Year: 2024
Type: conference-paper
Abstract: This study investigates Semantic Textual Related-ness (STR) within Natural Language Processing (NLP) through experiments conducted on a dataset from the SemEval-2024 STR task.The dataset comprises train instances with three features (PairID, Text, and Score) and test instances with two features (PairID and Text), where sentence pairs are separated by '/n' in the Text column.Using BERT(sentence transformers pipeline), we explore two approaches: one with fine-tuning (Track A: Supervised) and another without finetuning (Track B: UnSupervised).Fine-tuning the BERT pipeline yielded a Spearman correlation coefficient of 0.803, while without finetuning, a coefficient of 0.693 was attained using cosine similarity.The study concludes by emphasizing the significance of STR in NLP tasks, highlighting the role of pre-trained language models like BERT and Sentence Transformers in enhancing semantic relatedness assessments.
Authors: Anand Kumar, Hemanth Kumar
Institution: National Institute of Technology Karnataka
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4401042665