Mohammed Arif Khan

Also published as: Arif Khan, Arif Md. Khan


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CIC NLP at SMM4H 2022: a BERT-based approach for classification of social media forum posts
Atnafu Lambebo Tonja | Olumide Ebenezer Ojo | Mohammed Arif Khan | Abdul Gafar Manuel Meque | Olga Kolesnikova | Grigori Sidorov | Alexander Gelbukh
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2022 shared tasks. We participated in 2 tasks: a) Task 4: Classification of Tweets self-reporting exact age and b) Task 9: Classification of Reddit posts self-reporting exact age. We evaluated the two( BERT and RoBERTa) transformer based models for both tasks. For Task 4 RoBERTa-Large achieved an F1 score of 0.846 on the test set and BERT-Large achieved an F1 score of 0.865 on the test set for Task 9.


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BLISS: An Agent for Collecting Spoken Dialogue Data about Health and Well-being
Jelte van Waterschoot | Iris Hendrickx | Arif Khan | Esther Klabbers | Marcel de Korte | Helmer Strik | Catia Cucchiarini | Mariët Theune
Proceedings of the Twelfth Language Resources and Evaluation Conference

An important objective in health-technology is the ability to gather information about people’s well-being. Structured interviews can be used to obtain this information, but are time-consuming and not scalable. Questionnaires provide an alternative way to extract such information, though typically lack depth. In this paper, we present our first prototype of the BLISS agent, an artificial intelligent agent which intends to automatically discover what makes people happy and healthy. The goal of Behaviour-based Language-Interactive Speaking Systems (BLISS) is to understand the motivations behind people’s happiness by conducting a personalized spoken dialogue based on a happiness model. We built our first prototype of the model to collect 55 spoken dialogues, in which the BLISS agent asked questions to users about their happiness and well-being. Apart from a description of the BLISS architecture, we also provide details about our dataset, which contains over 120 activities and 100 motivations and is made available for usage.


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A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks
Arif Khan | Ingmar Steiner | Yusuke Sugano | Andreas Bulling | Ross Macdonald
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Simultaneous Feature Selection and Parameter Optimization Using Multi-objective Optimization for Sentiment Analysis
Mohammed Arif Khan | Asif Ekbal | Eneldo Loza Mencía
Proceedings of the 12th International Conference on Natural Language Processing


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Indian Institute of Technology-Patna: Sentiment Analysis in Twitter
Vikram Singh | Arif Md. Khan | Asif Ekbal
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)