An unsupervised dependency parser.
Project description
Introduction
This is an implementation of the unsupervised dependency parser described by Søgaard (2012). The parser is language independent and does not need any training data.
The parser operates in two stages. First, it constructs a directed graph from the words in a sentence using
information on word adjacency,
an (automatically created) list of function words [1],
morphological cues
and information from part-of-speech tags, if available [2].
The resulting graph structure is used to rank the words using the PageRank algorithm (Brin and Page, 1998). In the second stage, the parser constructs a dependency tree from that ranked list of words. If part-of-speech information is available, the parser can make use of universal dependency rules (Naseem et al., 2010).
Installation
Usurper can be easily installed using pip:
pip install Usurper
Usage
Using the usrpr executable
You can use the parser as a standalone program from the command line. Your input text has to be either in CoNLL-X format or in a simple format with one token per line and an empty line between sentences. If your data is part-of-speech tagged, the tags should be separated from the tokens by a tab:
Many JJ people NNS need VBP our PRP$ help NN . . Please UH continue VB our PRP$ important JJ partnership NN . .
General usage information, including a list of supported part-of-speech tagsets, is available via the -h option:
usrpr -h
If you want to use the full parser, i.e. you have part-of-speech tagged input data and you want to use the universal dependency rules, you can invoke the parser like this:
usrpr -t <tag-set> [--conll] <file>
If you do not want to use the universal dependency rules, you can use the --no-rules option:
usrpr --no-rules -t <tag-set> [--conll] <file>
If your data is untagged or you want to ignore the tags, simply omit the -t option (in that case it is not possible to make use of the universal dependency rules):
usrpr [--conll] <file>
Note that the parser tries to automatically identify function words. If your input file is too small, that cannot be done reliably and might have an impact on parser performance.
Using the module
You can easily incorporate the parser into your own Python projects. All you have to do is import usurper.soegaard:
from usurper import soegaard parse = soegaard.parse_sentence(tokens, function_words, no_rules, tags, tagset)
The parse_sentence function returns a networkx DiGraph object. You can convert it into a nested list representation using the export_to_conll_format function in usurper.utils.conll.
The function’s docstring gives more detailed information about the arguments it takes:
parse_sentence(tokens, function_words, no_rules, tags=[], tagset=None)
Parse sentence using the algorithm by Søgaard (2012).
Args:
tokens: list of tokens
function_words: set of function words
no_rules: boolean; true if universal dependency rules should
not be used
tags: list of tags, if available; the nth element of tags
should be the part-of-speech tag associated with the nth
element of tokens
tagset: string identifying one of the supported tagsets
Returns:
A networkx DiGraph representing the dependency structure.
Evaluation
Here is a table giving unlabeled attachment scores (ignoring punctuation) for a couple of languages. Test data for most of the languages is available from the CoNLL-X Shared Task website. Performance for English was evaluated on section 23 of the Penn Treebank.
Language |
no tags |
no rules |
full parser |
|---|---|---|---|
Danish |
30.04 |
37.66 |
38.20 |
English |
20.41 |
40.74 |
40.94 |
German |
18.59 |
33.93 |
39.24 |
Portuguese |
19.86 |
44.86 |
44.50 |
Slovene |
19.70 |
31.41 |
31.39 |
Swedish |
20.75 |
44.69 |
49.21 |
References
Brin, Sergey, Lawrence Page (1998): “The anatomy of a large-scale hypertextual web search engine.” In: Computer Networks and ISDN Systems 30/1–7, 107–117. PDF.
Mihalcea, Rada, Paul Tarau (2004): “TextRank: Bringing order into text.” In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP’04). ACL, 404–411. PDF.
Naseem, Tahira, Harr Chen, Regina Barzilay, Mark Johnson (2010): “Using universal linguistic knowledge to guide grammar induction.” In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP’10). ACL, 1234–1244. PDF.
Petrov, Slav, Dipanjan Das, Ryan McDonald (2012): “A universal part-of-speech tagset.” In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12), 2089–2096. PDF.
Søgaard, Anders (2012): “Unsupervised dependency parsing without training.” In: Natural Language Engineering 18/2, 187–203. Link.
