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    <p>Tooling aside...</p> <p>You may remember from theory that there are infinite grammars that define the same language. There are criteria for categorizing grammars and determining which is the "canonical" or "minimal" one for a given language, but in the end, the "best" grammar is the one that's more convenient for the task and tools at hand (remember the transformations of CFGs into LL and LR grammars?).</p> <p>Then, you probably don't need a huge lexicon to parse an sentence in English. There's a lot to be known about a word in languages like German or Latin (or even Spanish), but not in the many times arbitrary and ambiguos English. You should be able to get away with a small lexicon that contains only the key words necessary to arrive to the structure of a sentence. At any rate, the grammar you choose, no matter its size, can be cached in a way that thee tooling can directly use it (i.e., you can skip parsing the grammar).</p> <p>Given that, it could be a good idea to take a look at a simpler parser already worked on by someone else. There must be thousands of those in the literature. Studying different approaches will let you evaluate your own, and may lead you to adopt someone else's.</p> <p>Finally, as I already mentioned, interpreting natural languages is much more about artificial intelligence than about parsing. Because structure determines meaning and meaning determines structure you have to play with both at the same time. An approach I've seen in the literature since the '80s is to let different specialized agents take shots at solving the problem against a "<a href="http://en.wikipedia.org/wiki/Blackboard_system" rel="nofollow">blackboard</a>". With that approach syntatic and semantic analysis happen concurrently.</p>
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    1. POEfficient Context-Free Grammar parser, preferably Python-friendly
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    1. COAs I mentioned in the comment to Paul's post, the lexicon is not an issue in regards to performance. Caching the grammar is obvious and I'm doing it already, so the loading time is a secondary concern. However, it should be mentioned that an NLTK parser pickled to disk and reloaded loads slower than creating a new one from the grammar. Regarding using an existing parser, the problem with the semantic analysis approach I'm using (a variation of Montague grammar / DRT) is that it requires me to write a semantic processing step for each syntactic rule...
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      1. USMax Shawabkeh
    2. CO...and the rules have to be crafted in a certain way. Most wide-coverage grammars I've seen approach the issue by collecting a huge number of long rules empirically extracted from treebanks and assigning probabilities to them. This approach is valid for some uses, but not in my case.
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    3. COI guess my point is that, for natural languages, there isn't enough information to do semantic analysis during the syntactic analysis, and there's not enough semantic information to accurately grasp the syntactic structure with a typical parser. "Mary read that Jane read well. What Jane read, Mary read as well."
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