Many applications that deal with natural language desire constituency (or syntax) trees that describe the individual phrases in sentences and how they are hierarchically combined. Wikipedia has a nice example.

MeTA provides a constituency parser in the meta::parser namespace. This is a very efficient parser that is based on the shift-reduce parsing algorithm. For more information on the algorithm itself, refer to the following papers:

# Using the parser

First, you will need to download a parser model file from the releases page on the Github repository. MeTA currently supplies two trained shift-reduce parser models:

• A greedy, best-first parser (i.e. a beam size of 1)
• A beam-search parser with a maximum beam size of 4

Choosing between the two models is a time/performance tradeoff. The greedy parser is signifigantly faster than the beam-search parser, but achieves slightly lower accuracy than the beam-search parser. It should be noted, however, that either model is likely much faster than traditional PCFG-based parsers due to their lower algorithmic complexity during parsing.

In order to use the parser, you will need to have a [parser] configuration group in config.toml that looks like the following:

[parser]
prefix = "path/to/parser/model"

You will also likely want a part-of-speech tagging model, as the parser operates over pre-tagged sequences (it does not assign POS tags on its own). You should refer to the part-of-speech tagging tutorial for more information on the part-of-speech tagging models that MeTA provides.

## Interactive parsing

You can interactively parse sentences using the profile tool. See the profile tutorial for a walkthrough of that demo application.

## As an analyzer

The shift-reduce parser is now what powers MeTA’s structural tree feature extraction. In order to use the parser’s supplied tree_analyzer in your analyzer pipeline, you will need to add an analyzer group to config.toml that looks like the following:

[[analyzers]]
method = "tree"
filter = [{type = "icu-tokenizer"}, {type = "ptb-normalizer"}]
features = ["skel", "subtree"]
tagger = "path/to/greedy-tagger/model"
parser = "path/to/sr-parser/model"

There are a few things to note here. First, you must provide a path to a trained parser as well as a trained greedy tagger. The analyzer currently is not configured to use the CRF tagger (though this may be added in the future: patches welcome!).

You may modify the filter chain if you would like, but we strongly recommend sticking with he above setup as it is designed to match the original Penn Treebank tokenization format that the supplied models were trained on.

The supported features that can be specified in the features array are “branch”, “depth”, “semi-skel”, “skel”, “subtree”, and “tag”. These correspond to the feature types given in the structural tree feature paper linked above.

While you could use multiple [[analyzers]] groups for each individual tree feature you would like to compute, it is much preferable to list all of the features you want in the features key to avoid re-parsing sentences for each feature.

## Programmatically

To use the shift-reduce parser inside your own program, your code might look like this:

// load the models
meta::sequence::perceptron tagger{"path/to/perceptron/model/folder"};
meta::parser::sr_parser parser{"path/to/sr-parser/model/folder"};

meta::sequence::sequence seq;

// tag the sequence
tagger.tag(seq);

// parse the tagged sequence
auto tree = parser.parse(seq);

// print the parse tree in an indented, human-readable format
tree.pretty_print(std::cout);

// print the parse tree in a collapsed, machine-readable format
std::ofstream outfile{"my_output_file.trees"};
outfile << tree;

Have a look at the API documentation for the meta::parser::sr_parser class for more information.

# Training the parser

In order to train your own models using our provided programs, you will need to have a copy of the Penn Treebank (v3) extracted into your data prefix. (see the overview tutorial). Your folder structure should look like the following:

prefix
|---- penn-treebank
|---- treebank-3
|---- parsed
|---- mrg
|---- wsj
|---- 00
|---- 01
...
|---- 24


You will need to expand your [parser] configuration group in config.toml to look like the following:

[parser]
prefix = "parser"
treebank = "penn-treebank" # relative to data prefix
corpus = "wsj"
section-size = 99
# these are the standard training/development/testing splits for parsing
train-sections = [2, 21]
dev-sections = [22, 22]
test-sections = [23, 23]

You can also add the following additional keys to configure training behavior:

• train-threads: controls the number of training threads used (defaults to the number of processors on the machine)
• train-algorithm: controls the algorithm used for training. This can be one of the following:
• “early-termination” (default): trains a greedy parser (beam size = 1)
• “beam-search”: trains a parser using beam search
• beam-size: controls the size of the beam used during training. This option is ignored if the algorithm is “early-termination”

There are more options that can be tweaked, but the remaining options must be done programmatically. See the API documentation for the meta::parser::sr_parser class for more information (in particular, the training_options struct). If you want to try different options, you can use the code provided in src/parser/tools/parser_train.cpp as a starting point.

The parser may take several hours to train, depending on your training parameters. The greedy parser (“early-termination”) trains much faster than the beam search parser.

# Testing the parser

If you follow the instructions above, you should be able to test a parser model by running the following:

./parser-test config.toml tree-output

The application will run over the test set configured in config.toml and reports many of the evalb metrics. However, it is always best practice to run the output trees against the standard evalb program itself, which is why the trees are also output to a file.

The greedy parser obtains the following results:

Labeled Recall:    86.9455
Labeled Precision: 86.6949
Labeled F1:        86.82


The beam search parser (with a beam size of 4) obtains the following results:

Labeled Recall:    88.2171
Labeled Precision: 88.0778
Labeled F1:        88.1474