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I am guessing you might be conflating parsing natural language with parsing something that has a rigid and well defined grammar (like a programming language).

I am guessing you might be conflating parsing natural language with parsing something that has a rigid and well defined grammar (like a programming language). NLP is a whole different beast.

KirinDave 14y ago
> NLP is a whole different beast. The very same patterns that define "packrat-like" parsers (which share a strong relationship to the monadic and "arrow-adic" parsers) can be extended to define things like DFAs and semantic pattern matching. And languages with support for rich, somewhat lazy pattern matching like Haskell and Prolog wipe the floor with eager languages without (e.g., C), which is ideal for semantic analysis. While not an "authority" in the subject, I've spent a lot of time working with some very skilled folks in the field of NLP, Linguistics. Most tools they used (in our case licensed from X/PARC) had C underpinnings for performance, but ultimately consumed specifications that were very much like Prolog or Haskell in character. Talking to some of the linguists who wrote those tools suggested that had GHC existed (or Allegro or a fast prolog been cheaper) then they would have been much easier to write in those languages.
- yummyfajitas 14y ago
  Do you have more info on this? I'd love to read more.
  - KirinDave 14y ago
    I'm afraid I can't say much more beyond what I have without talking out of my rear. But you can read about X/P's XLE project here: http://www2.parc.com/isl/groups/nltt/xle/ http://www2.parc.com/isl/groups/nltt/xle/
- robrenaud 14y ago
  Maybe I've been brainwashed by statistical NLP people, but I think this summarizes my understanding pretty well. http://en.wikipedia.org/wiki/Frederick_Jelinek http://en.wikipedia.org/wiki/Frederick_Jelinek > "Every time I fire a linguist, the performance of the speech recognizer goes up" As far as I know, modern, successful NLP systems don't have much human knowledge baked in and are produced by training on large data sets.

I am guessing you might be conflating parsing natural language with parsing something that has a rigid and well defined grammar (like a programming language). NLP is a whole different beast.

KirinDave 14y ago

> NLP is a whole different beast. The very same patterns that define "packrat-like" parsers (which share a strong relationship to the monadic and "arrow-adic" parsers) can be extended to define things like DFAs and semantic pattern matching. And languages with support for rich, somewhat lazy pattern matching like Haskell and Prolog wipe the floor with eager languages without (e.g., C), which is ideal for semantic analysis. While not an "authority" in the subject, I've spent a lot of time working with some very skilled folks in the field of NLP, Linguistics. Most tools they used (in our case licensed from X/PARC) had C underpinnings for performance, but ultimately consumed specifications that were very much like Prolog or Haskell in character. Talking to some of the linguists who wrote those tools suggested that had GHC existed (or Allegro or a fast prolog been cheaper) then they would have been much easier to write in those languages.

yummyfajitas 14y ago
Do you have more info on this? I'd love to read more.
- KirinDave 14y ago
  I'm afraid I can't say much more beyond what I have without talking out of my rear. But you can read about X/P's XLE project here: http://www2.parc.com/isl/groups/nltt/xle/ http://www2.parc.com/isl/groups/nltt/xle/
robrenaud 14y ago
Maybe I've been brainwashed by statistical NLP people, but I think this summarizes my understanding pretty well. http://en.wikipedia.org/wiki/Frederick_Jelinek http://en.wikipedia.org/wiki/Frederick_Jelinek > "Every time I fire a linguist, the performance of the speech recognizer goes up" As far as I know, modern, successful NLP systems don't have much human knowledge baked in and are produced by training on large data sets.