This is an implementation of "Decomposed Local Models for Coordinate Structure Parsing".
pip install -r requirements.txt
make
usage: main.py train [-h] [--batchsize NUM] [--cachedir DIR] [--devfile FILE]
[--device ID] [--embedfile FILE] [--epoch NUM]
[--format {tree,genia}] [--gradclip VALUE]
[--inputs [{char,postag,elmo,bert-base,bert-large} [{char,postag,elmo,bert-base,bert-large} ...]]]
[--l2 VALUE] [--limit NUM] [--lr VALUE]
[--model KEY=VALUE] [--refresh] [--savedir DIR]
[--seed VALUE] --trainfile FILE
optional arguments:
-h, --help show this help message and exit
--batchsize NUM Number of examples in each mini-batch (default: 20)
--cachedir DIR Cache directory (default: cache)
--devfile FILE Development data file (default: None)
--device ID Device ID (negative value indicates CPU) (default: -1)
--embedfile FILE Pretrained word embedding file (default: None)
--epoch NUM Number of sweeps over the dataset to train (default:
20)
--format {tree,genia}
Training/Development data format (default: tree)
--gradclip VALUE L2 norm threshold of gradient norm (default: 5.0)
--inputs [{char,postag,elmo,bert-base,bert-large} [{char,postag,elmo,bert-base,bert-large} ...]]
Additional inputs for the encoder (default: ('char',
'postag'))
--l2 VALUE Strength of L2 regularization (default: 0.0)
--limit NUM Limit of the number of training samples (default: -1)
--lr VALUE Learning Rate (default: 0.001)
--model KEY=VALUE Model configuration (default: None)
--refresh, -r Refresh cache (default: False)
--savedir DIR Directory to save the model (default: None)
--seed VALUE Random seed (default: None)
--trainfile FILE Training data file (default: None)
In the tree format, a text file looks like following.
(S (NP-SBJ (NP (JJ Influential)(NNS members))(PP (IN of)(NP (DT the)(NNP House)(NP-CCP (NNP-COORD Ways)(CC-CC and)(NNP-COORD Means))(NNP Committee))))(VP (VBD introduced)(NP (NP (NN legislation))(SBAR (WHNP-1 (WDT that))(S (VP (MD would)(VP (VB restrict)(SBAR (WHADVP-2 (WRB how))(S (NP-SBJ (DT the)(JJ new)(NML (NN savings-and-loan)(NN bailout))(NN agency))(VP (MD can)(VP (VB raise)(NP (NN capital))))))(, ,)(S-ADV (VP (VBG creating)(NP (NP (DT another)(JJ potential)(NN obstacle))(PP (TO to)(NP (NP (NP (NML (DT the)(NN government))(POS 's))(NN sale))(PP (IN of)(NP (JJ sick)(NNS thrifts))))))))))))))(. .))
...
It is not necessary to represent one tree in one line.
A tree, however, must not contain nodes labeled as `` or ''.
To exclude such nodes, use data/clean.py.
Coordinate structures consist of CC and COORD nodes.
For further information of the annotation scheme, please refer to "Coordination Annotation Extension in the Penn Tree Bank".
The GENIA format is a special representation used in the original code of "Coordinate Structure Analysis with Global Structural Constraints and Alignment-Based Local Features". In the GENIA format, a sentence and its coordination are annotated in separated files. Each line in a sentence file has sentence ID, word index, word and POS tag fields separated by tabs.
4321 1 Positive JJ
4321 2 and CC
4321 3 negative JJ
4321 4 regulation NN
4321 5 of IN
4321 6 immunoglobulin NN
4321 7 gene NN
4321 8 expression NN
4321 9 by IN
4321 10 a DT
4321 11 novel JJ
4321 12 B-cell-specific JJ
4321 13 enhancer NN
4321 14 element NN
4321 15 . .
...
In the corresponding coordination file, coordinate structures are represented as following.
4321 1 * 1 3 ADJP-COOD
4321 1 1 1 1 ADJP-COOD
# 4321 1 2 2 2 ADJP-COOD
4321 1 3 3 3 ADJP-COOD
...
5432 1 * 14 36 VP-COOD
5432 1 1 14 19 VP-COOD
# 5432 1 2 20 20 VP-COOD
5432 1 3 21 25 VP-COOD
# 5432 1 4 26 26 VP-COOD
# 5432 1 5 27 27 VP-COOD
5432 1 6 28 36 VP-COOD
...
Lines have the following fields separated by tabs.
- Sentence id
- Coordination number
- Conjunct number (
*indicates the coordinate structure itself) - Beginning of the span
- End of the span
- Type of the span
A line beginning with # is not a comment, but regards its span as a separator between conjuncts.
To use the GENIA format files, give a sentence file to --trainfile/--devfile, put the corresponding coordination file in the same directory of the sentence file with the .coord file extension, and specify --format=genia.
To replace POS tags with those from an external file, put the file in the same directory of --trainfile/--devfile with the same basename combined with .tag.ssv file extension.
A sequence of POS tags for a sentence is placed in a line using single white spaces as a delimiter.
POS tag files are automatically loaded when available.
Training with contextualized embedding is enabled by including elmo, bert-base or bert-large in --inputs.
If so, put the contextualized embeddings files in the same directory of --trainfile/--devfile with the same basename combined with .elmo.hdf5, .bert-base.hdf5 or .bert-large.hdf5 file extension.
Do not forget to include other inputs like char and postag in --inputs if needed.
Contextualized embeddings files in the HDF5 format are easily obtained by chantera/chainer_contextualized_embeddings.
data/extract.py helps you extract raw sentences from a tree file.
usage: main.py test [-h] [--device ID]
[--filter {any,simple,not_simple,consecutive,multiple}]
[--limit NUM] --modelfile FILE --testfile FILE
optional arguments:
-h, --help show this help message and exit
--device ID Device ID (negative value indicates CPU) (default: -1)
--filter {any,simple,not_simple,consecutive,multiple}
Filter type for sentence (default: any)
--limit NUM Limit of the number of training samples (default: -1)
--modelfile FILE Trained model file (default: None)
--testfile FILE Test data file (default: None)
usage: main.py parse [-h] [--cembfile FILE] [--device ID] --input FILE
--modelfile FILE [--nbest NUM]
optional arguments:
-h, --help show this help message and exit
--cembfile FILE Contextualized embeddings file (default: None)
--device ID Device ID (negative value indicates CPU) (default: -1)
--input FILE Input text file to parse (default: None)
--modelfile FILE Trained model file (default: None)
--nbest NUM Number of candidates to output (default: 1)
In a target file, each sentence is represented as a sequence of WORD_POSTAG tokens.
Influential_JJ members_NNS of_IN the_DT House_NNP Ways_NNP and_CC Means_NNP Committee_NNP introduced_VBD legislation_NN that_WDT would_MD restrict_VB how_WRB the_DT new_JJ savings-and-loan_NN bailout_NN agency_NN can_MD raise_VB capital_NN ,_, creating_VBG another_DT potential_JJ obstacle_NN to_TO the_DT government_NN 's_POS sale_NN of_IN sick_JJ thrifts_NNS ._.
...
Unlike training/development files for training, a sentence can contain quotation marks.
@inproceedings{teranishi:2019:naacl,
title={Decomposed Local Models for Coordinate Structure Parsing},
author={Teranishi, Hiroki and Shindo, Hiroyuki and Matsumoto, Yuji},
booktitle={Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
year={2019},
location={Minneapolis, Minnesota},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/N19-1343},
doi={10.18653/v1/N19-1343},
pages={3394--3403},
}
Apache License 2.0