def extractSummary(document, size, sentence_separator=None):
"""
* 自动摘要
*
* @param document 目标文档
* @param size 需要的关键句的个数
* @param sentence_separator 分割目标文档时的句子分割符，正则格式， 如：[。？?！!；;]
* @return 关键句列表
"""
if sentence_separator:
return HanLP.extractSummary(document, size, sentence_separator)
else:
return HanLP.extractSummary(document, size)
def split_test(self, sentence):
#line = sentence.strip().decode('utf-8', 'ignore') # 去除每行首尾可能出现的空格，并转为Unicode进行处理
#line1 = re.sub("[0-9\s+\.\!\/_,$%^*()?;；:-【】+\"\']+|[+——！，;:。？、~@#￥%……&*（）]+".decode("utf8"),
# " ".decode("utf8"), line)
#wordList = list(jieba.cut(line1)) # 用结巴分词，对每行内容进行分词
print(HanLP.segment('你好，欢迎在Python中调用HanLP的API'))
for term in HanLP.segment('下雨天地面积水'):
print('{}\t{}'.format(term.word, term.nature)) # 获取单词与词性
testCases = [
"商品和服务", "结婚的和尚未结婚的确实在干扰分词啊", "买水果然后来世博园最后去世博会", "中国的首都是北京",
"欢迎新老师生前来就餐", "工信处女干事每月经过下属科室都要亲口交代24口交换机等技术性器件的安装工作",
"随着页游兴起到现在的页游繁盛，依赖于存档进行逻辑判断的设计减少了，但这块也不能完全忽略掉。"
]
for sentence in testCases:
print(HanLP.segment(sentence))
# 关键词提取
document = "水利部水资源司司长陈明忠9月29日在国务院新闻办举行的新闻发布会上透露，" \
"根据刚刚完成了水资源管理制度的考核，有部分省接近了红线的指标，" \
"有部分省超过红线的指标。对一些超过红线的地方，陈明忠表示，对一些取用水项目进行区域的限批，" \
"严格地进行水资源论证和取水许可的批准。"
print(HanLP.extractKeyword(document, 2))
# 自动摘要
print(HanLP.extractSummary(document, 3))
# 依存句法分析
print(HanLP.parseDependency("徐先生还具体帮助他确定了把画雄鹰、松鼠和麻雀作为主攻目标。"))
from pyhanlp import *
print(HanLP.segment('你好，欢迎在Python中调用HanLP的API'))
for term in HanLP.segment('下雨天地面积水'):
print('{}\t{}'.format(term.word, term.nature)) # 获取单词与词性
testCases = [
"商品和服务", "结婚的和尚未结婚的确实在干扰分词啊", "买水果然后来世博园最后去世博会", "中国的首都是北京", "欢迎新老师生前来就餐",
"工信处女干事每月经过下属科室都要亲口交代24口交换机等技术性器件的安装工作",
"随着页游兴起到现在的页游繁盛，依赖于存档进行逻辑判断的设计减少了，但这块也不能完全忽略掉。"
]
for sentence in testCases:
print(HanLP.segment(sentence))
print("# 关键词提取")
document = "水利部水资源司司长陈明忠9月29日在国务院新闻办举行的新闻发布会上透露，" \
"根据刚刚完成了水资源管理制度的考核，有部分省接近了红线的指标，" \
"有部分省超过红线的指标。对一些超过红线的地方，陈明忠表示，对一些取用水项目进行区域的限批，" \
"严格地进行水资源论证和取水许可的批准。"
print(HanLP.extractKeyword(document, 2))
print("# 自动摘要")
print(HanLP.extractSummary(document, 3))
print("# 依存句法分析")
print(HanLP.parseDependency("徐先生还具体帮助他确定了把画雄鹰、松鼠和麻雀作为主攻目标。"))
doc = "句法分析是自然语言处理中的关键技术之一，其基本任务是确定句子的句法结构或者句子中词汇之间的依存关系。\
主要包括两方面的内容，一是确定语言的语法体系，即对语言中合法的句子的语法结构给与形式化的定义；另一方面是句法分析技术，即根据给定的语法体系，自动推导出句子的句法结构，分析句子所包含的句法单位和这些句法单位之间的关系。"
print("关键词")
print(HanLP.extractKeyword(doc, 2))
print("# 自动摘要")
print(HanLP.extractSummary(doc, 3))
def main():
if len(sys.argv) == 1:
sys.argv.append('--help')
arg_parser = argparse.ArgumentParser(
description='HanLP: Han Language Processing v{}'.format(
HANLP_JAR_VERSION))
arg_parser.add_argument('-v',
'--version',
required=False,
action='store_true',
help='show installed versions of HanLP')
task_parser = arg_parser.add_subparsers(dest="task",
help='which task to perform?')
