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State of the art research indicates that joint processing (segmentation, tagging and syntactic analysis) achieves better results than a pipeline model. For an e

• syllogism 12y ago
  The Cython parser is basically an implementation of zpar, and achieves slightly faster run-times, and slightly better accuracy (I've added some extra features, and made some nips and tucks). Note that the Stanford label set has 40 labels, so there are about 80 classes to evaluate. The Penn2Malt scheme has 20 labels, so you need to be careful which dependency scheme is being referenced when run-time figures are reported. The way the run-time cost works is, if you extract f features per transition, for c classes, with a beam of size k and n words, you make O(cfkn) feature-lookups, which is the main cost. For the parser.py implementation, most of the speed is coming from greedy search (k=1), and low number of classes (c=3, instead of c=80). Number of feature templates, f, is similar between this parser and zpar. We could add some more templates for label features, and do labelled parsing, and gain about 1% accuracy here, at the cost of being about 40x slower. The only reason I didn't was that it complicates the implementation and presentation slightly. The implementation was all about the blog post. The Cython parser does everything with C data structures, which are manually memory managed. I don't think I'm paying any language overhead compared to a C++ implementation. So you're absolutely right that as more feature templates stack on, and you use more dependency labels, speed goes down. But, the Cython parser has no problem relative to zpar in this respect.
  • habeanf 12y ago
    First things first: hats off on a concise, accurate and efficient implementation in python. But the Cython parser is not doing labeled parsing.. a significant number of feature templates are relevant to labeled parsing. To reach Nivre and Zhang's 2011 results I think you'll find labeled parsing will degrade performance significantly and you'll need more complex code 1. you have more than an order of magnitude more transitions (80 vs 4) 2. you need to add beam search, with early update for training. beam size = 64 to be consistent with zpar 3. an order of magnitude more feature templates, with more complex features So now you'll be evaluating 20x more transitions, on ~64x more candidates, with ~10x more feature templates. You can't multithread properly in python, you can maybe get some more juice by using some form of SIMD (w/ or w/o GPU). Your model is much larger in memory, so any luck you had in L2/L3 caching is probably gone. Python has limits and this is the point you start hitting them. Edit: I see you've updated your comment, I guess we're on the same page. It's nice to see a concise implementation. I wrote one in golang for my masters thesis. I achieved perfect parity with zpar, but it took two order of magnitude more LOC.
    • syllogism 12y ago
      Sorry for the lack of clarity. The Cython parser results that I quoted uses labelled parsing with beam search. There are various versions in the repo, but there's definitely a configuration that runs the Zhang and Nivre (2011) experiment verbatim, with exactly the same feature templates, and the same results that they get. Replicating those results was essential to implementing the parser correctly.
      • habeanf 12y ago
        Apologies, could you add yet more clarity? According to the blog post your results are: Parser Accuracy Speed (w/s) Language LOC Stanford 89.6% 19 Java > 50,000[1] parser.py 89.8% 2,020 Python ~500 Redshift 93.6% 2,580 Cython ~4,000 Are these the labeled parsing results you are referring to? How many sents/sec? Using same PTB data sets as Zhang and Nivre '11?
        • 12y ago