WSD task based on Chinese BERT model

Word Sense Disambiguation (WSD) is a cornerstone protest in the field of Natural Language Processing (NLP), critical for applications like the machine translation along with information retrieval where the precise understanding of word meanings is paramount. Despite advancements, the task remains arduous due to the intricate nature of language and context variability. This paper introduces a novel approach to WSD by employing the Bidirectional Encoder Representations from Transformers (BERT) base-Chinese model, which is the state-of-the-art pre-trained language model well-known for its bidirectional context comprehension. this research is dedicated to refining the WSD performance through the fine-tuning of BERT, capitalizing on its robust contextual analysis to discern the accurate sense of polysemous words across diverse linguistic scenarios. the paper fine-tunes the model with meticulous adjustments to key hyper-parameters, including the learning rate and maximum sequence length, while training on a specialized Chinese WSD data set. The outcomes of this research's experiments not only significantly surpass traditional WSD methods in regards to precision along with F1 score but also underscore the transformer impact of BERT in this domain. this research's findings advocate for the customization of per-trained models like BERT for specific NLP tasks, thereby setting the stage for future investigations that may encompass broader datasets, more sophisticated model architectures, and the real-world embedding of the models into real-life applications.