6 ms·

Natural language parsing for the web

56 points by adammichaelc 16y ago

AlexMuir 16y ago
This is exactly the sort od thing that makes a trip to HN worthwhile! Very cool, Thanks to high-school latin I was able to make a bit of sense of the table of parts of speech. Shame I haven't got a use for it ATM.
jcsalterego 16y ago
Interesting, but I wonder how commercial use of the API requires the license: http://otlportal.stanford.edu/techfinder/technology/ID=24472 http://otlportal.stanford.edu/techfinder/technology/ID=24472
- gojomo 16y ago
  From http://nlp.stanford.edu/software/tagger.shtml http://nlp.stanford.edu/software/tagger.shtml The tagger is licensed under the GNU General Public License (v2 or later). The GPL, like other licenses meeting the 'open source definition', has no restrictions on use -- only on proprietary distribution under nonfree licenses.
WildUtah 16y ago
Parser spins and spins with no output for me. Have we already overloaded it?
finin 16y ago
I seem like it's just a part of speech tagger, rather than a parser.
- epochwolf 16y ago
  Wouldn't that make it a lexer then?
  - finin 16y ago
    I suppose so, but for human languages it's much more difficult, since it assigning a type to a token is context dependent.
    - danieldk 16y ago
      And there are unknown words (words that are not covered by the lexicon). But the top-poster is right: the linked website does part of speech tagging, not parsing. Providing a wide-coverage parser for the web is still hard. The number of possible parses for long sentences is enormous. Even if a sentence is not ambiguous to us, grammars allow all kinds of ambiguities. There is a web demo for a Dutch system that is developed in our research group[1], but heap size and time limits are used, to exit gracefully if parsing takes too much time/memory (and sentences with more than 20 words are ignored, since you'll really want to do offline parsing). [1] http://www.let.rug.nl/~vannoord/bin/alpino http://www.let.rug.nl/~vannoord/bin/alpino
      - sqrt17 16y ago
        Statistical methods do quite a good job of pushing that ambiguity back under the rug (see link to Berkeley parser further up, or the work on statistical disambiguation in Alpino). As to the time consumption and complexity, that's a known problem of unification grammars (or just any grammar that does a little bit more) - but see this paper by Matsuzaki et al for efficient techniques to speed this up: http://www-tsujii.is.s.u-tokyo.ac.jp/~matuzaki/paper/ijcai2007.pdf http://www-tsujii.is.s.u-tokyo.ac.jp/~matuzaki/paper/ijcai20...
        danieldk 16y ago
        True, but you still have to build up a forest from which every parse can be extracted (as Alpino does). Of course, it does reduce the cost of ambiguity. E.g., I implemented packing in the Alpino chart generator, and there are very many edges with the same 'semantics' that can be packed, especially since Alpino allows for a lot of optional punctuation, and there are often roots plus subcategorization frames that give multiple inflections. With a beam search, unpacking is a lot more efficient. There are also many other possible optimizations. E.g. with a left-corner parsers you can exclude left-corner spines that will probably not lead to a probable parse (Van Noord, Learning Efficient Parsing, 2009, and other work). And, of course, reduction of lexical ambiguity by restricting frames using a part of speech tagger. Still, even in this best-1, optimized scenario, real-time parsing of long sentences is still hard. So, when parsing large corpora we usually apply time and space limits (which is easy to do in Sicstus Prolog, with good recovery). Thanks for the link to the paper!
zepolen 16y ago
Shouldn't 'fuck' be a verb in this context? go/VB fuck/NN yourself/PRP
- AlexMuir 16y ago
  Imperative I think.
- sqrt17 16y ago
  Pretty much. If you replace it with unambiguous words, go/VB lemon/NN yourself/PRP doesn't sound good, whereas go/VB shave/VB yourself/PRP is fine. IMO, go should also be a VBP, not a VB.
- danieldk 16y ago
  Using my own tagger[1], trained using the Brown corpus: go/VB fuck/VB yourself/PPL It's very much in the amount and kind of training data and features used (assuming that the methodology is sound). [1] http://github.com/langkit/citar http://github.com/langkit/citar
LeBlanc 16y ago
So I was out to dinner tonight and got an email from my VPS saying my disk IO was high. At first I thought someone might have hacked my server, but it turns out that someone posted the link to my site on HN! I've been working on this website for the past couple of weeks. Please email me at andrew [AT] naturalparsing [DOT] com if you have any questions or suggestions, or want to use the API. FYI, right now the API is not using all the capabilities of the Stanford Parser, just the word tagging part. More features will be implemented soon. Let me know if you have any specific requests. Andrew
- ohashi 16y ago
  I hope it was a pleasant reason to be having trouble :) Thanks and keep up the good work!
- raffi 16y ago
  Have you seen: http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/postagger/ http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/postagger/ It's a POS tagger whose output is extremely close (read: identical) to the Stanford POS tagger. It's also much faster. I was able to tag a corpus of 300K sentences with this in 15 minutes. With the Stanford POS tagger it took the entire weekend. Sadly, this tool's license does not allow commercial use and it is not released under the GPL license.
sqrt17 16y ago
Darn, now I'm tempted to write a pure javascript POS tagger just to show that you don't really need anything server-side (or maybe just little bits here and there so the web page doesn't need to load a 20MB model right away - the computational effort, in any case, is not so bad that you couldn't do it in JS). Hmm. Maybe sometime. EDIT: The Stanford POS tagger is more complex and quite a bit slower than anything you'd do on your own. To quantify this, there's methods that are 10x as fast while sacrificing 0.05%-0.2% accuracy. (Or the easy ones that are 100x as fast, but are 1% less accurate - these would be fun to do in JS).
- jacquesm 16y ago
  Bold statements like that need to be backed up with code!
  - deleted 16y ago
    [deleted]
  - deleted 16y ago
    [deleted]
  - sqrt17 16y ago
    I don't have a JS version, but here's a functioning HMM tagger in under 300 lines of Python: http://gist.github.com/503784 http://gist.github.com/503784 (model loading doesn't work yet for some reason, but you see what it's doing in principle). This uses a smoothed trigram HMM, so it should, in principle, be a bit better than NLTK's HMM tagger but not as good as serious POS tagging packages (e.g. hunpos, or the Stanford POS tagger)
- arethuza 16y ago
  To me this looks like a classic case of where it makes sense to keep this kind of processing on the server and make it available as a service - what functional advantages would having a POS tagger locally give you (apart from offline access)?
  - sqrt17 16y ago
d_c 16y ago
Did you develop the underlying POS tagger or the web interface?
pierrefar 16y ago
It doesn't seem to be handling multi-word proper nouns as I thought it would: "Pride and Prejudice is a good book." becomes "Pride/NNP and/CC Prejudice/NNP is/VBZ a/DT good/JJ book/NN ./." I would have thought "Pride and Prejudice" would be lumped together.
- deleted 16y ago
  [deleted]
- raffi 16y ago
  It's a little confusing but this looks like a front-end to Stanford's Part-of-Speech tagger. POS taggers do not group multi-word tokens. This would be the role of a chunker or a parser.
fauigerzigerk 16y ago
I'm waiting for the day when fruit flies finally get recognition as a species and fruit stops flying ;-)
- gtani 16y ago
  I cannot recommend this comment highly enough! I urge you to waste no time to read this comment over and over again.
gtani 16y ago
lots chatter about MorphAdorner recently (relatively) http://dhigger.blogspot.com/2009/08/research-project.html http://dhigger.blogspot.com/2009/08/research-project.html http://workproduct.wordpress.com/2009/01/27/evaluating-pos-taggers-conclusions/ http://workproduct.wordpress.com/2009/01/27/evaluating-pos-t...

