def __init__(self):
os.environ["JAVAHOME"] = "C:\Program Files\Java\jdk1.8.0_151\\bin"
jar_path = "D:\Stanford Core NLP\stanford-corenlp-full-2018-10-05\stanford-corenlp-3.9.2.jar"
model_path = "D:\Stanford Core NLP\stanford-corenlp-full-2018-10-05\stanford-corenlp-3.9.2-models.jar"
self.parser = stanford.StanfordDependencyParser(
path_to_jar=jar_path, path_to_models_jar=model_path)
print("Standford Dependency Parser instantiated")
def __init__(self, host='http://localhost', port=9000):
self.nlp = StanfordCoreNLP(
host, port=port,
timeout=30000) # , quiet=False, logging_level=logging.DEBUG)
# self.nlp = StanfordCoreNLP(r'/Users/jovi/Desktop/CMU/NLP/project/stanford-corenlp-full-2018-02-27')
self.nlp_depParser = stanford.StanfordDependencyParser()
self.nlp_parser = stanford.StanfordParser()
self.props = {
'annotators': 'coref',
'pipelineLanguage': 'en' # ,
# 'outputFormat': 'json'
}
self.url = "http://nlp02.lti.cs.cmu.edu:9000"
def __init__(self):
root_folder = os.path.dirname(os.path.realpath(__file__))
with open(os.path.join(root_folder, 'environ.yaml'), 'r') as f:
env = yaml.load(f)
stanford_parser_folder = env['stanford_parser_folder']
os.environ['STANFORD_PARSER'] = stanford_parser_folder
os.environ['STANFORD_MODELS'] = stanford_parser_folder
with open(os.path.join(root_folder, 'ioput_maxent_classifier.pickle'),
'r') as f:
self._maxent_classifier = cPickle.loads(f.read())
self._tree_parser = stanford.StanfordParser(
model_path=env['model_path'])
self._dependency_parser = stanford.StanfordDependencyParser(
model_path=env['model_path'])
self._NPs = []
def parse(sentence):
os.environ["JAVAHOME"] = "C:\Program Files\Java\jdk1.8.0_151\\bin"
jar_path = "D:\Stanford Core NLP\stanford-corenlp-full-2018-10-05\stanford-corenlp-3.9.2.jar"
model_path = "D:\Stanford Core NLP\stanford-corenlp-full-2018-10-05\stanford-corenlp-3.9.2-models.jar"
parser = stanford.StanfordDependencyParser(path_to_jar=jar_path,
path_to_models_jar=model_path)
sentences = parser.raw_parse(sentence)
dep = sentences.__next__()
parsed = list(dep.triples())
# pprint(parsed)
# export(parsed, "test_result.pickle")
return parsed
def stanford_parse(sentence):
parser = stanford.StanfordDependencyParser(
model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz")
warnings.filterwarnings('error')
sub_obj_list = []
try:
parse = parser.raw_parse(sentence)
for sent in parse:
#print(type(sent))
#len(sent)
for node in sent.triples():
if ('nsubj' in node or 'dobj' in node or 'nsubjpass' in node):
# print node[2]
# if('NNP' in node[2] or 'NNPS' in node[2] or 'NN' in node[2] or 'NNS' in node[2]):
if (('NNP' or 'NNPS' or 'NN' or 'NNS') in node[2]):
sub_obj_list.append(node[2])
except Warning:
print 'Warning was raised as an exception!'
return sub_obj_list
def get_dep_parser(cls):
if cls._dep_parser is None:
cls._dep_parser = stanford.StanfordDependencyParser(
model_path=cls.PATH_TO_STANFORD + "englishPCFG.ser.gz")
return cls._dep_parser
import os
from nltk.parse import stanford
from collections import Counter
import spacy
os.environ['STANFORD_PARSER'] = '../stanford-parser-full-2018-10-17/stanford-parser.jar'
os.environ['STANFORD_MODELS'] = '../stanford-parser-full-2018-10-17/stanford-parser-3.9.2-models.jar'
pcfg_parser = stanford.StanfordParser(model_path='../stanford-parser-full-2018-10-17/englishPCFG.ser.gz')
dep_parser = stanford.StanfordDependencyParser(model_path='../stanford-parser-full-2018-10-17/englishPCFG.ser.gz')
nlp = spacy.load('en_core_web_lg')
def stanford_parser(sentence):
pcfg = pcfg_parser.raw_parse(sentence).__next__()
dep = dep_parser.raw_parse(sentence).__next__()
dep_list = list(dep.triples())
return dep_list, pcfg
def stanford_pcfg(sentence):
pcfg = pcfg_parser.raw_parse(sentence).__next__()
return pcfg
def stanford_dep(sentence):
dep = dep_parser.raw_parse(sentence).__next__()
dep_list = list(dep.triples())
return dep_list
#input should be a sentence
def Spacy_parser(sentence):
#from tabulate import tabulate
if __name__ == '__main__':
if len(sys.argv) >= 3:
input_file = sys.argv[1]
output_file = sys.argv[2]
else:
input_file = 'data/atomic/train_intent_react.txt'
output_file = 'data/atomic/train_svo.txt'
# os.environ['STANFORD_PARSER'] = '/usr/local/Cellar/stanford-parser/3.9.1/libexec/stanford-parser.jar'
# os.environ['STANFORD_MODELS'] = '/usr/local/Cellar/stanford-parser/3.9.1/libexec/stanford-parser-3.9.1-models.jar'
os.environ[
'STANFORD_PARSER'] = '/users4/kliao/data/stanford_nlp/stanford-parser.jar'
