master-thesis/utils/corenlp.py

import stanza
import os
from pathlib import Path
from stanza.server import CoreNLPClient

STOPWORDS_FILE = "../data/resource/stopwords.txt"
CORENLP_DIR = str(Path().absolute()) + '/corenlp-dir'
# properties for the CoreNLP Server
ANNOTATORS = ['tokenize', 'ssplit', 'pos', 'lemma', 'ner', 'regexner']

# install/download the latest version of CoreNLP
stanza.install_corenlp(dir=CORENLP_DIR)

extra_model_jar = os.path.join(CORENLP_DIR, 'stanford-corenlp-4.5.1-models-english-extra.jar')
if not os.path.isfile(extra_model_jar):
    # download corenlp english models
    stanza.download_corenlp_models(model='english-extra', version='4.5.1', dir=CORENLP_DIR)
else:
    print('English Extra CoreNLP models available!')

# set environment var of installation location
os.environ["CORENLP_HOME"] = CORENLP_DIR

VALUES_CONVERT = {
    'zero': '0',
    'one': '1',
    'two': '2',
    'three': '3',
    'four': '4',
    'five': '5',
    'six': '6',
    'seven': '7',
    'eight': '8',
    'nine': '9',
    'ten': '10',
    'not important': 'dont care',
    'not expensive': 'cheap'
}

# corenlp properties
properties = {
    'ner.applyFineGrained': False,
    'pos.model': 'edu/stanford/nlp/models/pos-tagger/english-caseless-left3words-distsim.tagger',
    'ner.model': 'edu/stanford/nlp/models/ner/english.all.3class.caseless.distsim.crf.ser.gz,'
                 'edu/stanford/nlp/models/ner/english.muc.7class.caseless.distsim.crf.ser.gz,'
                 'edu/stanford/nlp/models/ner/english.conll.4class.caseless.distsim.crf.ser.gz',
    'ner.additional.regexner.mapping': str(Path().absolute()) + '/regexner.rules'
}

DATE_TIME_NUM = {'TIME', 'DATE', 'NUMBER'}


class ValueExtractor:

    def __init__(self):
        # create the CoreNLP client
        self.client = CoreNLPClient(
            annotators=ANNOTATORS,
            properties=properties,
            timeout=300000,
            memory='8G',
            threads=8,
            be_quiet=True
        )

        # load the stopwords.txt file
        self.stopwords = set()
        with open(STOPWORDS_FILE, 'r') as file:
            for line in file:
                self.stopwords.add(line.strip())

    def start(self):
        self.client.start()

    def stop(self):
        self.client.stop()

    def extract_value_candidates(self, history):

        # format history and prepare text for annotation
        user_utterance = ''
        for utterance in history:
            # consider only user utterances
            if utterance.startswith('user :'):
                utterance = utterance[len('user :'):]
                # if sentence doesn't end with punctuation, add .
                if not utterance.endswith(('.', '?', '!')):
                    utterance = utterance + '.'
                # append all utterances
                user_utterance = user_utterance + utterance + ' '

        if not user_utterance:
            return []

        value_candidates = []
        user_utterance = user_utterance.replace("'", "")
        # use corenlp client and annotate text
        annotation = self.client.annotate(user_utterance)
        for sent in annotation.sentence:
            prev_word = ''
            for token in sent.token:
                # TODO :: remove
                # print("{:12s}\t{:12s}\t{:6s}\t{}".format(token.word, token.lemma, token.pos, token.ner))
                word = token.word.strip()
                # extract Adjectives & Adverbs using POS tags
                # exclude the custom entity types
                if token.pos in ['JJ', 'RB'] \
                        and token.ner not in ['AREA', 'PLACE', 'PLACE_TYPE', 'FOOD_TYPE']:
                    # check if the word ends with 'ly'
                    word = word.removesuffix('ly')
                    if word not in self.stopwords and word not in value_candidates:
                        if prev_word == 'not' and prev_word + ' ' + word in VALUES_CONVERT:
                            word = VALUES_CONVERT[prev_word + ' ' + word]
                        value_candidates.append(word)

                prev_word = word

                # extract day, time & numbers
                if token.ner in DATE_TIME_NUM:
                    if word in VALUES_CONVERT:
                        word = VALUES_CONVERT[word]
                    if token.ner == "DATE" and token.pos not in ["NNP", "CD"]:
                        continue
                    if word not in self.stopwords:
                        if token.ner == "TIME":
                            if not word[0].isdigit():
                                continue
                            if len(word) == 4:
                                word = word.zfill(5)
                        if word in value_candidates:
                            continue
                        value_candidates.append(word)

        entity_map = {}
        # extract named entities (place, area,...)
        for sent in annotation.sentence:
            for mention in sent.mentions:
                # TODO :: remove
                # print("{:30s}\t{}".format(mention.entityMentionText, mention.entityType))
                entity_text = mention.entityMentionText.strip()
                if mention.entityType not in {'TIME', 'DATE', 'NUMBER', 'DURATION'} \
                        and entity_text not in value_candidates:
                    if mention.entityType in ['AREA', 'FOOD_TYPE', 'PLACE_TYPE']:
                        entity_map[mention.entityType] = entity_text
                    else:
                        value_candidates.append(entity_text)
        more_values = list(entity_map.values())

        return value_candidates + more_values


# sample test TODO:: remove
# dialog_history = [
#     "user : i need a taxi to go to mahal of cambridge",
#     "system : i can help with that . did you have a specific time in mind ?",
#     "user : yes, i'd like to leave after 2:30 please.",
#     "system : where would you like to depart from ?",
#     "user : i am departing from jesus green outdoor pool.",
#     "system : your taxi is booked . it is a [taxi_type] .",
#     "user : what is the contact number for the taxi?",
#     "system : the contact number is [taxi_phone] . can i help you with anything else today ?",
#     "user : thanks that's all for today ."
# ]
# extractor = ValueExtractor()
# extractor.start()
# values = extractor.extract_value_candidates(dialog_history)
# extractor.stop()
# print('Extracted Values: ', values)