%0 Conference Proceedings %T TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network %A Sharif, Omar %A Hossain, Eftekhar %A Hoque, Mohammed Moshiul %Y Sharma, Dipti Misra %Y Ekbal, Asif %Y Arora, Karunesh %Y Naskar, Sudip Kumar %Y Ganguly, Dipankar %Y L, Sobha %Y Mamidi, Radhika %Y Arora, Sunita %Y Mishra, Pruthwik %Y Mujadia, Vandan %S Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task %D 2020 %8 December %I NLP Association of India (NLPAI) %C Patna, India %F sharif-etal-2020-techtexc %X This paper illustrates the details description of technical text classification system and its results that developed as a part of participation in the shared task TechDofication 2020. The shared task consists of two sub-tasks: (i) first task identify the coarse-grained technical domain of given text in a specified language and (ii) the second task classify a text of computer science domain into fine-grained sub-domains. A classification system (called ‘TechTexC’) is developed to perform the classification task using three techniques: convolution neural network (CNN), bidirectional long short term memory (BiLSTM) network, and combined CNN with BiLSTM. Results show that CNN with BiLSTM model outperforms the other techniques concerning task-1 of sub-tasks (a, b, c and g) and task-2a. This combined model obtained f1 scores of 82.63 (sub-task a), 81.95 (sub-task b), 82.39 (sub-task c), 84.37 (sub-task g), and 67.44 (task-2a) on the development dataset. Moreover, in the case of test set, the combined CNN with BiLSTM approach achieved that higher accuracy for the subtasks 1a (70.76%), 1b (79.97%), 1c (65.45%), 1g (49.23%) and 2a (70.14%). %U https://aclanthology.org/2020.icon-techdofication.8 %P 35-39