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Copyright 1999, 2000, 2001, 2005, 2006 Brown University, Providence, RI.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at


Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Basic Usage

The parser (found in the subdirectory PARSE) expects sentences delimited by <s> ... </s>, and outputs the parsed versions in Penn treebank style. The <s> and </s> must be separated by spaces from all other text. So if the input is:

<s> (``He'll work at the factory.'') </s>

the output will be (to stdout):

(S1 (PRN (-LRB- -LRB-) (S (`` ``) (NP (PRP He)) (VP (MD 'll) (VP (VB work) (PP (IN at) (NP (DT the) (NN factory))))) (. .) ('' '')) (-RRB- -RRB-)))

If you want to make it slightly easier for humans to read, use the command line argument -P (pretty print), in which case you will get:

(S1 (PRN (-LRB- -LRB-)
     (S (`` ``)
      (NP (PRP He))
      (VP (MD 'll)
       (VP (VB work) (PP (IN at) (NP (DT the) (NN factory)))))
      (. .)
      ('' ''))
     (-RRB- -RRB-)))

The parser will take input from either stdin, or, if given the name of a file, from that file. So in the latter case the call to the parser would be:

shell> parseIt /path/to/model/dir/ /path/to/file/with/sentences

For example:

shell> parseIt ../DATA/EN/ input-sentences.sgml

(Note that as the parser is currently distributed with three separate DATA directories, one each for English, Chinese, and English Language Modeling. The distributed parsing model is trained on an AUX-ified version of Wall Street Journal. More models (including a non-AUX-ified version) can be obtained with the ModelFetcher module in the Python library.)

As indicated above, the parser will first tokenize the input. If you do not want to to tokenize (for some reason you are handing it pretokenized input, as you would do if you were testing it's performance on the treebank), give it a -K option and pass space separated tokens:

<s> ( `` He 'll work at the factory . '' ) </s>

Compilation instructions

The easiest way is to run make in the top-level Makefile which will build the whole system. To just build the parser, run make PARSE. To only build the training tools for the first-stage parser, run make TRAIN.

n-best Parsing

The parser can produce n-best parses. So if you want the 50 highest scoring parses rather than just the highest scoring one, just add -N50 to the command line.

In n-best mode the output format is slightly different:

number-of-parses sentence-indicator-string



The sentence indicator string will typically just a sentence number. However, if the input to the parser is of the form (with this exact spacing):

<s sentence-id > ... </s>

then the sentence-id provided will be used instead. This is useful if, e.g., you want to know where article boundaries are.

Other options

The -S flag tells the parser to remain silent when it cannot parse a sentence (it just goes on to the next one).

The parser can now parse Chinese. It requires that the Chinese characters already be grouped into words. Assuming you have trained on the Chinese Treebank from LDC (see the README for the TRAIN programs), you tell the parser to be expecting Chinese by giving it the command line option -LCh. (The default is English, which is also be specified by -LEn.) The files you need to train Chinese are in DATA/CH/.

By default, the parser will skip any sentence consisting of more 100 tokens. To change this to 200 you give it the command line argument -l200.

The parser is set to be case sensitive. To make it case insensitive add the command line flag -C.

Currently there are various array sizes that make 400 the absolute maximum sentence length. To allow for longer sentences change (in Feature.h):

#define MAXSENTLEN 400

Similarly to allow for a larger dictionary of words from training, increase:

#define MAXNUMWORDS 500000

To see debugging information give it the on-line argument -d<number> where the <number> is > 10. As the numbers get larger, the verbosity of the information increases.


The subdirectory TRAIN contains the programs used to collect the statistics the parser requires from treebank data. As the parser comes with the statistics it needs you will only need this if you want to try experiments with the parser on more (or less, or different) treebank data. For more information see the README file in TRAIN.

Language Modeling

To use the parser as the language model described in Charniak (ACL 2001) you must first retrain the data using the settings found in DATA/LM/.

Then give parseIt a -M command line argument. If the data is from speech, and thus all one case, also use the case-insensitive (-C) flag.

The output in -M mode is of the form:

log-grammar-probability log-trigram-probability log-mixed-probability

Again, if the data is from speech and has a limited vocabulary, it will often be the case that the parser will have a very difficult time finding a parse because of incorrect words (or, in simulated speech output, the presence of "unk" the unknown word replacement), and there will be many parses with equally bad probabilities. In such cases the pruning that keeps memory in bounds for 50-best parsing fails. So just use 1-best, or maybe 10-best.

Faster Parsing

The default speed/accuracy setting should give you the results in the published papers. It is, however, easy to get faster parsing at the expense of some accuracy. So a command line argument of -T50 costs you about a percent of parsing accuracy, but rather than 1.4 sentences/second [editor's note: your mileage may vary] you will get better than 6 sentences/second. (The default is -T210.)

Multi-threaded version

[Update 2013] Using more than one thread is not currently recommended as there appear to be thread safety issues.

parseIt is multithreaded. It currently defaults to using a single thread. To change this, use the command line argument, -t4 to have it use, e,g, 4 threads. To change the maximum number of threads, change the following line in Features.h and recompile parseIt:

#define MAXNUMTHREADS [maximum number of threads]

The original non-threaded parseIt is available as oparseIt (has fewer features/bugfixes than parseIt). However, parseIt with a single thread should be safe to run.


evalTree takes Penn Treebank parse trees from stdin, and outputs to stdout:

sentence-number log2(parse-tree-probability)

for each tree, one per line. evalTree can be run using the following:

shell> evalTree /path/to/model/dir/

If the tree is assigned zero probability or the parse otherwise fails, it returns 0 for the log2 probability. In some cases, this may happen less often if the n-best list size is expanded.

If the sentence is parsable with the model, evalTree now returns the parser's log prob correctly 99.9% of the time (this used to be about 87%-95%). Due to quirks in the parsing model, the remaining 0.1% is both hard to detect or fix.

Parsing from tagged input

If you have tags from an external part-of-speech tagger or lexicon, you can now strongly encourage the parser to use these tags. This can now be done using a command such as the following:

shell> parseIt -K -Einput.tags /path/to/model/dir/ input.sgml

where input.sgml looks something like this:

<s> This is a test sentence . </s>

and input.tags looks something like this:

This DT
is VBZ
a DT
test NN
sentence NN
. .

Each token is given a list of zero or more tags and sentences are separated by --- (three hyphens). Tokens and tags are whitespace delimited. If a token is given zero tags, the standard tagging mechanism will be employed for tagging that token. If a token is given multiple tags, they will each be considered.

Note that the tokenization must match exactly between these files (tokens are space-separated in input.sgml). To ensure that tokenization matches, you should pretokenize your input and supply the -K flag.

Frequently confusing errors

  1. Parser provides no output at all:

    This is most likely caused by not having spaces around the <s> and </s> brackets, i.e.,:

    <s>This is a test sentence.</s>

    instead of:

    <s> This is a test sentence. </s>
  2. When retraining: Couldn't find term: _____ pSgT: InputTree.C:206: InputTree* InputTree::newParse(std::istream&, int&, InputTree*): Assertion `Term::get(trm)' failed.

    This means the training data contains an unknown term (phrasal or part of speech type). You'll need to add the appropriate entry to terms.txt in the model you're training. See the README in TRAIN for more details.

If you're still stuck, check the other README files then consider asking StackOverflow or filing a bug.