segment_parser = task_parser.add_parser(name='segment',
help='word segmentation')
tag_parser = segment_parser.add_mutually_exclusive_group(required=False)
tag_parser.add_argument('--tag',
dest='tag',
action='store_true',
help='show part-of-speech tags')
tag_parser.add_argument('--no-tag',
dest='tag',
action='store_false',
help='don\'t show part-of-speech tags')
segment_parser.set_defaults(tag=True)
segment_parser.add_argument(
'-a',
'--algorithm',
type=str,
default='viterbi',
help='algorithm of segmentation e.g. perceptron')
parse_parser = task_parser.add_parser(name='parse',
help='dependency parsing')
parse_keyword = task_parser.add_parser(name='keyword',
help='dependency Keyword')
parse_summary = task_parser.add_parser(name='summary',
help='dependency summary')
server_parser = task_parser.add_parser(
name='serve',
help='start http server',
description='A http server for HanLP')
server_parser.add_argument('--port', type=int, default=8765)
update_parser = task_parser.add_parser(name='update',
help='update jar and data of HanLP')
def add_args(p):
p.add_argument("--config",
default=PATH_CONFIG,
help='path to hanlp.properties')
# p.add_argument("--action", dest="action", default='predict',
# help='Which action (train, test, predict)?')
add_args(segment_parser)
add_args(parse_parser)
add_args(parse_keyword)
add_args(parse_summary)
if '-v' in sys.argv or '--version' in sys.argv:
print('jar {}: {}'.format(HANLP_JAR_VERSION, HANLP_JAR_PATH))
data_version = hanlp_installed_data_version()
print('data {}: {}'.format(data_version if data_version else '自定义',
HANLP_DATA_PATH))
print('config : {}'.format(
os.path.join(STATIC_ROOT, 'hanlp.properties')))
exit(0)
args = arg_parser.parse_args()
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
def die(msg):
eprint(msg)
exit(1)
if hasattr(args, 'config') and args.config:
if os.path.isfile(args.config):
JClass('com.hankcs.hanlp.utility.Predefine'
).HANLP_PROPERTIES_PATH = args.config
else:
die('Can\'t find config file {}'.format(args.config))
if args.task == 'segment':
segmenter = None
try:
segmenter = HanLP.newSegment(args.algorithm)
except JException as e:
if isinstance(e, java.lang.IllegalArgumentException):
die('invalid algorithm {}'.format(args.algorithm))
elif isinstance(e, java.lang.RuntimeException):
die('failed to load required model')
else:
die('unknown exception {}'.format(repr(e)))
is_lexical_analyzer = hasattr(segmenter, 'analyze')
if not args.tag:
if is_lexical_analyzer:
segmenter.enablePartOfSpeechTagging(False)
JClass('com.hankcs.hanlp.HanLP$Config').ShowTermNature = False
else:
JClass('com.hankcs.hanlp.HanLP$Config').ShowTermNature = False
for line in sys.stdin:
line = line.strip()
print(' '.join(term.toString()
for term in segmenter.seg(any2utf8(line))))
elif args.task == 'parse':
for line in sys.stdin:
line = line.strip()
print(HanLP.parseDependency(any2utf8(line)))
elif args.task == 'keyword':
for line in sys.stdin:
line = line.strip()
TextRankKeyword = JClass(
"com.hankcs.hanlp.summary.TextRankKeyword")
keyword_list = HanLP.extractKeyword(line, 3)
print(keyword_list)
#print(HanLP.parseDependency(any2utf8(line)))
elif args.task == 'summary':
for line in sys.stdin:
line = line.strip()
TextRankSentence = JClass(
"com.hankcs.hanlp.summary.TextRankSentence")
sentence_list = HanLP.extractSummary(line, 3)
print(sentence_list)
elif args.task == 'serve':
if PY == 3:
from pyhanlp import server
server.run(port=args.port)
else:
die('现在server.py暂时不支持Python2，欢迎参与移植')
elif args.task == 'update':
if hanlp_installed_data_version() == '手动安装':
die('手动配置不支持自动升级，若要恢复自动安装，请清除HANLP相关环境变量')
else:
from pyhanlp.static import update_hanlp
update_hanlp()
def extract_summa(s):
summa = HanLP.extractSummary(s, 15, r'[ ]')
return [x for x in summa if 10< len(x) <40]
return [s for s in summaries if 'c' not in extract_s_nature(s) and 'cc' not in extract_s_nature(s)]
sql = "SELECT distinct game_id FROM game_source.s_game_comments_taptap_game WHERE source='taptap'"
game_id_list = from_sql(sql)
game_s = pd.DataFrame()
from datetime import datetime
for game_id in tqdm(list(game_id_list['game_id'])):
sql = """
SELECT source, game_id, game_name, content FROM game_source.s_game_comments_taptap_game where game_id = %s and length(content)>300
""" % (game_id, )
df = from_sql(sql)
df['content'] = df['content'].apply(clear_text)
df['content'] = df['content'].apply(get_help_content)
df['summaries']=df['content'].apply(lambda x: list(HanLP.extractSummary(x, 1, r'[ ]')))
df_new = df[['source', 'game_id', 'game_name', 'summaries']].groupby(['source', 'game_id','game_name']).agg(join).reset_index()
#做了两次的提取摘要，
try:
df_new['summaries'] = df_new['summaries'].apply(extract_summa)
df_new['summaries'] = df_new['summaries'].apply(clear_summa)
except:
print('summar is []')
continue
if df_new.iloc[0]['summaries'] == []:
continue
trg_db='game_process'
trg_table='c_lcs_game_comment_summary'
