A python script to convert annotated data in standoff format (brat annotation tool) to the formats expected by Stanford NER and Relation Extractor models

standoff2corenlp.py

# A python script to turn annotated data in standoff format (brat annotation tool) to the formats expected by Stanford NER and Relation Extractor models
# - NER format based on: http://nlp.stanford.edu/software/crf-faq.html#a
# - RE format based on: http://nlp.stanford.edu/software/relationExtractor.html#training

# Usage:
# 1) Install the pycorenlp package
# 2) Run CoreNLP server (change CORENLP_SERVER_ADDRESS if needed)
# 3) Place .ann and .txt files from brat in the location specified in DATA_DIRECTORY
# 4) Run this script

# Cross-sentence annotation is not supported

from pycorenlp import StanfordCoreNLP
import os
from os import listdir
from os.path import isfile, join

DEFAULT_OTHER_ANNO = 'O'
STANDOFF_ENTITY_PREFIX = 'T'
STANDOFF_RELATION_PREFIX = 'R'
DATA_DIRECTORY = 'data'
OUTPUT_DIRECTORY = 'output'
CORENLP_SERVER_ADDRESS = 'http://localhost:9000'

NER_TRAINING_DATA_OUTPUT_PATH = join(OUTPUT_DIRECTORY, 'ner-crf-training-data.tsv')
RE_TRAINING_DATA_OUTPUT_PATH = join(OUTPUT_DIRECTORY, 're-training-data.corp')

if os.path.exists(OUTPUT_DIRECTORY):
    if os.path.exists(NER_TRAINING_DATA_OUTPUT_PATH):
        os.remove(NER_TRAINING_DATA_OUTPUT_PATH)
    if os.path.exists(RE_TRAINING_DATA_OUTPUT_PATH):
        os.remove(RE_TRAINING_DATA_OUTPUT_PATH)
else:
    os.makedirs(OUTPUT_DIRECTORY)

sentence_count = 0
nlp = StanfordCoreNLP(CORENLP_SERVER_ADDRESS)

# looping through .ann files in the data directory
ann_data_files = [f for f in listdir(DATA_DIRECTORY) if isfile(join(DATA_DIRECTORY, f)) and f.split('.')[1] == 'ann']

for file in ann_data_files:
    entities = []
    relations = []

    # process .ann file - place entities and relations into 2 seperate lists of tuples
    with open(join(DATA_DIRECTORY, file), 'r') as document_anno_file:
        lines = document_anno_file.readlines()
        for line in lines:
            standoff_line = line.split()
            if standoff_line[0][0] == STANDOFF_ENTITY_PREFIX:
                entity = {}
                entity['standoff_id'] = int(standoff_line[0][1:])
                entity['entity_type'] = standoff_line[1].capitalize()
                entity['offset_start'] = int(standoff_line[2])
                entity['offset_end'] = int(standoff_line[3])
                entity['word'] = standoff_line[4]
                entities.append(entity)

            elif standoff_line[0][0] == STANDOFF_RELATION_PREFIX:
                relation = {}
                relation['standoff_id'] = int(standoff_line[0][1:])
                relation['name'] = standoff_line[1]
                relation['standoff_entity1_id'] = int(standoff_line[2].split(':')[1][1:])
                relation['standoff_entity2_id'] = int(standoff_line[3].split(':')[1][1:])
                relations.append(relation)
            # relations.append((standoff_id, relation_name, standoff_entity1_id, standoff_entity2_id))

    # read the .ann's matching .txt file and tokenize its text using stanford corenlp
    with open(join(DATA_DIRECTORY, file.replace('.ann', '.txt')), 'r') as document_text_file:
        document_text = document_text_file.read()

    output = nlp.annotate(document_text, properties={
        'annotators': 'tokenize,ssplit,pos',
        'outputFormat': 'json'
    })

    # write text and annotations into NER and RE output files
    with open(NER_TRAINING_DATA_OUTPUT_PATH, 'a') as ner_training_data, open(RE_TRAINING_DATA_OUTPUT_PATH, 'a') as re_training_data:
        for sentence in output['sentences']:
            entities_in_sentence = {}
            sentence_re_rows = []

            for token in sentence['tokens']:
                offset_start = int(token['characterOffsetBegin'])
                offset_end = int(token['characterOffsetEnd'])

                re_row = {}
                entity_found = False
                ner_anno = DEFAULT_OTHER_ANNO

                # searching for token in annotated entities
                for entity in entities:
                    if offset_start >= entity['offset_start'] and offset_end <= entity['offset_end']:
                        ner_anno = entity['entity_type']

                    # multi-token entities for RE need to be handled differently than NER
                    if offset_start == entity['offset_start'] and offset_end <= entity['offset_end']:
                        entities_in_sentence[entity['standoff_id']] = len(sentence_re_rows)
                        re_row['entity_type'] = entity['entity_type']
                        re_row['pos_tag'] = token['pos']
                        re_row['word'] = token['word']

                        sentence_re_rows.append(re_row)
                        entity_found = True
                        break
                    elif offset_start > entity['offset_start'] and offset_end <= entity['offset_end'] and len(sentence_re_rows) > 0:
                        sentence_re_rows[-1]['pos_tag'] += '/{}'.format(token['pos'])
                        sentence_re_rows[-1]['word'] += '/{}'.format(token['word'])
                        entity_found = True
                        break

                if not entity_found:
                    re_row['entity_type'] = DEFAULT_OTHER_ANNO
                    re_row['pos_tag'] = token['pos']
                    re_row['word'] = token['word']

                    sentence_re_rows.append(re_row)

                # writing tagged tokens to NER training data
                ner_training_data.write('{}\t{}\n'.format(token['word'], ner_anno))

            # writing tagged tokens to RE training data
            token_count = 0
            for sentence_row in sentence_re_rows:
                re_training_data.write('{}\t{}\t{}\tO\t{}\t{}\tO\tO\tO\n'.format(str(sentence_count), sentence_row['entity_type'], str(token_count), sentence_row['pos_tag'], sentence_row['word']))
                token_count += 1

            re_training_data.write('\n')

            # writing relations to RE training data
            for relation in relations:
                if relation['standoff_entity1_id'] in entities_in_sentence and relation['standoff_entity2_id'] in entities_in_sentence:
                    entity1 = str(entities_in_sentence[relation['standoff_entity1_id']])
                    entity2 = str(entities_in_sentence[relation['standoff_entity2_id']])
                    relation_name = relation['name']
                    re_training_data.write('{}\t{}\t{}\n'.format(entity1, entity2, relation_name))

            re_training_data.write('\n')

            sentence_count += 1

        ner_training_data.write('\n')

    print('Processed file pair: {} and {}'.format(file, file.replace('.ann', '.txt')))

The package 'pycorenlp' is not available for windows Os?

Hello, I am trying your script but I am facing issues with the corenlp server. I downloaded the folder from https://stanfordnlp.github.io/CoreNLP/download.html, started the server in my command prompt, imported, and installed the corenlp package in google colab, however it fails with 'Exception: Check whether you have started the CoreNLP server'. So I logged onto the localhost:9000 server and tried getting the output on the server for a random single sentence. The server doesn't respond and my command prompt shows the error of ' java.lang.OutOfMemoryError: Java heap space'. While starting the server I used the command 'java -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -preload tokenize,ssplit,pos,lemma,parse \ -port 9000 -timeout 75000'.

I am aware this is not directly related to NLP, however, I would like to try your code for my project and would like any help that you can offer for this. Thank you.

Hey Gurikat, try adding a question on StackOverflow with tags like stanford-nlp, core-nlp, etc.