56 points by adammichaelc 16y ago

This is exactly the sort od thing that makes a trip to HN worthwhile! Very cool, Thanks to high-school latin I was able to make a bit of sense of the table of parts of speech. Shame I haven't got a use for it ATM.

jcsalterego 16y ago

Interesting, but I wonder how commercial use of the API requires the license: http://otlportal.stanford.edu/techfinder/technology/ID=24472 http://otlportal.stanford.edu/techfinder/technology/ID=24472

gojomo 16y ago
From http://nlp.stanford.edu/software/tagger.shtml http://nlp.stanford.edu/software/tagger.shtml The tagger is licensed under the GNU General Public License (v2 or later). The GPL, like other licenses meeting the 'open source definition', has no restrictions on use -- only on proprietary distribution under nonfree licenses.

WildUtah 16y ago

Parser spins and spins with no output for me. Have we already overloaded it?

finin 16y ago

I seem like it's just a part of speech tagger, rather than a parser.

epochwolf 16y ago
Wouldn't that make it a lexer then?
- finin 16y ago
  I suppose so, but for human languages it's much more difficult, since it assigning a type to a token is context dependent.
  - danieldk 16y ago
    And there are unknown words (words that are not covered by the lexicon). But the top-poster is right: the linked website does part of speech tagging, not parsing. Providing a wide-coverage parser for the web is still hard. The number of possible parses for long sentences is enormous. Even if a sentence is not ambiguous to us, grammars allow all kinds of ambiguities. There is a web demo for a Dutch system that is developed in our research group[1], but heap size and time limits are used, to exit gracefully if parsing takes too much time/memory (and sentences with more than 20 words are ignored, since you'll really want to do offline parsing). [1] http://www.let.rug.nl/~vannoord/bin/alpino http://www.let.rug.nl/~vannoord/bin/alpino
    - sqrt17 16y ago
      Statistical methods do quite a good job of pushing that ambiguity back under the rug (see link to Berkeley parser further up, or the work on statistical disambiguation in Alpino). As to the time consumption and complexity, that's a known problem of unification grammars (or just any grammar that does a little bit more) - but see this paper by Matsuzaki et al for efficient techniques to speed this up: http://www-tsujii.is.s.u-tokyo.ac.jp/~matuzaki/paper/ijcai2007.pdf http://www-tsujii.is.s.u-tokyo.ac.jp/~matuzaki/paper/ijcai20...
      - danieldk 16y ago
        True, but you still have to build up a forest from which every parse can be extracted (as Alpino does). Of course, it does reduce the cost of ambiguity. E.g., I implemented packing in the Alpino chart generator, and there are very many edges with the same 'semantics' that can be packed, especially since Alpino allows for a lot of optional punctuation, and there are often roots plus subcategorization frames that give multiple inflections. With a beam search, unpacking is a lot more efficient. There are also many other possible optimizations. E.g. with a left-corner parsers you can exclude left-corner spines that will probably not lead to a probable parse (Van Noord, Learning Efficient Parsing, 2009, and other work). And, of course, reduction of lexical ambiguity by restricting frames using a part of speech tagger. Still, even in this best-1, optimized scenario, real-time parsing of long sentences is still hard. So, when parsing large corpora we usually apply time and space limits (which is easy to do in Sicstus Prolog, with good recovery). Thanks for the link to the paper!

