Applying Natural Language Processing Techniques for Sentence-Level Relation Extraction: Analysis and Performance Evaluation

This study explores the field of sentence-level relation extraction in the context of natural language processing (NLP) applications. We have analyzed many approaches, including document-level relation extraction, in the goal of creating a reliable model for this purpose. This study clarified the difficulties associated with entity coreference resolution as well as the subtle capture of global context in large textual sources. We also assessed the effectiveness of current sentence-level relation extraction methods. The TACRED dataset provided the main source of information for our research, which also made use of the BERT (Bidirectional Encoder Representations from Transformers) model's impressive capabilities. The goal was to carefully examine how the Long Short-Term Memory (LSTM) and BERT model performed on the TACRED dataset and assess its precision in extracting relationships between entities embedded within sentences. This project provided insightful information on the relative performance of the LSTM and BERT models in the context of sentence-level relation extraction, which helped to clarify the relative advantages and disadvantages of each model. In order to gain a more comprehensive understanding of the state of the art in this subject, our research also examined the content of literature and research papers addressing sentence and document-level connection extraction strategies. These sources expanded the depth and scope of our research by providing methodology insights and serving as benchmarks for comparison with our own findings.