Tom Wolfe takes on linguistics

From my experience most people who say Chomsky is wrong have in most cases never actually read Chomsky's papers. Trust me it's really hard to finish any of his actual papers, even as someone who majored computational linguistics. I'm pretty sure 99% of people on this thread who are throwing around the term "generative linguistics" casually have no idea what it actually is in detail. Maybe they read some wikipedia article, maybe they watched a 45 minute Youtube video of some famous guy talking about it.

I see it as a same phenomenon as people criticizing that "Blue ocean" theory is wrong, "Black swan" theory is wrong, "Lean startups" theory is wrong, all without actually having read and thought about any of those books and basing their criticism on the shallow knowledge they picked up from blogs, which even I have been guilty of.

My thought: The approach by Chomsky and others in this field may not be in fashion nowadays, but it does provide a significant foundation on top of which many others build their theories. Also for these significant theories there is no "right" or "wrong", the whole point is the theory does exist and it gives us something to think about. Saying Karl Marx was wrong is a foolish thing to say because right or wrong is not what matters. Maybe thousands of years later in the future things may change and communism may end up becoming the perfect ideology for our society then. It's all contextual.

It's time to read the Old Testament story of the Tower of Babel once more. That's always been a fascinating story for me ... the notion that people were forming a real or virtual tower that threatened divine order and so needed to be split by making their speech mutually unintelligible.

Our goal is to build high-quality, sophisticated NLP solutions for "cleantext" : text that has been professionally written and copy-edited, so that it doesn't have obvious errors of spelling, grammar, punctuation, etc.

What makes our work different is that, unlike almost all other research in NLP, we don't depend on human-annotated data (like the Penn TreeBank) for training and evaluation. Instead, we build our models into lossless data compressors, and evaluate by invoking the compressor on a large text corpus.

In addition to this change in evaluation methodology, we are also deeply interested in the empirical study of text - that is, in the questions of traditional syntax research. For example, an important part of English syntax relates to verb argument structure: verbs differ in the types of arguments they can accept. Some verbs can accept infinitive complements, that-complements, or indirect objects; others cannot. Our system contains a module that handles argument structure and forwards this information to the parser and compressor.

I wrote a book presenting a rationale for why scientists should be deeply interested in large-scale, lossless data compression. Roughly, empirical data compression research is a variation of the scientific method: if theory A achieves better compression than theory B, then A is closer to the truth. Scientists can use this principle to search systematically through theory-space and find high-quality theories.

http://arxiv.org/abs/1104.5466

Interestingly, compression has a direct link to generation. By sending random bits into the decompressor, you obtain sample sentences that illustrate the model's "idea" of what English text looks like. By studying the discrepancy between the sample sentences and real text, you can discover ways to improve the model. This is a very productive technique that we use routinely. Chomsky declared that his goal was to build a machine that generates grammatical English sentences, but none of his followers actually tried to build such a device. So in some sense our research is actually closer to Chomsky's stated goal than what Chomsky's followers are doing.

I would love to hear from people who might want to collaborate on linguistics work, or use the data compression idea to explore other fields (eg. computer vision, bioinformatics, astronomy).