The increase in domain specific text processing applications are demanding tools and techniques for domain specific Text Classification (TC) which may be helpful in many downstream applications like Machine Translation, Summarization, Question Answering etc. Further, many TC algorithms are applied on globally recognized languages like English giving less importance for local languages particularly Indian languages. To boost the research for technical domains and text processing activities in Indian languages, a shared task named ”TechDOfication2020” is organized by ICON’20. The objective of this shared task is to automatically identify the technical domain of a given text which provides information about coarse grained technical domains and fine grained subdomains in eight languages. To tackle this challenge we, team MUCS have proposed three models, namely, DL-FineTuned model applied for all subtasks, and VC-FineTuned and VC-ngrams models applied only for some subtasks. n-grams and word embedding with a step of fine-tuning are used as features and machine learning and deep learning algorithms are used as classifiers in the proposed models. The proposed models outperformed in most of subtasks and also obtained first rank in subTask1b (Bangla) and subTask1e (Malayalam) with f1 score of 0.8353 and 0.3851 respectively using DL-FineTuned model for both the subtasks.

This paper presents the IITKGP contribution at the Technical DOmain Identification (TechDOfication) shared task at ICON 2020. In the preprocessing stage, we applied part-of-speech (PoS) taggers and dependency parsers to tag the data. We trained a graph convolution neural network (GCNN) based system that uses the tokens along with their PoS and dependency relations as features to identify the domain of a given document. We participated in the subtasks for coarse-grained domain classification in the English (Subtask 1a), Bengali (Subtask 1b) and Hindi language (Subtask 1d), and, the subtask for fine-grained domain classification task within Computer Science domain in English language (Subtask 2a).

With the instantaneous growth of text information, retrieving domain-oriented information from the text data has a broad range of applications in Information Retrieval and Natural language Processing. Thematic keywords give a compressed representation of the text. Usually, Domain Identification plays a significant role in Machine Translation, Text Summarization, Question Answering, Information Extraction, and Sentiment Analysis. In this paper, we proposed the Multichannel LSTM-CNN methodology for Technical Domain Identification for Telugu. This architecture was used and evaluated in the context of the ICON shared task “TechDOfication 2020” (task h), and our system got 69.9% of the F1 score on the test dataset and 90.01% on the validation set.

In this paper, we present a transfer learning system to perform technical domain identification on multilingual text data. We have submitted two runs, one uses the transformer model BERT, and the other uses XLM-ROBERTa with the CNN model for text classification. These models allowed us to identify the domain of the given sentences for the ICON 2020 shared Task, TechDOfication: Technical Domain Identification. Our system ranked the best for the subtasks 1d, 1g for the given TechDOfication dataset.

Coarse-grained and Fine-grained classification tasks are mostly based on sentiment or basic emotion analysis. Now, switching from emotion and sentiment analysis to another domain, in this paper, we are going to work on technical domain identification. The task is to identify the technical domain of a given English text. In the case of Coarse-grained domain classification, such a piece of text provides information about specific Coarse-grained technical domains like Computer Science, Physics, Math, etc, and in Fine-grained domain classification, Fine-grained subdomains for Computer science domain, it can be like Artificial Intelligence, Algorithm, Computer Architecture, Computer Networks, Database Management system, etc. To do the task, Word2Vec skip-gram model is used for word embedding, later, applied the Bidirectional Long Short Term memory (BiLSTM) model to classify Coarse-grained domains and Fine-grained sub-domains. To evaluate the performance of the approached model accuracy, precision, recall, and F1-score have been applied.

In this paper we present two Machine Learning algorithms namely Stochastic Gradient Descent and Multi Layer Perceptron to Identify the technical domain of given text as such text provides information about the specific domain. We performed our experiments on Coarse-grained technical domains like Computer Science, Physics, Law, etc for English, Bengali, Gujarati, Hindi, Malayalam, Marathi, Tamil, and Telugu languages, and on fine-grained sub domains for Computer Science like Operating System, Computer Network, Database etc for only English language. Using TFIDF as a feature extraction method we show how both the machine learning models perform on the mentioned languages.

The introduction of transformers in 2017 and successively BERT in 2018 brought about a revolution in the field of natural language processing. Such models are pretrained on vast amounts of data, and are easily extensible to be used for a wide variety of tasks through transfer learning. Continual work on transformer based architectures has led to a variety of new models with state of the art results. RoBERTa(CITATION) is one such model, which brings about a series of changes to the BERT architecture and is capable of producing better quality embeddings at an expense of functionality. In this paper, we attempt to solve the well known text classification task of fine-grained domain classification using BERT and RoBERTa and perform a comparative analysis of the same. We also attempt to evaluate the impact of data preprocessing specially in the context of fine-grained domain classification. The results obtained outperformed all the other models at the ICON TechDOfication 2020 (subtask-2a) Fine-grained domain classification task and ranked first. This proves the effectiveness of our approach.

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%).

The recent surge of complex attention-based deep learning architectures has led to extraordinary results in various downstream NLP tasks in the English language. However, such research for resource-constrained and morphologically rich Indian vernacular languages has been relatively limited. This paper proffers a solution for the TechDOfication 2020 subtask-1f: which focuses on the coarse-grained technical domain identification of short text documents in Marathi, a Devanagari script-based Indian language. Availing the large dataset at hand, a hybrid CNN-BiLSTM attention ensemble model is proposed that competently combines the intermediate sentence representations generated by the convolutional neural network and the bidirectional long short-term memory, leading to efficient text classification. Experimental results show that the proposed model outperforms various baseline machine learning and deep learning models in the given task, giving the best validation accuracy of 89.57% and f1-score of 0.8875. Furthermore, the solution resulted in the best system submission for this subtask, giving a test accuracy of 64.26% and f1-score of 0.6157, transcending the performances of other teams as well as the baseline system given by the organizers of the shared task.