nltk.parse.malt module¶
- class nltk.parse.malt.MaltParser[source]¶
Bases:
ParserI
A class for dependency parsing with MaltParser. The input is the paths to: - (optionally) a maltparser directory - (optionally) the path to a pre-trained MaltParser .mco model file - (optionally) the tagger to use for POS tagging before parsing - (optionally) additional Java arguments
- Example:
>>> from nltk.parse import malt >>> # With MALT_PARSER and MALT_MODEL environment set. >>> mp = malt.MaltParser(model_filename='engmalt.linear-1.7.mco') >>> mp.parse_one('I shot an elephant in my pajamas .'.split()).tree() (shot I (elephant an) (in (pajamas my)) .) >>> # Without MALT_PARSER and MALT_MODEL environment. >>> mp = malt.MaltParser('/home/user/maltparser-1.9.2/', '/home/user/engmalt.linear-1.7.mco') >>> mp.parse_one('I shot an elephant in my pajamas .'.split()).tree() (shot I (elephant an) (in (pajamas my)) .)
- __init__(parser_dirname='', model_filename=None, tagger=None, additional_java_args=None)[source]¶
An interface for parsing with the Malt Parser.
- Parameters:
parser_dirname (str) – The path to the maltparser directory that contains the maltparser-1.x.jar
model_filename (str) – The name of the pre-trained model with .mco file extension. If provided, training will not be required. (see http://www.maltparser.org/mco/mco.html and see http://www.patful.com/chalk/node/185)
tagger (function) – The tagger used to POS tag the raw string before formatting to CONLL format. It should behave like nltk.pos_tag
additional_java_args (list) – This is the additional Java arguments that one can use when calling Maltparser, usually this is the heapsize limits, e.g. additional_java_args=[‘-Xmx1024m’] (see https://goo.gl/mpDBvQ)
- generate_malt_command(inputfilename, outputfilename=None, mode=None)[source]¶
This function generates the maltparser command use at the terminal.
- Parameters:
inputfilename (str) – path to the input file
outputfilename (str) – path to the output file
- parse_sents(sentences, verbose=False, top_relation_label='null')[source]¶
Use MaltParser to parse multiple sentences. Takes a list of sentences, where each sentence is a list of words. Each sentence will be automatically tagged with this MaltParser instance’s tagger.
- Parameters:
sentences – Input sentences to parse
- Returns:
iter(DependencyGraph)
- parse_tagged_sents(sentences, verbose=False, top_relation_label='null')[source]¶
Use MaltParser to parse multiple POS tagged sentences. Takes multiple sentences where each sentence is a list of (word, tag) tuples. The sentences must have already been tokenized and tagged.
- Parameters:
sentences – Input sentences to parse
- Returns:
iter(iter(
DependencyGraph
)) the dependency graph representation of each sentence
- train(depgraphs, verbose=False)[source]¶
Train MaltParser from a list of
DependencyGraph
objects- Parameters:
depgraphs (DependencyGraph) – list of
DependencyGraph
objects for training input data
- nltk.parse.malt.find_malt_model(model_filename)[source]¶
A module to find pre-trained MaltParser model.