted two purely visual automatic runs to the medical retrieval task, which used different normalization in the feature extraction stage....

We present a new approach to image indexing and retrieval, which integrates appearance with global image geometry in the indexing process, while enjoying robustness against viewpoint change, photometric variations, occlusion, and background clutter. We exploit shape parameters of local features to estimate image alignment via a single correspondence. Then, for each feature, we construct a spars...

In this paper we describe our approach and task for the PAN 2013 Author Identification. Best thing for an intelligent application is a large corpus, but when it is small, it would be helpful to extract many features from dataset and study on them. So, we extract many features from documents like: word bag, stop word bag, punctuation bag, part of speech (POS) bag and etc. It is so important that...

We present an algorithm for simultaneously recognizing and localizing planar textured objects in an image. The algorithm can scale efficiently with respect to a large number of objects added into the database. In contrast to the current state-of-the-art on large scale image search, our algorithm can accurately work with query images consisting of several specific objects and/or multiple instanc...

Word embeddings have become widelyused in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words. In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clusterin...

There is enormous amount information available in different forms of sources and genres. In order to extract useful from a massive data, automatic mechanism required. The text summarization systems assist with content reduction keeping the important filtering non-important parts text. Good document representation really get relevant information. Bag-of-words cannot give word similarity on synta...