As life moves on, things change, people change, technology changes too. Here I share, very few of the personal, professional and social stuff, which I have come across over the years. Life is on.
Sunday, January 12, 2014
Tuesday, December 3, 2013
Another piece of paper
Although I don't know what is the real social contribution of this piece of paper. I wish there was some.
Zia Ul-Qayyum and Wasif Altaf, 2012. Paraphrase Identification using Semantic Heuristic Features. Research Journal of Applied Sciences, Engineering and Technology, 4(22): 4894-4904.
Paraphrase Identification (PI) problem is to classify that whether or not two sentences are close enough in meaning to be termed as paraphrases. PI is an important research dimension with practical applications in Information Extraction (IE), Machine Translation, Information Retrieval, Automatic Identification of Copyright Infringement, Question Answering Systems and Intelligent Tutoring Systems, to name a few. This study presents a novel approach of paraphrase identification using semantic heuristic features envisaging improving the accuracy compared to state-of-the-art PI systems. Finally, a comprehensive critical analysis of misclassifications is carried out to provide insightful evidence about the proposed approach and the corpora used in the experiments.
Sunday, May 20, 2012
a bit late but another phase starts - my first paper ever!
Paraphrase Identification: Current State of the Art
By: Zia Ul-Qayyum (Corresponding Author), Muhammad Aslam, Wasif Altaf, Muhammad Ramzan
Paraphrasing generally may be done at various levels like at word, sentence, paragraph or discourse level. However, from NLP perspective, research issues related to paraphrasing include paraphrase generation, paraphrase acquisition and paraphrase identification. Paraphrase identification (PI) is an important research dimension having practical implementations which are of paramount importance in application domain like Information Retrieval, Automatic Identification of Copyright Infringement, Question Answering, Natural Language Generation, Modelling Language Perception in an Intelligent Agent and Intelligent Tutoring Systems etc. PI has been approached previously by various lexical, syntactic, semantic and hybrid techniques. Moreover, machine learning approaches have also been used. This paper provides a comprehensive overview of this important research domain along with a review of prevalent techniques being employed to address the PI problem.
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