What is one challenge that neural networks help overcome in NLP?

Neural networks are particularly effective in natural language processing (NLP) due to their ability to learn complex patterns and relationships in data. One of the significant challenges in NLP is disambiguating words with multiple meanings, often referred to as lexical ambiguity. For instance, the word "bank" can refer to the financial institution or the side of a river. Traditional methods may struggle to determine the correct context for such words, while neural networks, especially those utilizing techniques like word embeddings and attention mechanisms, can capture contextual nuances and meanings based on surrounding words in a sentence. By processing vast amounts of text data, neural networks can learn contextual representations that help in accurately interpreting and disambiguating words according to their usage in different contexts. This ability is crucial for tasks like sentiment analysis, machine translation, and question-answering systems, making neural networks a vital tool for overcoming this challenge in NLP.