def test_cnvk():
logging.info("=========================================")
logging.info("= cnvk =")
logging.info("=========================================")
test_cases = get_test_cases()
for tc in test_cases:
title = tc['title']
body = tc['body']
logging.info("ひらがな（全角） to カタカナ（全角） for %s" % title)
calc_time(cnvk.convert, body, cnvk.HIRA2KATA)
logging.debug("result: %s" % cnvk.convert(body, cnvk.HIRA2KATA))
logging.info("カタカナ（全角） to ひらがな（全角） for %s" % title)
calc_time(cnvk.convert, body, cnvk.Z_KATA, cnvk.KATA2HIRA)
logging.debug("result: %s" % cnvk.convert(body, cnvk.Z_KATA, cnvk.KATA2HIRA))
logging.info("ひらがな（全角） to カタカナ（半角） for %s" % title)
calc_time(cnvk.convert, body, cnvk.HIRA2KATA, cnvk.H_KATA)
logging.debug("result: %s" % cnvk.convert(body, cnvk.KATA2HIRA, cnvk.H_KATA))
logging.info("半角 to 全角 for %s" % title)
calc_time(cnvk.convert, body, cnvk.Z_ASCII)
logging.debug("result: %s" % cnvk.convert(body, cnvk.Z_ASCII))
logging.info("全角 to 半角 for %s" % title)
calc_time(cnvk.convert, body, cnvk.H_ASCII)
logging.debug("result: %s" % cnvk.convert(body, cnvk.H_ASCII))
def sampling(http, cou, number):
list2 = []
html2 = requests.get(http)
soup2 = BeautifulSoup(html2.content, "html.parser")
[s.extract() for s in soup2('sup')]
#[s.replace_with('削除済') for s in soup2(text =re.compile('#'))]
title_and_trash = soup2.select('[class~=firstHeading]')
title = title_and_trash[0].get_text()
title = cnvk.convert(
title, cnvk.Z_ASCII, cnvk.Z_KATA, {u"⋯": u"…"}, {u"–": u"―"},
{u"—": u"―"}, {u"－": u"‐"}, {u"－": u"‐"}, {u"～": u"〜"}, {u"·": u"・"},
{u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"}, {u"‹": u"〈"}, {u"»": u"》"},
{u"«": u"《"}, {u"≥": u"≧"}, {u"≤": u"≦"}, {u"µ": u"μ"}, {u"〝": u"“"},
{u"〟": u"”"}, {u"⁄": u"／"}, {u"=": u"＝"})
title, cou2 = filt(title, cou)
starting_point = soup2.select('[class~=toclimit-3],[class~=toc]')
if len(starting_point) == 0:
return cou, False
fo = codecs.open('Random_Contents' + '.txt', 'a', 'utf-8')
fo2 = codecs.open('Random_Lists' + '.txt', 'a', 'utf-8')
print(title)
ti = '\n=' + title + '=\n'
line2 = ti.strip()
Len = len(line2)
number += Len
fo.write(ti)
fo2.write(ti)
fo.close()
fo2.close()
followers = starting_point[0].find_previous_siblings('p')
for k in followers:
follower = k.get_text()
list2.append(follower)
list2.reverse()
for line in list2:
line = cnvk.convert(line, cnvk.ZAC, cnvk.ZK, {u"⋯": u"…"},
{u"–": u"―"}, {u"—": u"―"}, {u"－": u"‐"},
{u"－": u"‐"}, {u"～": u"〜"}, {u"·": u"・"},
{u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"},
{u"‹": u"〈"}, {u"»": u"》"}, {u"«": u"《"},
