As computational linguists, we rely heavily on data-driven methods to model and interpret language. Corpus linguistics plays a foundational role in Natural Language ...
As computational linguists, we rely heavily on data-driven methods to model and interpret language. Corpus linguistics plays a foundational role in Natural Language Processing (NLP), offering authentic language input for tasks such as machine translation, sentiment analysis, and syntactic parsing. I would like to invite discussion on how the quality, representativeness, and annotation of corpora influence the performance of NLP models. Additionally, what are some challenges you have encountered—or foresee—when applying corpus-based methods to low-resource languages or domain-specific texts?
Let’s also consider ethical implications, such as bias in training data and the digital divide in corpus availability. How can computational linguists ensure that corpus-informed NLP tools remain inclusive, accurate, and socially responsible? Your insights, examples, or relevant experiences are most welcome.