A Graph Convolution Network-based System for Technical Domain Identification (2020)

Abstract: 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).

## introduction text classification is the task of assigning a category to a given piece of text based on its content from a predefined set of categories @xcite . it is a fundamental natural language processing (nlp) task with several downstream applications such as sentiment analysis, topic labeling and machine translation. the icon 2020 shared task on technical domain identification is essentially a text classification problem which involves identification of the technical domain in which a document belongs to a fixed set of domains. in this task, the participants are expected to develop a system that automatically identifies the technical domain of a given text (a small passage) in specified language. we participated in the following subtasks: we applied pos tagging and dependency parsing on the training and test data using pos taggers and dependency parsers in the corresponding languages. we have used the pos tags and the dependency relations of the tokens with their parent nodes in the dependency trees as features of the tokens in our system. in the domain identification stage, the technical domain of a document is predicted from the representation of the document obtained using a gcnn, that takes the contextual representations of the constituent tokens of the document and a graph representation of the document as input. the contextual representations of the tokens are obtained by using a multi-layer bi-directional long-short term memory (lstm) @xcite ) that takes the representations of the tokens as input. corresponding to each subtask we submitted single runs of the systems. ## our proposed model in this section, we will discus our proposed technical domain identification system in detail. the steps for predicting the domain of a given document are as follows. s1. we partitioned the document into constituent sentences and tokenized each sentence into its constituent words. s2. we pos tagged and dependency parsed each sentence. s3. we obtained the contextual representation of each token in the document by applying a multi-layer bi-directional lstm on the representations of the tokens where we considered the entire document as a single sequence. s4. we used the contextual representations of the tokens obtained in the previous step and the graph representation of the document as input to a gcnn to obtain the final feature representation of each word. ## data description the statistics of the data corresponding to the different subtasks in which we have participated are summarized in in this section we present the settings and hyperparameters used in our domain prediction system. in ## results in this section, we present the performance of our systems corresponding to the different subtasks. in ## error analysis we have inspected the outputs of our system on the development set. thorough error analysis has revealed several type of errors. aside from the errors propagated from the pos tagger and dependency parser, some of the errors generated in the output are discussed bellow: these instances are from subtask 1a. s1 is detected as physics (actual class: math) where as s2 is detected as math (actual class: physics). same errors happen in case of subtask 1b for classes: cse (computer science) and com tech(communication technology) 2. most of the candidate texts are single sentences. sometimes system has failed to identify the correct class as it could not understand the contextual information from those single sentences. for example: • s3: why that is what is the reason why? system has failed to classify the domain of these sentences properly. from these errors, it is evident that the problem of domain selection (among highly related classes) is really a challenging task. the accuracy and f-1 score of our system are also lower than the best system submitted. in future we will apply some other data processing techniques and smarter machine learning models to improve our performance. thomas n kipf and max welling. 2016. semisupervised classification with graph convolutional networks. arxiv preprint arxiv:1609.02907. kamran kowsari, kiana jafari meimandi, mojtaba heidarysafa, sanjana mendu, laura e. barnes, and donald e. brown. 2019. text classification algorithms: a survey. joakim nivre, marie-catherine de marneffe, filip ginter, yoav goldberg, jan hajič, christopher manning, ryan mcdonald, slav petrov, sampo pyysalo, natalia silveira, reut tsarfaty, and daniel zeman. 2016. universal dependencies v1: a multilingual treebank collection. in proceedings of the 10th international conference on language resources and evaluation (lrec 2016), pages 1659-1666, portorož, slovenia. european language resources association. peng qi, timothy dozat, yuhao zhang, and christopher d. manning. 2018. universal dependency parsing from scratch. in proceedings of the conll 2018 shared task: multilingual parsing from raw text to universal dependencies, pages 160-170, brussels, belgium. association for computational linguistics. m. schuster and k. k. paliwal. 1997. bidirectional recurrent neural networks. ieee transactions on signal processing, 45(11):2673-2681.