Package edu.stanford.nlp.parser.lexparser

This package contains implementations of three parsers for natural language text.

See:
          Description

Interface Summary
TreebankLangParserParams Contains fields necessary to get the parser to parse an arbitrary treebank.
 

Class Summary
ChineseTreebankParserParams Parameter file for parsing the Penn Chinese Treebank.
ChineseUnknownWordModel Stores, trains, and scores with an unknown word model.
EnglishTreebankParserParams Parser parameters for the Penn English Treebank (WSJ, Brown, Switchboard)
FactoredParser  
LexicalizedParser A reasonably good lexicalized PCFG parser.
ParentAnnotationStats See what parent annotation helps in treebank, based on support and KL divergence.
SisterAnnotationStats See what parent annotation helps in treebank, based on support and KL divergence.
 

Package edu.stanford.nlp.parser.lexparser Description

This package contains implementations of three parsers for natural language text. There is an accurate unlexicalized probabilistic context-free grammar (PCFG) parser, a lexical dependency parser, and a factored, lexicalized probabilistic context free grammar parser, which does joint inference over the first two parsers. For many purposes, we recommend just using the unlexicalized PCFG. With a well-engineered grammar (as supplied), it is fast, accurate, requires much less memory, and in many circumstances, lexical preferences are unavailable or inaccurate across domains or genres and it will perform just as well as a lexicalized parser. However, the factored parser will sometimes provide greater accuracy through knowledge of lexical dependencies. Using the dependency parser by itself is not very useful.

The factored parser and the unlexicalized PCFG parser are described in:

The factored parser uses a product-of-experts model, where the preferences of a unlexicalized PCFG parser and a lexicalized dependency parser are combined by a third parser, which does exact search using precalculated A* outside estimates.

All the internal guts of the parser are in one file, FactoredParser.java, and are not exposed as public.

LexicalizedParser provides a simple interface, for either training a parser from a treebank, or parsing text using an already serialized grammar.

End user usage

Requirements

You need Java (preferably JDK1.4+) installed on your system, and java in your PATH where commands are looked for.

You need a machine with a fair amount of memory. Required memory depends on the choice of parser, the size of the grammar, and other factors like presence of numerous unknown words To run the PCFG parser on sentences of up to 40 words you need 100 Mb of memory. To be able to handle longer sentence, you need more (to parse sentences up to 100 words, you need 400 Mb. For running the Factored Parser, 500-600 Mb is needed for dealing with sentences up to 40 words (which are quite typical in newsire!). Training a new lexicalzed parser requires about 1500m of memory; less for a PCFG.

You need a serialized parser model (grammars, lexicon, etc.). Four are provided (compressed) (in /u/nlp/data/lexparser for local users, or in the root directory of the distributed version). There is serializedFactoredParser.gz and serializedPCFGParser.gz for English, and serializedChineseFactoredParser.gz and serializedChinesePCFGParser.gz for Chinese. And you need the parser code accessible. This can be done by having the supplied javanlp.jar in your CLASSPATH. Then if you have some sentences in test.txt (as plain text), the following commands should work.

Command line usage

Parsing a local text file:

java -mx100m edu.stanford.nlp.parser.lexparser.LexicalizedParser serializedPCFGParser.gz test.txt

Parsing a document over the web:

java -mx100m edu.stanford.nlp.parser.lexparser.LexicalizedParser serializedPCFGParser.gz http://nlp.stanford.edu/~danklein/project-parsing.shtml
NB: This program just does very rudimentary stripping of HTML tags, and so it'll work okay on plain text web pages, but it won't work adequately on most complex commercial script-driven pages.

Parsing a Chinese sentence (in the default input encoding of GB18030 -- and you'll need the right fonts to see the output correctly):

java -mx100m edu.stanford.nlp.parser.lexparser.LexicalizedParser -tLPP edu.stanford.nlp.parser.lexparser.ChineseTreebankParserParams serializedChinesePCFGParser.gz chinese-onesent
or for Unicode (UTF-8) format files:
java -mx100m edu.stanford.nlp.parser.lexparser.LexicalizedParser -tLPP edu.stanford.nlp.parser.lexparser.ChineseTreebankParserParams -encoding UTF-8 serializedChinesePCFGParser.gz chinese-onesent-utf

The program has many options. The most useful end-user option is -maxLength n which determines the maximum length sentence that the parser will parser. Longer sentences are skipped, with a message printed to stderr.

Input formatting and tokenization options

The parser supports many different input formats: tokenized/not, sentences/not, and tagged/not.

The input may be tokenized or not, and users may supply their own tokenizers. The input is by default assumed to not be tokenized; if the input is tokenized, supply the option -tokenized. If the input is not tokenized, you may supply the name of a tokenizer class with -tokenizer tokenizerClassName; otherwise the default tokenizer (edu.stanford.nlp.processor.PTBTokenizer) is used.

The input may have already been split into sentences or not. The input is by default assumed to be not split; if sentences are split, supply the option -sentences delimitingToken, where the delimiting token may be any string. If the delimiting token is sDelimited the parser will accept input in which sentences are marked XML-style with <s> ... </s> (the same format as the input to Eugene Charniak's parser). If the delimiting token is newline the parser will assume that each line of the file is a sentence.

Finally, the input may be tagged or not. If it is tagged, it assumes that words and tags are separated by a non-whitespace separating character such as '/' or '_'. You may supply the option -tagSeparator tagSeparator to specify a tag separator; otherwise the default '/' is used.

We do not at present provide a Chinese word segmenter. We assume that Chinese input has already been word-segmented according to Penn Chinese Treebank conventions). Choosing Chinese with -tLPP edu.stanford.nlp.parser.lexparser.ChineseTreebankParserParams makes this space separated words the default tokenization.

Programatic usage

LexicalizedParser would more usually be called programmatically. It implements a couple of useful interfaces that provide for simple use: edu.stanford.nlp.parser.ViterbiParser and edu.stanford.nlp.process.Appliable. The following simple class shows typical usage:

import java.util.*;
import edu.stanford.nlp.trees.*;
import edu.stanford.nlp.parser.lexparser.LexicalizedParser;

class Demo {
public static void main(String[] args) {
  LexicalizedParser lp = new LexicalizedParser("serializedPCFGParser.gz");
  String[] sent = { "This", "is", "an", "easy", "sentence", "." };
  Tree parse = (Tree) lp.apply(Arrays.asList(sent));
  parse.pennPrint();
  System.out.println(parse.dependencies(new CollinsHeadFinder()));
}
}

In a usage such as this, the parser expects sentences already tokenized according to Penn Treebank conventions. For arbitrary text, prior processing must be done to achieve such tokenization (a simple example of doing this is provided in the main method of LexicalizedParser).

Implementation notes. The current version uses class objects as temporary objects to avoid short-lived object creation, and as global numberer spaces. Because of this, the parser doesn't support concurrent usage in multiple threads.

Author:
Dan Klein, Christopher Manning, Roger Levy, Teg Grenager


Stanford NLP Group