Work place: Manonmaniam Sundaranar University/Department of Computer science and Engineering, Tirunelveli, India

E-mail: selvaperumal.p@gmail.com


Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computational Learning Theory, Data Mining, Information Retrieval, Data Structures and Algorithms


P.Selvaperumal received his Bachelor degree (2006) in Computer science from Sacred heart college, Tirupattur and Master degree in Computer science (2009) from Bannari Amman Institute of Technology, Sathyamangalam and a second masters in technology in Veltech Technical university, Chennai. He is currently a Ph.D student pursuing Computer engineering in M.S University, Tirunelveli, Tamilnadu, India. His areas of interest include Text Mining, Machine Learning, NLP, Data science, big data exploration and Information Retrieval. He is an ACM Student member and a member of Indian society of technical education (ISTE).

Author Articles
Semi-Supervised Personal Name Disambiguation Technique for the Web

By P.Selvaperumal A.Suruliandi

DOI: https://doi.org/10.5815/ijmecs.2016.03.04, Pub. Date: 8 Mar. 2016

Personal name ambiguity in the web arises when more than one person shares the same name. Personal name disambiguation involves disambiguating the name by clustering web page collection such that each cluster represents a person having the ambiguous name. In this paper, a personal name disambiguation technique that makes use of rich set of features like Nouns, Noun phrases, and frequent keywords as features is proposed. The proposed method consists of two phases namely clustering seed pages and then clustering the actual web page collection. In the first phase, seed pages representing different namesakes are clustered and in the second phase, web pages in the collection are clustered with the similar seed page clusters. The usage of seed pages increases the accuracy of clustering process. Since it is difficult to predict the number of clusters need to be formed beforehand, the proposed technique uses Elbow method to calculate the number of clusters. The efficiency of the proposed name disambiguation technique is tested using both synthetic and organic datasets. Experimental result shows the proposed method achieves robust results across different datasets and outperforms many existing methods.

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String Variant Alias Extraction Method using Ensemble Learner

By P.Selvaperumal A.Suruliandi

DOI: https://doi.org/10.5815/ijisa.2016.02.08, Pub. Date: 8 Feb. 2016

String variant alias names are surnames which are string variant form of the primary name. Extracting string variant aliases are important in tasks such as information retrieval, information extraction, and name resolution etc. String variant alias extraction involves candidate alias name extraction and string variant alias validation. In this paper, string variant aliases are first extracted from the web and then using seven different string similarity metrics as features, candidate aliases are validated using ensemble classifier random forest. Experiments were conducted using string variant name-alias dataset containing name-alias data for 15 persons containing 30 name-alias pairs. Experimental results show that the proposed method outperforms other similar methods in terms of accuracy.

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