os.environ[
'STANFORD_MODELS'] = '/users4/kliao/data/stanford_nlp/stanford-parser-3.9.1-models.jar'
parser = stanford.StanfordDependencyParser(
model_path='edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz')
lines = open(input_file, 'r').readlines()
lines = [line.strip().split(' | ') for line in lines]
output_file = open(output_file, 'w')
for line in lines:
event, intent, react = line
verb = None
subj = None
obj = None
graph = next(parser.raw_parse(event))
children_dict = {}
for i in graph.nodes:
def __init__(self):
self.parser = stanford.StanfordDependencyParser(
model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz")
from nltk.parse.stanford import StanfordDependencyParser
from nltk.parse import stanford
import nltk
import os
from dataset import del_mac_DS
from helper import *
from nltk.tokenize import casual_tokenize
import time
#添加stanford环境变量,此处需要手动修改，jar包地址为绝对地址。
dir_path = './stanford_parser_jar'
os.environ['STANFORD_PARSER'] = os.path.join(dir_path, 'stanford-parser.jar')
os.environ['STANFORD_MODELS'] = os.path.join(
dir_path, 'stanford-parser-3.5.2-models.jar')
parser = stanford.StanfordDependencyParser(
model_path=os.path.join(dir_path, 'englishPCFG.ser.gz'))
def get_the_parse(sent_list_in):
tag_dict = {}
sentences = parser.parse_sents([sent_list_in])
tree = [parse.tree() for parse in list(sentences)[0]]
n_leaves = len(tree[0].leaves())
leavepos = list(tree[0].leaf_treeposition(n) for n in range(n_leaves))
for pos in tree[0].treepositions():
c_word = None
if pos not in leavepos:
c_word = tree[0][pos].label()
else:
c_word = tree[0][pos]
import numpy as np
import re, os
import networkx as nx
from sklearn.svm import LinearSVC
from sklearn.metrics import precision_recall_fscore_support, accuracy_score
from nltk.parse import stanford
FILE_ENCODING = "utf8"
TRAINING_SIZE = 3000
os.environ['JAVAHOME'] = "C:/Program Files/Java/jdk1.8.0_40/bin"
os.environ['STANFORD_PARSER'] = '/jars'
os.environ['STANFORD_MODELS'] = '/jars'
parser = stanford.StanfordParser(model_path="/jars/englishPCFG.ser.gz")
dep_parser = stanford.StanfordDependencyParser(model_path="/jars/englishPCFG.ser.gz")
#===========================================================================
# parsed = parser.raw_parse("Example sentence.")
# dep_parsed = dep_parser.raw_parse("Example sentence.")
#===========================================================================
# Classifier
clf = LinearSVC()
sentences = []
labels = []
def chunks(arr, n):
for i in range(0, len(arr), n):
yield arr[i:i+n]
import os
from nltk.parse import stanford
from nltk import word_tokenize
import nltk.data
from nltk.tag.stanford import StanfordNERTagger
#java_path = "C:\\Program Files\\Java\\jdk1.8.0_131\\bin\\java.exe"
#java_path = "C:\\Program Files\\Java\\jdk1.8.0_151\\bin\\java.exe"
java_path = "java.exe"
os.environ['JAVAHOME'] = java_path
os.environ['STANFORD_PARSER'] = 'jars\\stanford-parser.jar'
os.environ['STANFORD_MODELS'] = 'jars\\stanford-parser-3.9.2-models.jar'
parser = stanford.StanfordParser(model_path="englishPCFG.ser.gz")
dep_parser = stanford.StanfordDependencyParser(
'jars\\stanford-parser.jar', 'jars\\stanford-parser-3.9.2-models.jar')
tokenizer = nltk.data.load('tokenizers\\punkt\\english.pickle')
ner3 = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz',
'jars\\stanford-ner.jar')
ner7 = StanfordNERTagger('english.muc.7class.distsim.crf.ser.gz',
'jars\\stanford-ner.jar')
def sentence_parse(sentence):
return parser.raw_parse(sentence)
def get_root_sub_obj(sentence):
final_dependency = []
result = dep_parser.raw_parse(sentence)
parsetree = list(result)[0]
# Hyper Parameters
DEP_VEC_LEN = 50
TRIGGER_CANDIDATE_LIST = [
'NN', 'NNS', 'NNP', 'NNPS', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ'
]
# 添加stanford环境变量,此处需要手动修改，jar包地址为绝对地址。
os.environ['STANFORD_PARSER'] = '/home/sfdai/jars/stanford-parser.jar'
os.environ[
'STANFORD_MODELS'] = '/home/sfdai/jars/stanford-parser-3.8.0-models.jar'
# 为JAVAHOME添加环境变量
java_path = "/usr/lib/jvm/java-8-oracle/jre/bin/java"
os.environ['JAVAHOME'] = java_path
dependency_parser = stanford.StanfordDependencyParser(
model_path=
"/home/sfdai/stanford-parser-full-2017-06-09/stanford-parser-3.8.0-models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz"
)
word_not_vec_dic = {
"'s": "0",
"n't": "not",
"'re": "are",
"'ve": "have",
"'ll": "will",
"'m": "am",
"'d": "1"
}
def process_sentence(raw_sentence):
sentences = dependency_parser.raw_parse(raw_sentence)
