We will discuss fundamental linguistics and data science concepts that underpin the ability to extract signal from text. This talk brings theoretical context to general data science and NLP approaches. Topics will include the linguistic grounding of large language models (LLMs), basic NLP methods, and common pitfalls in textual analysis. We will also present some tools developed by our lab that can act as powerful lenses for textual data. Some examples we will use to approach these topics include: word frequency and distributions, Zipf’s law, the Distributional Hypothesis, allotaxonometry, sentiment, time series, and scale.
Takeaways from this talk will be theoretical background and tools that support a holistic approach to extracting signal from text, empowering attendees to engage critically with NLP applications in the wild and to deploy NLP approaches responsibly and creatively.