NAMED ENTITY RECOGNITION USING OPENAI GPT SERIES MODELS
Bohdan M. Pavlyshenko, I. Drozdov · Electronics and Information Technologies · 2023
The amount of information grew dramatically over all available sources. One of the most important parts of it is textual data, so Natural Language Processing is one of the most important areas of research. A growing amount of information demands more sophisticated and effective models and approaches to be introduced. Named entity recognition is a key part of text processing and plays an important role in text understanding, automatic text summarization, translation, etc. A wide range of different approaches were used for named entity recognition, however, the introduction of the transformers architecture with self-attention mechanism made a significant impact on current approaches to Natural Language Processing tasks in general. Most tasks are currently leveraging transformers as a state-of-the-art approach. Meanwhile, simpler transformer architecture in comparison with others grants the possibility of large language models with a huge number of parameters like GPT-3. The main purpose of this article is to investigate how effectively OpenAI GPT series models could recognize named entities in English and Ukrainian texts. The research was based on the CoNLL 2003 dataset one of the most used for such kind of research and the lang-uk team labeled dataset. Due to known possibilities for GPT series models to be more effective with few-shot learning examples, experiments were built with zero, one, and three shots. Moreover, experiments were performed for whole articles and sentence by sentence from the same article to compare results. Different prompts were investigated, and one was chosen for the whole experiment. The estimation of the results was based on the F1 score and specifics of the results. Results demonstrate the overall great performance of the most recent models and the increase in performance from older to newer models. Furthermore, our findings indicate that there is still room for improvement and investigation. Keywords : Named entity recognition, natural language processing, GPT, OpenAI