ragflow/deepdoc/parser/resume/entities/corporations.py

import re,json,os
import pandas as pd
from rag.nlp import rag_tokenizer
from . import regions
current_file_path = os.path.dirname(os.path.abspath(__file__))
GOODS = pd.read_csv(os.path.join(current_file_path, "res/corp_baike_len.csv"), sep="\t", header=0).fillna(0)
GOODS["cid"] = GOODS["cid"].astype(str)
GOODS = GOODS.set_index(["cid"])
CORP_TKS = json.load(open(os.path.join(current_file_path, "res/corp.tks.freq.json"), "r"))
GOOD_CORP = json.load(open(os.path.join(current_file_path, "res/good_corp.json"), "r"))
CORP_TAG = json.load(open(os.path.join(current_file_path, "res/corp_tag.json"), "r"))

def baike(cid, default_v=0):
    global GOODS
    try:
        return GOODS.loc[str(cid), "len"]
    except Exception as e:
        pass
    return default_v


def corpNorm(nm, add_region=True):
    global CORP_TKS
    if not nm or type(nm)!=type(""):return ""
    nm = rag_tokenizer.tradi2simp(rag_tokenizer.strQ2B(nm)).lower()
    nm = re.sub(r"&", "&", nm)
    nm = re.sub(r"[\(\)（）\+'\"\t \*\\【】-]+", " ", nm)
    nm = re.sub(r"([—-]+.*| +co\..*|corp\..*| +inc\..*| +ltd.*)", "", nm, 10000, re.IGNORECASE)
    nm = re.sub(r"(计算机|技术|(技术|科技|网络)*有限公司|公司|有限|研发中心|中国|总部)$", "", nm, 10000, re.IGNORECASE)
    if not nm or (len(nm)<5 and not regions.isName(nm[0:2])):return nm

    tks = rag_tokenizer.tokenize(nm).split(" ")
    reg = [t for i,t in enumerate(tks) if regions.isName(t) and (t != "中国" or i > 0)]
    nm = ""
    for t in tks:
        if regions.isName(t) or t in CORP_TKS:continue
        if re.match(r"[0-9a-zA-Z\\,.]+", t) and re.match(r".*[0-9a-zA-Z\,.]+$", nm):nm += " "
        nm += t

    r = re.search(r"^([^a-z0-9 \(\)&]{2,})[a-z ]{4,}$", nm.strip())
    if r:nm = r.group(1)
    r = re.search(r"^([a-z ]{3,})[^a-z0-9 \(\)&]{2,}$", nm.strip())
    if r:nm = r.group(1)
    return nm.strip() + (("" if not reg else "(%s)"%reg[0]) if add_region else "")


def rmNoise(n):
    n = re.sub(r"[\(（][^()（）]+[)）]", "", n)
    n = re.sub(r"[,. &（）()]+", "", n)
    return n

GOOD_CORP = set([corpNorm(rmNoise(c), False) for c in GOOD_CORP])
for c,v in CORP_TAG.items():
    cc = corpNorm(rmNoise(c), False)
    if not cc: print (c)
CORP_TAG = {corpNorm(rmNoise(c), False):v for c,v in CORP_TAG.items()}

def is_good(nm):
    global GOOD_CORP
    if nm.find("外派")>=0:return False
    nm = rmNoise(nm)
    nm = corpNorm(nm, False)
    for n in GOOD_CORP:
        if re.match(r"[0-9a-zA-Z]+$", n):
            if n == nm: return True
        elif nm.find(n)>=0:return True
    return False

def corp_tag(nm):
    global CORP_TAG
    nm = rmNoise(nm)
    nm = corpNorm(nm, False)
    for n in CORP_TAG.keys():
        if re.match(r"[0-9a-zA-Z., ]+$", n):
            if n == nm: return CORP_TAG[n]
        elif nm.find(n)>=0:
            if len(n)<3 and len(nm)/len(n)>=2:continue
            return CORP_TAG[n]
    return []