Merge branch 'master' into cordex
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commit
b36e07253f
6
.gitignore
vendored
6
.gitignore
vendored
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__pycache__
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tmp
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resources
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tmp
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venv
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build
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*.egg-info
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16
README.md
16
README.md
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@ -4,14 +4,18 @@ Pipeline for parsing a list of arbitrary Slovene strings and assigning
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each to a syntactic structure in the DDD database, generating
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provisional new structures if necessary.
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## Setup
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## Installation
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Most of the scripts come from other repositories and python libraries.
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Run the set-up script:
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Installation requires the [CLASSLA](https://github.com/clarinsi/classla) standard_jos models, as
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well as (for now) the wani.py script from
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[luscenje_struktur](https://gitea.cjvt.si/ozbolt/luscenje_struktur):
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```
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$ scripts/setup.sh
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```
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pip install .
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python -c "import classla; classla.download('sl', dir='resources/classla', type='standard_jos')"
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curl -o resources/wani.py https://gitea.cjvt.si/ozbolt/luscenje_struktur/raw/branch/master/wani.py
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The classla directory and wani.py file do not necessarily need to be placed under resources/, but
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the wrapper script scripts/process.py assumes that they are.
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## Usage
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import shutil
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import codecs
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import tempfile
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import lxml.etree as lxml
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import classla
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import cordex
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import classla.models.parser as classla_manual
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from structure_assignment.constants import *
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from structure_assignment.tweak_conllu import tweak as tweak_conllu
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@ -50,6 +52,65 @@ class Runner:
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pipeline.export_file(output_file_name, 'tei-initial')
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self.cleanup(pipeline)
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def tagged_to_dictionary(self, strings_file_name, input_file_name, output_file_name, input_structure_file_name, output_structure_file_name): # TODO: refactor/tidy
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classla_conllu_file_name = '/tmp/classla.conlu'
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merged_conllu_file_name = '/tmp/merged.conlu'
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parsed_conllu_file_name = '/tmp/parsed.conlu'
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pipeline = Pipeline(self.nlp)
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pipeline.import_file(strings_file_name, 'strings-list')
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pipeline.do_tokenise()
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pipeline.do_tweak_conllu()
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pipeline.do_parse()
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pipeline.export_file(classla_conllu_file_name, 'classla-parsed')
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classla_conllu_file = codecs.open(classla_conllu_file_name, 'r')
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tagged_conllu_file = codecs.open(input_file_name, 'r')
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merged_conllu_file = codecs.open(merged_conllu_file_name, 'w')
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for (classla_line, tagged_line) in zip(classla_conllu_file, tagged_conllu_file):
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classla_line = classla_line.strip()
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tagged_line = tagged_line.strip()
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if ((len(classla_line) == 0 and len(tagged_line) == 0)
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or (classla_line.startswith('#') and tagged_line.startswith('#'))):
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merged_line = classla_line
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else:
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classla_columns = classla_line.split('\t')
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tagged_columns = tagged_line.split('\t')
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assert len(classla_columns) == 10, 'Missing token in classla-generated conllu ({}).'.format(tagged_line)
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assert len(tagged_columns) == 10, 'Missing token in pre-tagged conllu ({}).'.format(classla_line)
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assert classla_columns[1] == tagged_columns[1], 'Pre-tagged token form ({}) does not match classla-generated token form ({}).'.format(classla_columns[1], tagged_columns[1])
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merged_columns = [classla_columns[i] if i in (3,5,9) else tagged_columns[i] for i in range(10)]
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merged_line = '\t'.join(merged_columns)
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merged_conllu_file.write(merged_line + '\n')
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merged_conllu_file.close()
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tagged_conllu_file.close()
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classla_conllu_file.close()
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classla_map = {
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'save_dir':self.classla_directory + '/sl/depparse',
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'save_name':'standard_jos.pt',
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'eval_file':merged_conllu_file_name,
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'output_file':parsed_conllu_file_name,
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'gold_file':merged_conllu_file_name,
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'shorthand':'sl_ssj',
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'mode':'predict',
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'pretrain_file':self.classla_directory + '/sl/pretrain/standard.pt'
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}
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classla_arguments = []
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for (key, value) in classla_map.items():
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classla_arguments += ['--' + key, value]
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classla_manual.main(args=classla_arguments)
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pipeline.import_file(parsed_conllu_file_name, 'classla-parsed')
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pipeline.do_translate_jos()
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pipeline.do_conllu_to_tei()
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pipeline.import_file(input_structure_file_name, 'structures-old')
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self._parse_to_dictionary_sequence(pipeline)
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pipeline.export_file(output_file_name, 'dictionary')
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pipeline.export_file(output_structure_file_name, 'structures-new')
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self.cleanup(pipeline)
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def parse_to_dictionary(self, input_file_name, output_file_name, input_structure_file_name, output_structure_file_name):
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pipeline = Pipeline()
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pipeline.import_file(input_file_name, 'tei-initial')
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