Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Selvaperumal, P.
- Supervised Alias Name Validation Using Statistical Similarity Coefficients
Abstract Views :263 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 6, No 1 (2015), Pagination: 1070-1075Abstract
Alias name is the surnames for a known name. Extracting and validating alias names is an interesting research topic in language processing and has a number of Natural language processing applications like Information extraction, Information retrieval, Sentimental analysis, Question and answering. Alias name validation involves the process of validating whether a name is alias name or not. In this work, seven statistical similarity coefficients were used as features in classifier to validate alias names. For each name-alias pair, seven statistical similarity coefficient values were calculated and used as features to train a classifier. The trained classifier is then employed to classify whether a name-alias pair is valid or not. Experiments were conducted using Indian name-alias data that has data for 15 persons containing 35 name-alias pairs. Results show that SVM classifier with Radial Basis Function Kernel outperforms all the other classifiers in terms of overall accuracy.Keywords
Alias Name Extraction, Information Extraction, Web Mining.
References
- Paul Hsiung, Andrew Moore, Daniel Neill and Jeff Schneider, “Alias detection in link data sets”, Proceedings of the International Conference on Intelligence Analysis, 2005.
- Wee Meng Soon, Hwee Tou Ng and Daniel Chung Yong Lim, “A machine learning approach to coreference resolution of noun phrases”, Association for Computational Linguistics, Vol. 27, No. 4, pp. 521-544, 2001.
- James Mayfield, David Alexander, Bonnie J. Dorr, Jason Eisner, Tamer Elsayed, Tim Finin, Clayton Fink et al. “Cross-Document Coreference Resolution: A Key Technology for Learning by Reading”, AAAI Spring Symposium: Learning by Reading and Learning to Read, pp. 65-70, 2009.
- Shalom Lappin and Herbert J. Leass, “An algorithm for pronominal anaphora resolution”, Association for Computational linguistics, Vol. 20, No. 4, pp. 535-561, 1994.
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka, “Automatic discovery of personal name aliases from the web”, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 6, pp. 831-844, 2011.
- Christopher D. Manning and Hinrich Schutze, “Foundations of Statistical Natural Language Processing”, MIT Press, 1999.
- Tomoko Hokama and Hiroyuki Kitagawa, “Extracting mnemonic names of people from the web”, Digital Libraries: Achievements, Challenges and Opportunities: Lecture Notes in Computer Science, Vol. 4312, pp. 121-130, 2006.
- Vinay Bhat, Tim Oates, Vishal Shanbhag and Charles Nicholas, “Finding aliases on the web using latent semantic analysis”, Data & Knowledge Engineering, Vol. 49, No. 2, pp. 129-143, 2004.
- Hsin-Hsi Chen, Ming-Shun Lin and Yu-Chuan Wei, “Novel association measures using web search with double checking”, Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp. 1009-1016, 2006.
- William W. Cohen, Pradeep Ravikumar and Stephen E. Fienberg, “A comparison of string metrics for matching names and records”, KDD Workshop On Data Cleaning And Object Consolidation, Vol. 3, pp. 73-78, 2003.
- Tarique Anwar, Muhammad Abulaish and Khaled Alghathbar, “Web content mining for alias identification: A first step towards suspect tracking”, Proceedings of the IEEE International Conference on Intelligence and Security Informatics, pp. 195-197, 2011.
- Peter Mika, “Bootstrapping the foaf-web: An experiment in social networking network mining”, Proceedings of 1st Workshop on Friend of a Friend, Social Networking and the Semantic Web, 2004.
- Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Takuichi Nishimura, Hideaki Takeda, Koiti Hasida and Mitsuru Ishizuka, “POLYPHONET: an advanced social network extraction system from the web”, Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 5, No. 4, pp. 262-278, 2006.
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka, “A web search engine-based approach to measure semantic similarity between words”, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 7, pp. 977-990, 2011.
- Danushka Bollegala, Yutaka Matsuo, Taiki Honma and Mitsuru Ishizuka, “Identification of personal name aliases on the web”, Proceedings of 17th International Conference on World Wide Web, 2008.
- Kenneth Ward Church and Patrick Hanks, “Word association norms, mutual information, and lexicography”, Computational linguistics, Vol. 16, No. 1, pp. 22-29, 1990.
- Rudi L Cilibrasi and Paul MB Vitanyi, “The Google similarity distance”, IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 3, pp. 370-383, 2007.
- http://in.mathworks.com/help/stats/naive-bayes-classification.html
- Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan et al. “Top 10 algorithms in data mining”, Knowledge and Information Systems, Vol. 14, No. 1, pp. 1-37, 2008.
- Ian H Witten and Eibe Frank, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 2005.
- Ning An, Lilli Jiang, Jianyonng Wang, Ping Luoe, Min Wange and Bing Nan Li, “Towards detecting of alias without string similarity”, Information Sciences, Vol. 261, pp. 89-100, 2014.
- W. Bruce Croft, Donald Metzler and Trevor Strohman, “Search engines: Information retrieval in practice”, Addison-Wesley, 2010.
- Validating the Performance of Personalization Techniques in Search Engine
Abstract Views :172 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 5, No 3 (2015), Pagination: 965-970Abstract
User profiling is an important and basic component in personalized search engine. Search engines respond to a user's query by using the bag-of-words model, which matches keyword between the query and web documents but ignore contexts and users' preferences. Personalized search greatly improves the search results as of the results provided by the search engine without personalization. In this paper, the performance of personalized search based on content analysis and personalized search based on user group have been evaluated. In personalized search based on content analysis the contents are traced by finding the user's browsed documents and search history, which reduce the users search time. In user profile only user preference alone is taken into consideration. The experimental results show that the personalized search based on user group method having higher precision and recall rate than the content analysis method.Keywords
Search Engine, Personalization, User Profile, Content Analysis.- Hybrid Multi Resolution Method of Image Fusion for Medical Image Processing
Abstract Views :144 |
PDF Views:4
Authors
Affiliations
1 VELTECH Dr. RR and Dr. SR Technical University, Chennai, IN
1 VELTECH Dr. RR and Dr. SR Technical University, Chennai, IN
Source
Oriental Journal of Computer Science and Technology, Vol 4, No 1 (2011), Pagination: 195-199Abstract
Image fusion is a process of combining multiple input images of the same scene into a single fused image, which preserves relevant information and also retains the important features from each of the original images and makes it more suitable for human and machine perception. The reason for going onto image fusion is that, in the medical image processing, different sources of images produce complementary information and so one has to fuse all the sources of images to get more details required for the diagnosis of the patients. In this method the raw data is the MR scan image of a patient’s brain which is observed at different angles or resolutions. The images possess both different as well as common information with respect to each other. Thus when these images are fused together the redundant images are neglected and the complementary images are added thereby producing an accurate diagnosis with a single image.Keywords
Image Fusion, Transform, Segmentation, Filter Bank, NSCT.Full Text