zepolen 16y ago

Shouldn't 'fuck' be a verb in this context? go/VB fuck/NN yourself/PRP

AlexMuir 16y ago
Imperative I think.
sqrt17 16y ago
Pretty much. If you replace it with unambiguous words, go/VB lemon/NN yourself/PRP doesn't sound good, whereas go/VB shave/VB yourself/PRP is fine. IMO, go should also be a VBP, not a VB.
danieldk 16y ago
Using my own tagger[1], trained using the Brown corpus: go/VB fuck/VB yourself/PPL It's very much in the amount and kind of training data and features used (assuming that the methodology is sound). [1] http://github.com/langkit/citar http://github.com/langkit/citar

LeBlanc 16y ago

So I was out to dinner tonight and got an email from my VPS saying my disk IO was high. At first I thought someone might have hacked my server, but it turns out that someone posted the link to my site on HN! I've been working on this website for the past couple of weeks. Please email me at andrew [AT] naturalparsing [DOT] com if you have any questions or suggestions, or want to use the API. FYI, right now the API is not using all the capabilities of the Stanford Parser, just the word tagging part. More features will be implemented soon. Let me know if you have any specific requests. Andrew

ohashi 16y ago
I hope it was a pleasant reason to be having trouble :) Thanks and keep up the good work!
raffi 16y ago
Have you seen: http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/postagger/ http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/postagger/ It's a POS tagger whose output is extremely close (read: identical) to the Stanford POS tagger. It's also much faster. I was able to tag a corpus of 300K sentences with this in 15 minutes. With the Stanford POS tagger it took the entire weekend. Sadly, this tool's license does not allow commercial use and it is not released under the GPL license.

sqrt17 16y ago

Darn, now I'm tempted to write a pure javascript POS tagger just to show that you don't really need anything server-side (or maybe just little bits here and there so the web page doesn't need to load a 20MB model right away - the computational effort, in any case, is not so bad that you couldn't do it in JS). Hmm. Maybe sometime. EDIT: The Stanford POS tagger is more complex and quite a bit slower than anything you'd do on your own. To quantify this, there's methods that are 10x as fast while sacrificing 0.05%-0.2% accuracy. (Or the easy ones that are 100x as fast, but are 1% less accurate - these would be fun to do in JS).

jacquesm 16y ago
Bold statements like that need to be backed up with code!
- deleted 16y ago
  [deleted]
- deleted 16y ago
  [deleted]
- sqrt17 16y ago
  I don't have a JS version, but here's a functioning HMM tagger in under 300 lines of Python: http://gist.github.com/503784 http://gist.github.com/503784 (model loading doesn't work yet for some reason, but you see what it's doing in principle). This uses a smoothed trigram HMM, so it should, in principle, be a bit better than NLTK's HMM tagger but not as good as serious POS tagging packages (e.g. hunpos, or the Stanford POS tagger)
arethuza 16y ago
To me this looks like a classic case of where it makes sense to keep this kind of processing on the server and make it available as a service - what functional advantages would having a POS tagger locally give you (apart from offline access)?
- sqrt17 16y ago

d_c 16y ago

Did you develop the underlying POS tagger or the web interface?

pierrefar 16y ago

It doesn't seem to be handling multi-word proper nouns as I thought it would: "Pride and Prejudice is a good book." becomes "Pride/NNP and/CC Prejudice/NNP is/VBZ a/DT good/JJ book/NN ./." I would have thought "Pride and Prejudice" would be lumped together.

deleted 16y ago
[deleted]
raffi 16y ago
It's a little confusing but this looks like a front-end to Stanford's Part-of-Speech tagger. POS taggers do not group multi-word tokens. This would be the role of a chunker or a parser.

fauigerzigerk 16y ago

I'm waiting for the day when fruit flies finally get recognition as a species and fruit stops flying ;-)

gtani 16y ago
I cannot recommend this comment highly enough! I urge you to waste no time to read this comment over and over again.

gtani 16y ago

lots chatter about MorphAdorner recently (relatively) http://dhigger.blogspot.com/2009/08/research-project.html http://dhigger.blogspot.com/2009/08/research-project.html http://workproduct.wordpress.com/2009/01/27/evaluating-pos-taggers-conclusions/ http://workproduct.wordpress.com/2009/01/27/evaluating-pos-t...