A mrita_ CEN _ NLP @ WOSP 3 C Citation Context Classification Task (2020)

Abstract: Identification of the purpose and influence of citation is significant in assessing the impact of a publication. ‘3C’ Citation Context Classification Task in Workshop on Mining Scientific Publication is a shared task to address the abovementioned problems. This working note describes the submissions of Amrita_CEN_NLP team to the shared task. We used Random Forest with cost-sensitive learning for classification of sentences encoded into a vector of dimension 300.

## introduction the number of publications in the scientific domain increased exponentially recently, which allows researchers to look for various literature to extend their research. one method of finding the more relevant literature is to check the number of citations. a publication with more citation generally has more influence in the research community. such publications typically give significant insight into specific problems. to test whether a paper is relevant for a particular domain, one should analyse the context in which it is written. it is equally important to identify the context of the citations also. this information, as well as the citation count, give a better understanding of a publication in a particular domain. in @xcite , david pride and petr knoth proposed an automatic method for identifying the citations with influence. in addition to it, identification of the purpose of a citation is also an essential task. this paper describes the submission of amrita cen nlp team in '3c' citation context classification task a part of workshop on mining scientific publications (wosp) 2020 (n. @xcite . this shared task consisted of two subtasks. the goal of subtask-a was to identify the purpose of the citations. the subtask-b intended to classify the classification based on their importance into either influential or incidental. we used machine learning-based models for identifying the purpose and influence of the citations according to the context. the word2vec @xcite , @xcite algorithm was used to convert the words into vectors by capturing the contexts of words in the given corpus. we employed various classification algorithms with varying dimensions of word vectors for the aforementioned tasks. the random forest classifier @xcite , @xcite , @xcite with a word vector of size 300 achieved the best performance with 5-fold cross-validation. the organization of the paper as follows: section 2 gives an brief description on the related research, which will be followed by a description of the dataset in the section 3. the next section discusses the steps involved in designing the machine learning model and the paper concludes with the conclusion section. ## related works the number of research works reported for the classification of scientific literature according to the context in which it is written are limited despite it's significance. s. teufel et al. @xcite proposed an annotation scheme along with a classification model for the categorization of the citations. they considered 12 classes for annotation. the machine learning model was trained over 2829 citation instances collected from 116 articles. they used ibk algorithm for classification with hand-engineered features like cue phrases. d. jurgens @xcite used feature such as pattern-based features, topic-based features, and prototypical argument features to classify the documents into 6 classes. the authors used random forest algorithm for classification. a. cohan et al. @xcite used glove and elmo word embedding features and bidirectional lstm with attention models for classifying the citations. ## dataset description the training and test datasets used for subtask-a and subtask-b were the same. the training data and test data contained 3000 and 1000 sentences, respectively. the subtask-a was a multiclass problem with six classes in it. the distribution of the data for this task was highly uneven. in the dataset, 54.93% of the data belong to the background category, whereas the share of the future category was mere 2.07%. the subtask-b was a binary classification problem, and the data set for this task contained evenly distributed class labels. ## dataset description the training and test datasets used for subtask-a and subtask-b were the same. the training data and test data contained 3000 and 1000 sentences, respectively. the subtask-a was a multi-class problem with six classes in it. the distribution of the data for this task was highly uneven. in the dataset, 54.93% of the data belong to the background category, whereas the share of the future category was mere 2.07%. the subtask-b was a binary classification problem, and the data set for this task contained evenly distributed class labels. ## system description amrita cen nlp team participated in both the subtasks. we used machine learning algorithms for both tasks. the implementation pipeline is as follows, 1. preprocessing ## conclusion