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parsing.py
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parsing.py
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#!/usr/bin/env python
# coding: utf-8
# In[4]:
from tqdm import tqdm, tnrange
import json
import pandas as pd
import os
import utils
import parsers
# In[ ]:
# # Parse training XML file
# In[5]:
Training = parsers.TrainingParser('../resources/WSD_Evaluation_Framework/Training_Corpora/SemCor/semcor.data.xml')
Training.create_vocab(input_vocab_path = "../resources/semcor.input.vocab.json",
pos_vocab_path = "../resources/semcor.pos.vocab.json",
left_out_vocab_path = "../resources/semcor.leftout.vocab.json",
subsampling_rate=1e-4,
min_count=5)
# # converting eval datasets
# In[6]:
dir_ = "../resources/WSD_Evaluation_Framework/Evaluation_Datasets"
eval_datasets = [i for i in os.listdir(dir_) if i.startswith("se")]
eval_datasets
# In[7]:
for name in eval_datasets:
print("Dataset: {}".format(name))
path = os.path.join(dir_, name)
gold_file = [i for i in os.listdir(path) if i.endswith('gold.key.txt')][0]
gold_file = os.path.join(path, gold_file)
print("using {}".format(gold_file))
df = utils.parse_evaluation(gold_file = gold_file,
babelnet2wordnet = '../resources/babelnet2wordnet.tsv',
babelnet2wndomains = '../resources/babelnet2wndomains.tsv',
babelnet2lexnames = '../resources/babelnet2lexnames.tsv')
base = gold_file.split(".gold.key.txt")[0]
df[['sentence_idx', 'babelnet']].to_csv(base+".gold.babelnet.txt", header=None, index=None, sep=' ')
df[['sentence_idx', 'wordnet_domains']].to_csv(base+".gold.wordnet_domains.txt", header=None, index=None, sep=' ')
df[['sentence_idx', 'lexicographer']].to_csv(base+".gold.lexicographer.txt", header=None, index=None, sep=' ')
# # Gold output vocab (training file semcor)
# In[ ]:
# df = utils.parse_evaluation(gold_file = "../resources/WSD_Evaluation_Framework/Training_Corpora/SemCor/semcor.gold.key.txt",
# babelnet2wordnet = '../resources/babelnet2wordnet.tsv',
# babelnet2wndomains = '../resources/babelnet2wndomains.tsv',
# babelnet2lexnames = '../resources/babelnet2lexnames.tsv')
# In[ ]:
# for net in ['WordNet', 'BabelNet', 'WordNetDomain', 'LexNames']:
# output_vocab = df[net].dropna().unique()
# output_path = "../resources/semcor.vocab.{}.json".format(net)
# print(output_path)
# # with open(output_path, 'w') as f:
# # f.write('\n'.join(output_vocab))
# with open(output_path, 'w') as f:
# json.dump(list(output_vocab), f)
# # Create mapping file between synset types to be used for all purposes
# In[8]:
def create_mapping(output_path = "../resources/mapping.csv",
babelnet2wordnet = '../resources/babelnet2wordnet.tsv',
babelnet2wndomains = '../resources/babelnet2wndomains.tsv',
babelnet2lexnames = '../resources/babelnet2lexnames.tsv'):
"""
creates a mapping csv
:param output_path: path
:param babelnet2wordnet: path
:param babelnet2wordnet: path
:param babelnet2wordnet: path
:return None: saves output csv to output_path
"""
BabelNet = pd.read_csv(babelnet2wordnet, sep = '\t', names = ['babelnet', 'WordNet'])
WordNetDomain = pd.read_csv(babelnet2wndomains, sep = '\t', names = ['babelnet', 'wordnet_domains'])
LexicographerNet = pd.read_csv(babelnet2lexnames, sep = '\t', names = ['babelnet', 'lexicographer'])
df = BabelNet.join(WordNetDomain.set_index('babelnet'), on='babelnet')
df = df.join(LexicographerNet.set_index('babelnet'), on='babelnet')
df.wordnet_domains.fillna("factotum", inplace=True)
df.lexicographer.fillna("misc", inplace=True)
df.to_csv(output_path, index = False)
# In[9]:
create_mapping()
# In[ ]:
# In[ ]:
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