{u"≥": u"≧"}, {u"≤": u"≦"}, {u"µ": u"μ"},
{u"〝": u"“"}, {u"〟": u"”"}, {u"⁄": u"／"},
{u"=": u"＝"})
line, cou2 = filt(line, cou2)
line2 = line.strip()
Len = len(line2)
number += Len
fo = codecs.open('Random_Contents' + '.txt', 'a', 'utf-8')
fo.write(line)
fo.close()
return cou2, html2
def TITLEs(url, cou):
html2 = requests.get(url)
soup2 = BeautifulSoup(html2.content, "html.parser")
[s.extract() for s in soup2('sup')]
title_and_trash = soup2.select('[class~=firstHeading]')
title = title_and_trash[0].get_text()
title = cnvk.convert(
title, cnvk.Z_ASCII, cnvk.Z_KATA, {u"⋯": u"…"}, {u"–": u"―"},
{u"—": u"―"}, {u"－": u"‐"}, {u"－": u"‐"}, {u"～": u"〜"}, {u"·": u"・"},
{u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"}, {u"‹": u"〈"}, {u"»": u"》"},
{u"«": u"《"}, {u"≥": u"≧"}, {u"≤": u"≦"}, {u"µ": u"μ"}, {u"〝": u"“"},
{u"〟": u"”"}, {u"⁄": u"／"}, {u"=": u"＝"})
title, _ = filt(title, cou)
return title
def tf_cnvk(text):
# 半角を全角に統一（khcoderに投入するため）
return cnvk.convert(text, cnvk.Z_NUM, cnvk.Z_ALPHA, cnvk.Z_KIGO, cnvk.Z_KATA) #KATA2HIRA
# coding: utf-8
'''
Created on 2014/02/20
@author: nahki
'''
import cnvk
if __name__ == '__main__':
ex_word = ''
of = open('kaomoji_zenkaku.csv', 'w', 1000)
with open('kaomoji_jisho.txt', 'r', 1000) as f:
for line in f:
word = cnvk.convert(line.upper(), cnvk.Z_NUM, cnvk.Z_ALPHA,
cnvk.Z_KIGO, cnvk.Z_KATA).strip()
if word == ex_word:
ex_word = word
print word
continue
cost = int(max(-32768, 6000 - 200 * len(word)**1.3))
res = u"%s,,,%d,名詞,一般,*,*,*,*,*,*,*,%s\n" % (word, cost,
u"kaomoji")
of.write(res)
ex_word = word
of.close()
def tf_cnvk(text):
# 半角を全角に統一（khcoderに投入するため）
return cnvk.convert(text, cnvk.Z_NUM, cnvk.Z_ALPHA, cnvk.Z_KIGO,
cnvk.Z_KATA) #KATA2HIRA
fin_name = 'jawiki-latest-all-titles-in-ns0'
fout_name = 'wikipedia.csv'
fin = codecs.open(fin_name, "r", "utf-8")
fout = codecs.open(fout_name, "w", "utf-8")
for line in fin:
word = line.rstrip()
if len(word) <= 3: continue
if re.compile(r'^[-.0-9]+$').search(word) is not None: continue
if re.compile(r'/[0-9]{4}.').search(word) is not None: continue
if re.compile(r'^\.').search(word) is not None: continue
if re.compile(r',').search(word) is not None: continue
print word
word = word.upper()
# word = word.replace(u'"', u'""')
# word = word.replace(u'〜', u'~')
"""
score = [-36000.0 ,-400 *(title.size**1.5)].max.to_i
をちょっと変更することで、良い結果が得られました。
naist-jdicの名詞の標準的なスコアはだいたい6000点ぐらいだったので、
そこから16bitの符号付整数の最小値である-32768に向けてもうちょっと分布が広がるように調整してみました。
score = [-32768.0, (6000 - 200 *(title.size**1.3))].max.to_i
