A mrita_ CEN _ NLP @ SDP 2021 Task A and B (2021)

Abstract: The purpose and influence of a citation are important in understanding the quality of a publication. The 3c citation context classification shared task at the Second Workshop on Scholarly Document Processing aims at addressing this problem. This paper is the submission of the team Amrita_CEN_NLP to the shared task. We employed Bi-directional Long Short Term Memory (LSTM) networks and a Random Forest classifier for modelling the aforementioned problems by considering the class imbalance problem in the data.

## introduction in the evolution of the information age, where colossal amounts of research papers and scientific literature are now available, the need for a method which measures the scientific impact of a paper has become paramount. one such method is citation analysis. citations are defined as a reference to the source of information used in one's research. the conventional approach to citation analysis involves utilising the frequency of citations @xcite while treating all citations equally. this methodology provides a vague or even inaccurate overview of scientific development. there are restrictions on the type of conclusions that can be drawn from citation counts, as many of the intricacies of citations are connected with the quality of the paper cited, and the context in which the citation is made. two essential features for gauging the quality of a research paper are discerning the intent of citation and its level of influence. these features are the crux of the 3c shared task which has been split into two subtasks -purpose (a) and influence (b). purpose is a multi-class classification problem which classifies the cited title based on how it is related to the citing paper. meanwhile, influence is a binary-class classification problem which classifies whether the cited title is merely incidental or actually influential to the citing paper. this paper reports the submissions of the am-rita_cen_nlp team for the 3c citation context classification shared task @xcite . we used deep learning and machine learning models developed using bi-lstm and random forest algorithms @xcite , @xcite , @xcite to complete the subtasks. they will be elaborated upon in section 4. ## literature review though the importance of categorizing scientific literature according to context is apparent, the reported amount of research that has been carried out is insufficient. along with a classification model, @xcite also proposed an annotation scheme for the categorization of the citations. 12 classes were considered for annotation. from 116 articles, 2829 citation samples were gathered. these were used to train the machine learning model. 113k algorithms were used for classification with hand-engineered features. one of such features was cue phrases. features such as patternbased features, topic-based features, and prototypical argument features were used by d. @xcite to separate the documents into its 6 corresponding classes. the randomforest algorithm was used for classification. cohan et al. (2019) also utilised glove, elmo word embedding features, and bi-lstm with attention models to aid in the classification of the citations. kunnath et al. (2020) organized the first shared task on citation classification in 2020, where different teams came up with different approaches to solve 3c classification problem. ## dataset description the training data that has been provided in kaggle as a part of 3c shared task contains 3000 instances annotated using the act platform, whereas the test set contains 1000 datapoints. the dataset is in csv format and contains the following fields: unique identifier, coreid of citing paper (name of the text files in the full text dump), citing paper title (research paper in consideration), citing paper author (author of the citing paper), cited paper title (paper cited by the citing paper), cited paper author (author of the cited paper), citation context (citation context represents the sentence containing the citations), and the class labels. the training set for subtask-a is highly imbalanced, where the class background contains 54.93% of the total data, and the class future comprises a mere 2.07% of the data. the training data for the subtask-b is balanced and has two class labels. labels, description and the percentage of share in the dataset for subtask-a and subtask-b are shown in tables 1 and 2 , respectively. ## system description this section describes the approaches used by am-rita_cen_nlp to implement the models for both subtask-a and b. we experimented with machine learning as well as deep learning algorithms to complete the tasks. the workflow of the approach is given below. 1. preprocessing