Python load_attr_map Beispiele

Beispiel #1

import build_dict
import _pickle as cPickle
import re
import jieba
import json
import requests

attr_map = build_dict.load_attr_map("attr_mapping.txt")  # 属性名统一为一个
attr_ac = cPickle.load(open("attr_ac.pkl", "rb"))  # 所有的属性名的自动机
ent_dict = build_dict.load_entity_dict("all_entity.txt")  # 所有实体名
val_dict = build_dict.load_val_dict("Person_val.txt")  # 属性值对应的属性名


def _remove_dup(word_list):
    '''
    args:
        word_list: 一个字符串的list
    '''
    distinct_word_list = []
    for i in range(len(word_list)):
        is_dup = False
        for j in range(len(word_list)):
            if j != i and word_list[i] in word_list[j]:
                is_dup = True
                break
        if not is_dup:
            distinct_word_list.append(word_list[i])
    return distinct_word_list


def _map_predicate(pred_name, map_attr=True):  #找出一个字符串中是否包含知识库中的属性名

Beispiel #2

import requests
from Queue import Queue
import urllib
try:
    import simplejson as json
except:
    import json
from collections import defaultdict
import jieba
import jieba.posseg as psg
import re
import cPickle
import build_dict

attr_map = build_dict.load_attr_map("./search/data/attr_map.txt")
attr_ac = cPickle.load(open("./search/data/attr_ac.pkl", "rb"))
ent_dict = build_dict.load_entity_dict("./search/data/data_entity.txt")


def home(request):
    return render(request, "home.html", {})


def search(request):
    question = request.GET['question']
    lf_question = translate_NL2LF(question)
    answer, msg, query_type = _parse_query(lf_question)
    # answer, msg, query_type = _parse_query(question)
    if msg == 'done':
        if query_type == 1:

Beispiel #3

Datei: views.py Projekt: TVect/es-qa-demo

import requests
from Queue import Queue
import urllib
try:
    import simplejson as json
except:
    import json
from collections import defaultdict
import jieba
import re
import cPickle

import build_dict

attr_map = build_dict.load_attr_map("/mnt/demo/search/data/attr_mapping.txt")
attr_ac = cPickle.load(open("/mnt/demo/search/data/attr_ac.pkl", "rb"))
ent_dict = build_dict.load_entity_dict("/mnt/demo/search/data/all_entity.txt")


def home(request):
    return render(request, "home.html", {})


def search(request):
    question = request.GET['question']
    answer, msg, query_type = _parse_query(question)
    if msg == 'done':
        if query_type == 1:
            return render(request, "entity.html", {
                "question": question,

Beispiel #4

Datei: views.py Projekt: cixl/PersonKBQA

except:
    import json
from collections import defaultdict
import jieba
import re
import cPickle

import build_dict

#
# attr_map = build_dict.load_attr_map("/mnt/demo/search/data/attr_mapping.txt")
# attr_ac = cPickle.load(open("/mnt/demo/search/data/attr_ac.pkl","rb"))
# ent_dict = build_dict.load_entity_dict("/mnt/demo/search/data/all_entity.txt")
# val_dict = build_dict.load_val_dict("/mnt/demo/search/data/Person_val.txt")

attr_map = build_dict.load_attr_map("./data/attr_mapping.txt")
attr_ac = cPickle.load(open("./data/attr_ac.pkl", "rb"))
ent_dict = build_dict.load_entity_dict("./data/all_entity.txt")
val_dict = build_dict.load_val_dict("./data/Person_val.txt")


def home(request):
    return render(request, "home.html", {})


def search(request):
    question = request.GET['question']
    val_d = _val_linking(question)
    lf_question = translate_NL2LF(question)
    answer, msg, query_type = _parse_query(lf_question)
    # answer, msg, query_type = _parse_query(question)

Beispiel #5

Datei: views.py Projekt: zgd716/chatbot

import requests
from Queue import Queue
import urllib
try:
    import simplejson as json
except:
    import json
from collections import defaultdict
import jieba
import re
import cPickle

import build_dict

attr_map = build_dict.load_attr_map("E:/projects/opensource/chatbot/KBQA/ES/search/data/attr_mapping.txt")
attr_ac = cPickle.load(open("E:/projects/opensource/chatbot/KBQA/ES/search/data/attr_ac.pkl","rb"))
ent_dict = build_dict.load_entity_dict("E:/projects/opensource/chatbot/KBQA/ES/search/data/all_entity.txt")
val_dict = build_dict.load_val_dict("E:/projects/opensource/chatbot/KBQA/ES/search/data/Person_val.txt")

def home(request):
    return render(request, "home.html", {})

def search(request):
    question = request.GET['question']
    val_d = _val_linking(question)
    lf_question = translate_NL2LF(question)
    answer, msg, query_type = _parse_query(lf_question)
    # answer, msg, query_type = _parse_query(question)
    if msg == 'done':
        if query_type == 1: