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This paper illustrates the details description of technical textclassification system and its results that developed as a part of participationin the shared task TechDofication 2020. The shared task consists of twosub-tasks: (i) first task identify the coarse-grained technical domain of giventext in a specified language and (ii) the second task classify a text ofcomputer science domain into fine-grained sub-domains. A classification system(called 'TechTexC') is developed to perform the classification task using threetechniques: convolution neural network (CNN), bidirectional long short termmemory (BiLSTM) network, and combined CNN with BiLSTM. Results show that CNNwith BiLSTM model outperforms the other techniques concerning task-1 ofsub-tasks (a, b, c and g) and task-2a. This combined model obtained f1 scoresof 82.63 (sub-task a), 81.95 (sub-task b), 82.39 (sub-task c), 84.37 (sub-taskg), and 67.44 (task-2a) on the development dataset. Moreover, in the case oftest set, the combined CNN with BiLSTM approach achieved that higher accuracyfor the subtasks 1a (70.76%), 1b (79.97%), 1c (65.45%), 1g (49.23%) and 2a(70.14%).

TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network