  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%).
