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modified aligner

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rjawor 2019-06-12 14:44:50 +02:00
parent 4a21673352
commit e00f27d62f
3 changed files with 36 additions and 23 deletions
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1
fast-aligner/.gitignore vendored
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@ -1 +1,2 @@
corpora/
fast_align

12
fast-aligner/Makefile
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@ -30,16 +30,16 @@ corpora/$(CORPUS_NAME)/trg.dict:
./collect_dict.py $(TRG_LANG) $(SRC_LANG) $(DICTIONARY_WEIGHT) > $@
corpora/$(CORPUS_NAME)/src.lem: corpora/$(CORPUS_NAME)/src.txt
/usr/local/bin/concordia-sentence-tokenizer -c ../concordia.cfg < $< | ./sentence_lemmatizer.py $(SRC_LANG) > $@
corpora/$(CORPUS_NAME)/src.norm corpora/$(CORPUS_NAME)/src.lem: corpora/$(CORPUS_NAME)/src.txt
./sentence_lemmatizer.py $< $(SRC_LANG) corpora/$(CORPUS_NAME)/src.norm corpora/$(CORPUS_NAME)/src.lem
corpora/$(CORPUS_NAME)/trg.lem: corpora/$(CORPUS_NAME)/trg.txt
/usr/local/bin/concordia-sentence-tokenizer -c ../concordia.cfg < $< | ./sentence_lemmatizer.py $(TRG_LANG) > $@
corpora/$(CORPUS_NAME)/trg.norm corpora/$(CORPUS_NAME)/trg.lem: corpora/$(CORPUS_NAME)/trg.txt
./sentence_lemmatizer.py $< $(TRG_LANG) corpora/$(CORPUS_NAME)/trg.norm corpora/$(CORPUS_NAME)/trg.lem
corpora/$(CORPUS_NAME)/src_clean.txt corpora/$(CORPUS_NAME)/src_clean.lem corpora/$(CORPUS_NAME)/trg_clean.txt corpora/$(CORPUS_NAME)/ids_clean.txt corpora/$(CORPUS_NAME)/falign_corpus.txt: corpora/$(CORPUS_NAME)/src.txt corpora/$(CORPUS_NAME)/trg.txt corpora/$(CORPUS_NAME)/ids.txt corpora/$(CORPUS_NAME)/src.lem corpora/$(CORPUS_NAME)/trg.lem corpora/$(CORPUS_NAME)/src.dict corpora/$(CORPUS_NAME)/trg.dict
./prepare_corpus.py corpora/$(CORPUS_NAME)/src.txt corpora/$(CORPUS_NAME)/trg.txt corpora/$(CORPUS_NAME)/ids.txt corpora/$(CORPUS_NAME)/src.lem corpora/$(CORPUS_NAME)/trg.lem corpora/$(CORPUS_NAME)/src.dict corpora/$(CORPUS_NAME)/trg.dict corpora/$(CORPUS_NAME)/src_clean.txt corpora/$(CORPUS_NAME)/src_clean.lem corpora/$(CORPUS_NAME)/trg_clean.txt corpora/$(CORPUS_NAME)/ids_clean.txt corpora/$(CORPUS_NAME)/falign_corpus.txt $(SRC_LANG) $(TRG_LANG)
./prepare_corpus.py corpora/$(CORPUS_NAME)/src.norm corpora/$(CORPUS_NAME)/trg.norm corpora/$(CORPUS_NAME)/ids.txt corpora/$(CORPUS_NAME)/src.lem corpora/$(CORPUS_NAME)/trg.lem corpora/$(CORPUS_NAME)/src.dict corpora/$(CORPUS_NAME)/trg.dict corpora/$(CORPUS_NAME)/src_clean.txt corpora/$(CORPUS_NAME)/src_clean.lem corpora/$(CORPUS_NAME)/trg_clean.txt corpora/$(CORPUS_NAME)/ids_clean.txt corpora/$(CORPUS_NAME)/falign_corpus.txt $(SRC_LANG) $(TRG_LANG)
corpora/$(CORPUS_NAME)/falign_result.txt: corpora/$(CORPUS_NAME)/falign_corpus.txt
fast_align -i $< -d -o -v > $@
./fast_align -i $< -d -o -v > $@

46
fast-aligner/sentence_lemmatizer.py
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@ -9,29 +9,41 @@ BUFFER_SIZE = 500
def lemmatize_sentences(language_code, sentences):
data = {
'operation': 'lemmatizeAll',
'languageCode':language_code,
'lemmatize': True,
'language':language_code,
'sentences':sentences
}
address = 'http://localhost:8800'
response = requests.post(address, data = json.dumps(data))
response.encoding = 'utf-8'
response = requests.post(url = 'http://127.0.0.1:10002/preprocess', json = data)
response_json = json.loads(response.text)
return '\n'.join(response_json['lemmatizedSentences'])
result = {'normalized':[], 'lemmatized':[]}
print(response_json)
for processed_sentence in response_json['processed_sentences']:
result['normalized'].append(processed_sentence['normalized'])
result['lemmatized'].append(processed_sentence['tokens'])
return result
def write_result(result, norm_file, lem_file):
for s in result['normalized']:
norm_file.write(s+'\n')
for s in result['lemmatized']:
lem_file.write(s+'\n')
language_code = sys.argv[1]
file_name = sys.argv[1]
language_code = sys.argv[2]
norm_output_name = sys.argv[3]
lem_output_name = sys.argv[3]
sentences_buffer = []
for line in sys.stdin:
sentences_buffer.append(line.rstrip())
if len(sentences_buffer) == BUFFER_SIZE:
print(lemmatize_sentences(language_code,sentences_buffer))
sentences_buffer = []
with open(file_name) as in_file, open(norm_output_name, 'w') as out_norm, open(lem_output_name, 'w') as out_lem:
for line in in_file:
sentences_buffer.append(line.rstrip())
if len(sentences_buffer) == BUFFER_SIZE:
write_result(lemmatize_sentences(language_code,sentences_buffer), out_norm, out_lem)
sentences_buffer = []
if len(sentences_buffer) > 0:
print(lemmatize_sentences(language_code,sentences_buffer))
if len(sentences_buffer) > 0:
write_result(lemmatize_sentences(language_code,sentences_buffer), out_norm, out_lem)