| import spacy |
| from spacy.tokens import Doc |
| import logging, sys, time |
| from lib.CoNLL_Annotation import CoNLL09_Token |
| import my_utils.file_utils as fu |
| from germalemma import GermaLemma |
| |
| |
| TIGER_CORPUS = "/home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09" |
| OUTPUT_FILE = "/home/daza/datasets/TIGER_conll/tiger_spacy_parsed.conllu" |
| TEXT_OUTPUT = "/home/daza/datasets/TIGER_conll/tiger_all.txt" |
| |
| |
| class WhitespaceTokenizer(object): |
| def __init__(self, vocab): |
| self.vocab = vocab |
| |
| def __call__(self, text): |
| words = text.split(' ') |
| # All tokens 'own' a subsequent space character in this tokenizer |
| spaces = [True] * len(words) |
| return Doc(self.vocab, words=words, spaces=spaces) |
| |
| |
| def get_conll_str(spacy_doc): |
| conll_lines = [] # We want: [ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC] |
| for ix, token in enumerate(spacy_doc): |
| content = (str(ix), token.text, find_germalemma(token.text, token.tag_, token.lemma_), token.pos_, token.tag_, "_", "_", "_", "_", "_") |
| conll_lines.append("\t".join(content)) |
| return "\n".join(conll_lines) |
| |
| |
| # def freeling_lemma_lookup(): |
| # dicts_path = "/home/daza/Frameworks/FreeLing/data/de/dictionary/entries/" |
| |
| def find_germalemma(word, pos, spacy_lemma): |
| simplify_pos = {"ADJA":"ADJ", "ADJD":"ADJ", |
| "NA":"N", "NE":"N", "NN":"N", |
| "ADV":"ADV", "PAV":"ADV", "PROAV":"ADV", "PAVREL":"ADV", "PWAV":"ADV", "PWAVREL":"ADV", |
| "VAFIN":"V", "VAIMP":"V", "VAINF":"V", "VAPP":"V", "VMFIN":"V", "VMINF":"V", |
| "VMPP":"V", "VVFIN":"V", "VVIMP":"V", "VVINF":"V", "VVIZU":"V","VVPP":"V" |
| } |
| # simplify_pos = {"VERB": "V", "ADV": "ADV", "ADJ": "ADJ", "NOUN":"N", "PROPN": "N"} |
| try: |
| return lemmatizer.find_lemma(word, simplify_pos.get(pos, "UNK")) |
| except: |
| return spacy_lemma |
| |
| |
| if __name__ == "__main__": |
| file_has_next, chunk_ix = True, 0 |
| CHUNK_SIZE = 10000 |
| |
| # ===================================================================================== |
| # LOGGING INFO ... |
| # ===================================================================================== |
| logger = logging.getLogger(__name__) |
| console_hdlr = logging.StreamHandler(sys.stdout) |
| file_hdlr = logging.FileHandler(filename=f"logs/Parse_Tiger.SpaCy.log") |
| logging.basicConfig(level=logging.INFO, handlers=[console_hdlr, file_hdlr]) |
| logger.info(f"Chunking TIGER Corpus in chunks of {CHUNK_SIZE} Sentences") |
| |
| # ===================================================================================== |
| # POS TAG DOCUMENTS |
| # ===================================================================================== |
| spacy_de = spacy.load("de_core_news_lg", disable=["ner", "parser"]) |
| spacy_de.tokenizer = WhitespaceTokenizer(spacy_de.vocab) # We won't re-tokenize to respect how the source CoNLL are tokenized! |
| write_out = open(OUTPUT_FILE, "w") |
| lemmatizer = GermaLemma() |
| write_plain = open(TEXT_OUTPUT, "w") |
| |
| start = time.time() |
| total_processed_sents = 0 |
| line_generator = fu.file_generator(TIGER_CORPUS) |
| while file_has_next: |
| sents, gld, file_has_next = fu.get_file_text_chunk(line_generator, chunk_size=CHUNK_SIZE, token_class=CoNLL09_Token) |
| total_processed_sents += len(sents) |
| logger.info(f"Already processed {total_processed_sents} sentences...") |
| for doc in spacy_de.pipe(sents, batch_size=1000, n_process=10): |
| conll_str = get_conll_str(doc) |
| write_out.write(conll_str) |
| write_out.write("\n\n") |
| write_plain.write(" ".join([x.text for x in doc])+"\n") # OPTIONAL: This can be commented for efficiency... |
| |
| end = time.time() |
| logger.info(f"Processing File {TIGER_CORPUS} took {(end - start)} seconds!") |
| |