Computation and its Connotations
A Review of Language Machines by Leif Weatherby
Elias Stengel-Eskin, Aaron Steven White, Sheng Zhang, Benjamin Van Durme · Universal Decompositional Semantic Parsing · SlidesLive
Professional Conference Recording
Maite Taboada
Maite Taboada. Professor. Department of Linguistics, Simon Fraser University. Research: discourse analysis, computational linguistics
The Stanford NLP Group
Performing groundbreaking Natural Language Processing research since 1999.
Harper | Privacy-First Offline Grammar Checker
Blazing-fast, open-source grammar & spell checking that never sends your words to the cloud.
How linguistics learned to stop worrying and love the language models
Language models (LMs) can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.
We Have Always Been Action TheoristsToward a Critical Theory of Language for the Era of “Large Language Models”
Scholars of literature and culture understandably place themselves among the world’s premiere experts on matters of language. But they also know that fields like linguistics and communication have their own ways of studying how people express themselves through speech and written media. A key difference concerns the theories and methodologies...
blowdart/idunno.AtProto.Lexicons
A collection of lexicon implementations for AT Protocol for use with idunno.AtProto
Quantization from the ground up | ngrok blog
A complete guide to what quantization is, how it works, and how it's used to compress large language models
Back-to-basics approach can match or outperform AI in language analysis
A new study led by Dr Andrea Nini at The University of Manchester has found that a grammar-based approach to language analysis can match or outperform advanced AI systems in identifying who wrote a text. The method, called LambdaG, uses patterns in grammar and sentence construction rather than large-scale AI models, offering comparable accuracy ...
How Large Language Models Actually Work
Named Entity Recognition with NLTK and SpaCy
NER is used in many fields in Natural Language Processing (NLP)
Stanford CS336 Language Modeling from Scratch I 2025
Packrat parsing: | Proceedings of the seventh ACM SIGPLAN international conference on Functional programming
For decades we have been using Chomsky's generative system of grammars, particularly context-free grammars (CFGs) and regular expressions (REs), to express the syntax of programming languages and protocols. The power of generative grammars to express ...