この数式だと日本語でだいたい20文字(utf-8で)ぐらいまでの名詞が 分布されるようになります。
"""
cost = int(max(-32768, 6000 - 200*len(word)**1.3))
word = cnvk.convert(word.replace(u'_', u' '), cnvk.Z_NUM, cnvk.Z_ALPHA, cnvk.Z_KIGO, cnvk.Z_KATA).strip()
fout.write(u"\"%s\",,,%d,名詞,一般,*,*,*,*,*,*,*,%s\n" % (word, cost, u"Wikipedia"))
fin.close()
fout.close()
# coding: utf-8
'''
Created on 2014/02/20
@author: nahki
'''
import cnvk
if __name__=='__main__':
ex_word = ''
of = open('kaomoji_zenkaku.csv', 'w',1000)
with open('kaomoji_jisho.txt', 'r', 1000) as f:
for line in f:
word = cnvk.convert(line.upper(), cnvk.Z_NUM, cnvk.Z_ALPHA, cnvk.Z_KIGO, cnvk.Z_KATA).strip()
if word == ex_word:
ex_word = word
print word
continue
cost = int(max(-32768, 6000 - 200*len(word)**1.3))
res = u"%s,,,%d,名詞,一般,*,*,*,*,*,*,*,%s\n" % (word, cost, u"kaomoji")
of.write(res)
ex_word = word
of.close()
def make_stopwords():
u"""コピペ用ストップワードを作成して表示
"""
import mojimoji
import cnvk
stopwords=set()
hira=u"あいうえおかきくけこさしすせそたちつてとなにぬねのはひふへほまみむめもらりるれろやゐゆゑよわをんがぎぐげござじずぜぞだぢづでどばびぶべぼぱぴぷぺぽぁぃぅぇぉゃゅょっゔ"
kata=[]
for h in hira:
kata.append(cnvk.convert(h,cnvk.HIRA2KATA,cnvk.Z_KATA))
kata.append(u"ヴ")
hankata=[]
for k in kata:
hankata.append(mojimoji.zen_to_han(k))
kazu=u"0123456789"
stopwords.add(u"10")
stopwords.add(u"11")
stopwords.add(u"12")
stopwords.add(u"13")
stopwords.add(u"14")
stopwords.add(u"15")
stopwords.add(u"16")
stopwords.add(u"17")
stopwords.add(u"18")
stopwords.add(u"19")
stopwords.add(u"20")
stopwords.add(u"１０")
stopwords.add(u"１１")
stopwords.add(u"１２")
stopwords.add(u"１３")
stopwords.add(u"１４")
stopwords.add(u"１５")
stopwords.add(u"１６")
stopwords.add(u"１７")
stopwords.add(u"１８")
stopwords.add(u"１９")
stopwords.add(u"２０")
zenkazu=mojimoji.han_to_zen(kazu)
kazukan=u"一二三四五六七八九十百千万億兆"
minialpha=u"abcdefghijklmnopqlstuvwxyz"
bigalpha=u"ABCDEFGHIJKLMNOPQLSTUVWXYZ"
han_minialpha=mojimoji.han_to_zen(minialpha)
han_bigalpha=mojimoji.han_to_zen(bigalpha)
hiramoji=[u"する",u"なる",u"てる",u"れる",u"やる",u"いる",u"さん",u"なん",u"くん",u"それ",u"こと",\
u"ちゃん",u"ある",u"これ",u"して",u"くれる",u"くださる",u"そう",u"せる",u"した",u"いか",\
u"ので",u"よう",u"てるん",u"もん",u"られる",u"あそこ",u"あたり",u"あちら",u"あっち",u"あと",\
u"あな",u"あなた",u"あれ",u"いくつ",u"いつ",u"いま",u"いろいろ",u"うち",u"おおまか",u"おまえ",u"おれ",
u"がい",u"かく",u"かたちの",u"かやの",u"から",u"がら",u"きた",u"こせ",u"ここ",u"こっち",u"こと",u"ごと",\
u"こちら",u"これ",u"これら",u"ごろ",u"さまざま",u"さらい",u"しかた",u"しよう",u"すか",u"ずつ",u"すね",\
u"そう",u"そこ",u"そちら",u"そっち",u"そで",u"それ",u"それぞれ",u"それなり",u"たくさん",u"たち",u"たび",\
u"ため",u"ちゃ",u"てん",u"とおり",u"とき",u"どこ",u"どこか",u"ところ",u"どちら",u"どれ",u"なか",u"なかば",\
u"なに",u"など",u"なん",u"はじめ",u"はず",u"はるか",u"ひと",u"ひとつ",u"ふく",u"ぶり",u"べつ",u"へん",u"べん",\
