Transforming Language Processing: Automatic Spelling Correction Using BiLSTM

Abstract

Automatic spelling correction is an important tool in digital communication and works to capture and correct errors in written text. This paper discusses the transition of spell-checking techniques from traditional rule-based methods to complex neural networks with a focus on Bidirectional Long Short-Term Memory networks. It describes the different types of errors that can occur: nonword and real-word errors, which require deep contextual understanding. Usually, traditional approaches fail to resolve such context-sensitive errors. BiLSTMs are particularly highlighted for their excellent ability to capture complex contextual information by reading text bidirectionally, resulting in more accurate correction for both types, especially within context-sensitive scenarios. They rather permit sequential dependency capture due to their architecture. Extensive comparative analysis shows that BiLSTM is advantageous over the traditional approaches. The result verifies and presents further improvement in the performance. The authors discuss persistent challenges such as data availability for low-resource languages and computational cost as some of the greatest barriers to the advancement of the field. In the paper, future directions are suggested that would include integrating BiLSTMs with other deep learning state-of-the-art methods like attention mechanisms and Transformer architectures for enhanced performance and addressing still-existing limitations.