NITK NLP at F in C ausal-2020 Task 1 Using BERT and Linear models. (2020)

Abstract: FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don’t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677.

## introduction the important aspect as far as the financial news is the variability and the impact which it causes. in the information retrieval process, causality is an essential and well-known topic. several nlp methods can be used to find the relationship between financial data and its effect. the main focus of this work is to come out with a better solution for the fincausal-2020 shared task @xcite . this shared task mainly focuses on determining causality associated with the financial object's transformation in quantified facts. we have applied classification for the financial data using linear model and deep learning bert @xcite model. in the case of linear model, an svm classifier is used, which is further fine tuned to produce better result. the fine-tuned bert base uncased version was used as a deep learning model. this paper is presented as follows; the details about the data being used are explained in section 2, system description is being explained in section 3, results and discussion in section 4, which follows the conclusions in the last section 5. ## dataset description the task organisers provided the data for the shared task as csv files, namely trial, practice, and evaluation. these data where extracted from a corpus of 2019 financial news provided by quam. the original data being html pages corresponding to the daily financial news feed is extracted. these raw set is being arranged with the column as index, text, and category. initially, the trial and practice dataset were released to build and train the model, which consists of data as shown in the example sentences from the dataset: ## system description we have developed both linear as well as deep learning models. the svm classifier was used as a linear model and, bert was used as a deep learning model. the hardware used for the experiments were colaboratory by google with gpu ranging from 10gb to 16gb(tesla k80/tesla p100). the description of each system is explained one by one below. the linear model svm classifier was given as a baseline by the task organiser. we have tried with nbsvm model @xcite , to apply svm model for the textual data, some basic preprocessing like removing url, html tags @xcite , special symbols, and accented characters were done. further, these texts were converted to lowercase and tf-idf vectorizer @xcite was applied. the experiment was conducted in two phases, the first one being training the model using the trial dataset and testing with the practice dataset. the second one was training the model using practice dataset and testing with the trial dataset. the same steps were followed for both the phases. the prediction for the evaluation data provided by the organiser is done by building a model which was trained on both practice and train data. the first phase of the experiment was done by splitting @xcite the trial data into 85% and 15% for train and validation, respectively. the tf-idf was experimented with different n-grams and fixed a range of (1-5). the minimum and the maximum number of occurrences (min df and max df) of words to be considered to make a vocabulary were also altered. after grid search, the maximum and minimum occurrences were fixed at 90% (maximum number of sentences) and 2(least number of sentences), which gave better scores for the metrics as in the practice and trial data were combined to predict the evaluation data whose scores are also given in ## conclusions the fincausal-2020 shared task was mainly aimed to come up with a model that could analyze the text and say whether it belongs to a particular financial causality or not. the main challenge was the imbalanced dataset and from which we need to develop a model that could produce an accurate result. as per the experiments being conducted and observing the results, the fine-tuned bert model could perform well for the blind dataset using the k nown data than bert and the liner svm model. if we could come up with more balanced data with sampling methods, bert would outperform most of the existing models.