Parma Nand


2016

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“How Bullying is this Message?”: A Psychometric Thermometer for Bullying
Parma Nand | Rivindu Perera | Abhijeet Kasture
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Cyberbullying statistics are shocking, the number of affected young people is increasing dramatically with the affordability of mobile technology devices combined with a growing number of social networks. This paper proposes a framework to analyse Tweets with the goal to identify cyberharassment in social networks as an important step to protect people from cyberbullying. The proposed framework incorporates latent or hidden variables with supervised learning to determine potential bullying cases resembling short blogging type texts such as Tweets. It uses the LIWC2007 - tool that translates Tweet messages into 67 numeric values, representing 67 word categories. The output vectors are then used as features for four different classifiers implemented in Weka. Tests on all four classifiers delivered encouraging predictive capability of Tweet messages. Overall it was found that the use of numeric psychometric values outperformed the same algorithms using both filtered and unfiltered words as features. The best performing algorithms was Random Forest with an F1-value of 0.947 using psychometric features compared to a value of 0.847 for the same algorithm using words as features.

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Answer Presentation in Question Answering over Linked Data using Typed Dependency Subtree Patterns
Rivindu Perera | Parma Nand
Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)

In an era where highly accurate Question Answering (QA) systems are being built using complex Natural Language Processing (NLP) and Information Retrieval (IR) algorithms, presenting the acquired answer to the user akin to a human answer is also crucial. In this paper we present an answer presentation strategy by embedding the answer in a sentence which is developed by incorporating the linguistic structure of the source question extracted through typed dependency parsing. The evaluation using human participants proved that the methodology is human-competitive and can result in linguistically correct sentences for more that 70% of the test dataset acquired from QALD question dataset.

2015

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Selecting Contextual Peripheral Information for Answer Presentation: The Need for Pragmatic Models
Rivindu Perera | Parma Nand
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters

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RealText-asg: A Model to Present Answers Utilizing the Linguistic Structure of Source Question
Rivindu Perera | Parma Nand
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters

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Generating Lexicalization Patterns for Linked Open Data
Rivindu Perera | Parma Nand
Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data

2014

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A Multi-Strategy Approach for Location Mining in Tweets: AUT NLP Group Entry for ALTA-2014 Shared Task
Parma Nand | Rivindu Perera | Anju Sreekumar | Lingmin He
Proceedings of the Australasian Language Technology Association Workshop 2014