Hey NLP is not only machine learning it also has to do with language.
But you are not alone!
Here are some of my classics that deal with linguistics
Linguistic Fundamentals for Natural Language Processing
by @emilymbender
May all-time favorite & silver bullet! A must read.
The beauty of this book is that it explains the most important linguistic concepts in short chunks and illustrates this with easy to understand examples
You are pulled into the topics very immersively and you can not stop reading! You literally soak up the knowledge
Another classic is: Semantics in Generative Grammar by Irene Heim and Angelika Kratzer not an easy read and not suitable for beginners but if you are interested in semantics and syntax you should have a look.
At its core it consists of a population of Kenyon cells, which receive inputs from multiple sensory modalities.
These cells are inhibited by the anterior paired lateral neuron, thus creating a sparse high dimensional representation of the inputs.
In this work they study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task.
When deep learning meets causal inference: a computational framework for drug repurposing from real-world data - Drug repurposing is an effective strat to iden. new uses for existing drugs
Narratives: fMRI data for evaluating models of naturalistic language comprehension - MRI datasets collected while human subjects listened to naturalistic spoken stories.
The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words).
This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension.
Semantic annotation or tagging is the process of attaching to a text document or other unstructured content, metadata about concepts (e.g., people, places, organizations, products or topics) relevant to it.