Chandan Mittal ¹, Vishal Goyal ¹, Umrinderpal Singh ¹

Affiliations
1 Department of computer Science, Punjab University, Patiala – 147002, IN

Abstract

This paper presents a Hidden Markov Model (HMM) based Chunker for Punjabi. Chunking is the process of segmenting the text into syntactically correlated word groups known as chunks and then identifying the labels of the defined chunks. A robust Chunker is an important component for various applications requiring Natural Language Processing (NLP). In this research work, my goal is to develop an HMM based Chunker for Punjabi language. HMM Chunker is based on statistical probabilities. I have followed Hidden Markov Model in achieving my goal in which Viterbi Algorithm is used for calculating the highest probability of chunks and to train the system, Baum-Welch algorithm is followed and 25,000 lines of chunked Punjabi text are used. An annotated text file having 1,000 lines is used for testing the system. The accuracy of the system to find the chunk boundaries of the system is about 80% approx and the labelling is applied with an accuracy of about 98% and the labelling is applied with an accuracy of about 82%.

Keywords

Baum-Welch Algorithm, Chunking, Hidden Markov Model, Viterbi Algorithm

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Lalit Goyal ¹, Vishal Goyal ²

Affiliations
1 Department of Computer Science, DAV College, Jalandhar - 144008, Punjab, IN
2 Department of Computer Science, Punjabi University, Patiala - 147002, Punjab, IN

Abstract

Objective: Development of Indian Sign Language video dictionary is essential in the today’s world of computerization. Though a lot of human video sign language dictionaries are available, we aim to develop the Indian Sign Language dictionary using synthetic animation which uses the computer generated cartoon rather than real human. Methods/Statistical Analysis: Sign Language cannot be spoken or written unlike other languages like English, Punjabi, Hindi, etc. The most commonly used words in Indian Sign Language are categorized and then these words are converted into the sign language writing notation (HamNoSys - Hamburg Notation System). This HamNoSys notation is then converted into SiGML (Signing Gesture Markup Language) using which the synthetic animation (using a computer generated cartoon) of the sign is generated. Findings: The synthetic animations are better as compared to human videos in terms of memory consumption, standardization, and flexibility. Synthetic animations can be modified as per the requirement whereas the human videos cannot be modified. The only drawback that seem is, these synthetic animations may lack the natural non-manual component of sign. Applications/Improvements: The synthetic dictionary created in this work can be used for translation system in which spoken or written sentence can be converted into the sign language animation. The dictionary created can be used to education to hard of hearing people. Display boards can be created for displaying the important messages in Indian sign language at the public gathering.

Keywords

HamNoSys, Machine Translation System, Natural Language Processing, Sign Language, SiGML.

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Urvashi Garg ¹, Vishal Goyal ¹

Affiliations
1 Punjabi University, Patiala, NH 64, Urban Estate Phase II, Patiala - 147002, Punjab, IN

Abstract

Objective: The objective of this paper is to present an automated plagiarism detection software tool called Maulik. There are many plagiarism detection tools available for English text. Maulik detects plagiarism in Hindi documents. Method: Maulik divides the text into n-grams and then matches it with the text present in repository as well as with documents present online. Preprocessing techniques such as stop word removal and stemming has been used. The best value of n-gram for finding out the similarity of two Hindi documents has also been found out. Cosine similarity has been used for finding the similarity score. Findings: Similarity score of 96.3 has been achieved which is higher as compared to the existing Hindi plagiarism detection tools such as Plagiarism checker, Plagiarism finder, Plagiarisma, Dupli checker, Quetext. These tools compared only exact matches ignoring the language specific constraints whereas Maulik is capable of finding plagiarism if ischolar_main of a word is used or a word is replaced by its synonyms. Application: Maulik is a software tool which discourages plagiarism as well as motivates the writing skills of people.

Keywords

Cosine Similarity, Plagiarism, Stemming, Stop Word, Synonyms

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