T ech T ex C : Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network (2020)

Abstract: 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%).

## introduction due to the substantial growth and effortless access to the internet in recent years, an enormous amount of unstructured textual contents have generated. it is a crucial task to organize or structure such a voluminous unstructured text in manually. thus, automatic classification can be useful to manipulate a huge amount of texts, and extract meaningful insights which save a lot of time and money. text categorization is a classical nlp problem which aims to categorize texts into organized groups. it has a wide range of applications like machine translation, question answering, summarization, and sentiment analysis. there are several approaches available to classify texts according to their labels. however, deep learning method outperforms the rule-based and machine learning-based models because of their ability to capture sequential and semantic information from texts @xcite . we propose a classifier using cnn @xcite , and bilstm @xcite to classify technical texts in the computer science domain. furthermore, by sequentially adding these networks, remarkable accuracy in several shared classification tasks can be obtained. the rest of the paper is organized as follows: related work given in section 2. section 3 describes the dataset. the framework described in section 4. the findings presented in section 5. ## related work cnn and lstm have achieved great success in various nlp tasks such as sentence classification, document categorization, sentiment analysis, and summarization. @xcite used convolution neural network to classify sentences. a method used contents and citations to classify scientific document (cao and gao). zhou et al. (2016) used 2-d max pooling and bidirectional lstm to classify texts. zhou et al. (2015) combined cnn and lstm to classify sentiment and question type. their system achieved superior accuracy than cnn and lstm individually. hossain et al. (2020) used lstm to classify sentiment of bengali text documents. their system got maximum accuracy with one layer of lstm followed by three dense layers. ranjan et al. (2017) proposed a document classification framework using lstm and feature selection algorithms. ameur et al. (2020) combined cnn and rnn methods to categorize arabic texts. they used dynamic, finetuned words embedding to get effective result on open-source arabic dataset. ## dataset to develop the classifier model, we used the dataset provided by the organizers of the shared task foot_0 . this shared task consists of two subtasks: subtask-1 and subtask-2. subtask-1 aims to the identification of coarse-grained domain for a piece of text. organizers provided data including eight different languages (english, bangla, hindi, gujarati, malayalam, marathi, tamil, telugu) each having a different number of classes for this task. in subtask-2, the goal is to find the finegrained sub domain of a text from the computer science domains. seven classes such as artificial intelligence, algorithm, computer architecture, computer networks, database management systems, programming, software engineering are available in this subtask-2. the number of training, validation and test texts for each of the task is different. summary of the dataset presents in ## experiments google co-laboratory platform is used to conduct experiments. deep learning model developed with @xmath0.4.0 framework with @xmath1.3.0 in the backend. for data preparation and evaluation, we use @xmath2.6.9 and secikit-@xmath3.22.2. ## conclusion this paper presents a detail description of the proposed system and its evaluation for the technical texts classification in different languages. as the baseline method, we used cnn and bilstm, and compare these methods with the proposed model @xcite . each model is trained, tuned and evaluated separately for subtasks 1 and 2. the proposed method showed better performance in terms of accuracy for subtasks (a, b, c, g) of task 1 and task 2a on development set. however, in the case of test set, the system performed better for the subtasks 1a, 1b, 1c, 1g and 2a. more dataset can be included for improved performance. in future, the attention mechanism may be explored to observe its effects on text classification tasks.