Informatics Publishing Limited

D. K. Kanitha ¹, D. Muhammad Noorul Mubarak ²

Affiliations
1 Compuational Linguistics, Department of Linguistics, University of Kerala, Kariavattom, Thiruvananthapuram, IN
2 Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram, IN

Abstract

The availability of online information shows a need of efficient text summarization system. The text summarization system follows extractive and abstractive methods. In extractive summarization, the important sentences are selected from the original text on the basis of sentence ranking methods. The Abstractive summarization system understands the main concept of texts and predicts the overall idea about the topic. This paper mainly concentrated the survey of existing extractive text summarization models. Numerous algorithms are studied and their evaluations are explained. The main purpose is to observe the peculiarities of existing extractive summarization models and to find a good approach that helps to build a new text summarization system.

Keywords

Text Summarization, Abstractive Summarization, Extractive Summarization, Statistical Methods, Latent Semantic Analysis.

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D. Muhammad Noorul Mubarak ¹

Affiliations
1 Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, IN

Abstract

In this current technological era, there is an enormous increase in the information available on web and also in the online databases. This information abundance increases the complexity of finding relevant information. To solve such challenges, there is a need for improved and intelligent systems for efficient search and retrieval. Intelligent Agents can be used for better search and information retrieval in a document collection. The information required by a user is scattered in a large number of databases. In this paper, the object oriented modeling for agent based information retrieval system is presented. The paper also discusses the framework of agent architecture for obtaining the best combination terms that serve as an input query to the information retrieval system. The communication and cooperation among the agents are also explained. Each agent has a task to perform in information retrieval.

Keywords

Intelligent Agents, Crawling, Agent Based Information Retrieval, Object Oriented Modeling, Unified Modeling Language, Ontology, Agent Architecture.

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D. Muhammad Noorul Mubarak ¹, Sareesh Madhu ², S. A. Shanavas ²

Affiliations
1 Department of Computer Science, University of Kerala, IN
2 Department of Linguistics, University of Kerala, IN

Abstract

Parts-of-speech tagging is the process of labeling each word in a sentence. A tag mentions the word's usage in the sentence. Usually, these tags indicate syntactic classification like noun or verb, and sometimes include additional information, with case markers (number, gender etc) and tense markers. A large number of current language processing systems use a parts-of-speech tagger for pre-processing.

There are mainly two approaches usually followed in Parts of Speech Tagging. Those are Rule based Approach and Stochastic Approach. Rule based Approach use predefined handwritten rules. This is the oldest approach and it use lexicon or dictionary for reference. Stochastic Approach use probabilistic and statistical information to assign tag to words. It use large corpus, so that Time complexity and Space complexity is high whereas Rule base approach has less complexity for both Time and Space. Stochastic Approach is the widely used one nowadays because of its accuracy.

Malayalam is a Dravidian family of languages, inflectional with suffixes with the ischolar_main word forms. The currently used Algorithms are efficient Machine Learning Algorithms but these are not built for Malayalam. So it affects the accuracy of the result of Malayalam POS Tagging.

My proposed Approach use Dictionary entries along with adjacent tag information. This algorithm use Multithreaded Technology. Here tagging done with the probability of the occurrence of the sentence structure along with the dictionary entry.

Keywords

NLP, POS Tagger, Rule Based Approach, Stochastic Approach, Multithreading, Dictionary Entry, Malayalam.

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D. Muhammad Noorul Mubarak ¹, M. Mohamed Sathik ², S. Zulaikha Beevi ³, K. Revathy ¹

Affiliations
1 Department of Computer Science, University of Kerala, Trivandrum, IN
2 Sathakathullah Appa College, Tirunelveli, Tamilnadu, IN
3 Department of IT, National College of Engineering, Tirunelveli, Tamilnadu, IN

Abstract

In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliable on homogeneity and region characteristics. Furthermore, the method is simple but robust and it can extract objects and boundary smoothly.

Keywords

Region Growing, Segmentation, Seeds Selection, Homogeneity Criterion, Cloud Model.

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