def run(args):
data = loader.load_treebanks(TREEBANK_PATH)
train_data, dev_data, test_data = loader.train_test_split(
data, 0.8, 0.1, 0.1)
words, embeddings = loader.load_word_embeddings(EMBEDDING_PATH)
pcfg = PCFG(train_data)
pcfg.train(train_data)
pcfg.set_oov(OOV, words, embeddings)
if args.generate_output:
output = pcfg.generate_output(test_data)
if args.evaluation:
accs, nb_no_parse = pcfg.predict(test_data[:2])
if args.parse:
corpus = []
with open(args.txt_path, 'r') as f:
corpus = f.read().split('\n')
pcfg.parse_from_txt(corpus)
from pcfg import PCFG
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--corpus", help="training treebank corpus", type=str)
parser.add_argument("--sentences", help="raw token sentences", type=str)
parser.add_argument("--outfile", help="name of the output file", type=str)
args = parser.parse_args()
grammar = PCFG(args.corpus)
grammar.parse_corpus()
grammar.predict(args.sentences, args.outfile)
