TCS WITM 2022@FinSim4-ESG: Augmenting BERT with Linguistic and Semantic features for ESG data classification
Tushar Goel, Vipul Chauhan, Suyash Sangwan, Ishan Verma, Tirthankar Dasgupta, Lipika Dey · 2022
Advanced neural network architectures have provided several opportunities to develop systems to automatically capture information from domain-specific unstructured text sources.The FinSim4-ESG shared task, collocated with the FinNLP workshop, proposed two sub-tasks.In sub-task1, the challenge was to design systems that could utilize contextual word embeddings along with sustainability resources to elaborate an ESG taxonomy.In the second subtask, participants were asked to design a system that could classify sentences into sustainable or unsustainable sentences.In this paper, we utilize semantic similarity features along with BERT embeddings to segregate domain terms into a fixed number of class labels.The proposed model not only considers the contextual BERT embeddings but also incorporates Word2Vec, cosine, and Jaccard similarity which gives word-level importance to the model.For sentence classification, several linguistic elements along with BERT embeddings were used as classification features.We have shown a detailed ablation study for the proposed models.