u"ほう",u"ほか",u"まさ",u"まし",u"まとも",u"まま",u"みたい",u"みつ",u"みなさん",u"みんな",u"もと",u"もの",\
u"もん",u"やつ",u"よう",u"よそ",u"わけ",u"わたし",u"くる",u"すぎる",u"れる",u"いう",u"くださる",u"ちゃう",\
u"つく",u"せる",u"てるん",u"すぎ",u"ところ",u"おれ",u"ぼく",u"わたし",u"てる",u"しまう",u"みる",
]
katamoji=[]
for h in hiramoji:
katamoji.append(cnvk.convert(h,cnvk.HIRA2KATA,cnvk.Z_KATA))
han_katamoji=[]
for k in katamoji:
han_katamoji.append(mojimoji.zen_to_han(k))
kanmoji=["笑","今","気","今日","明日","方","人","俺","私","僕","時","思う","行く","言う","見る","出す","年","月","日","分","秒","週","火","水","木","金","土","国","都",\
"道","府","県","市","区","町","村","各","第","何","的","度","達","誰","者","類","用","別","等","際","系","品","化","所","毎","回","匹","個","席","束","歳","円","毎",\
"前","後","左","右","次","先","春","夏","秋","冬","下記","上記","時間","今回","前回","場合","自分","ヶ所","ヵ所","カ所","箇所","ヶ月","カ月","箇月","名前","本当","確か","時点",\
"様々","結局","半ば","以前","以後","以降","未満","以上","以下","毎日","自体","何人","手段","感じ","同じ","点","君"]
h_kigou=cnvk.H_KIGO
kigou=[]
for h in h_kigou:
for x in h:
kigou.append(x)
kigou.append(u"ω")
kigou.append(u'ー')
kigou.append(u"д")
#参考 内容推測に適したキーワード抽出のための日本語ストップワード(https://www.jstage.jst.go.jp/article/jjske/12/4/12_511/_pdf)
kokubu_words=[u"ない",u"高い",u"多い",u"少ない","強い","大きい","小さい","長い","ながい",
u"良い",u"よい",u"いい","悪い",
u"ある","いる","なる","行く","いく","来る","とる",
"見る","みる","言う","いう","得る","過ぎる","すぎる",
"する","やる","行なう","行う","おこなう","出来る","できる",
"おもう","思う","考える","かんがえる","わかる","見える",
"知る","しれる","いえる","示す","述べる","書く","かく","よる",
"異なる","違う","ちがう","くらべる",
"入れる","出る","でる","入る","はいる",
"使う","用いる","もちいる","持つ","もつ","作る","つくる",
"なす","起こる","おこる","つく","つける","聞く","よぶ",
"かれる","つまり","上","下","次","つぎ",
"わが国","自分","人々","人びと","別","他","間","話","例","形","日","家","手","名","身",
"そのもの","一つ","あと",
#2016/01/24 更に偏在度の高いものと、忘れてたひらがなを追加
"きゃ","きゅ","きょ","しゃ","しゅ","しょ","ちゃ","ちゅ","ちょ","にゃ","にゅ","にょ",
"ひゃ","ひゅ","ひょ","みゃ","みゅ","みょ","りゃ","りゅ","りょ","ゎ",
"事","目","とこ","中","字","お前","全部","きみ","もらう",
]
for h in hira:
stopwords.add(h)
for k in kata:
stopwords.add(k)
for h in hankata:
stopwords.add(h)
for k in kazu:
stopwords.add(k)
for z in zenkazu:
stopwords.add(z)
for k in kazukan:
stopwords.add(k)
for m in minialpha:
stopwords.add(m)
for b in bigalpha:
stopwords.add(b)
for h in han_minialpha:
stopwords.add(h)
for h in han_bigalpha:
stopwords.add(h)
for h in hiramoji:
stopwords.add(h)
for k in katamoji:
stopwords.add(k)
for h in han_katamoji:
stopwords.add(h)
for k in kanmoji:
stopwords.add(unicode(k))
for k in kigou:
stopwords.add(k)
for k in kokubu_words:
stopwords.add(unicode(k))
print "set([",
for s in sorted(stopwords):
print "u\"{0}\",".format(s),
print "])"
import re
import cnvk
jisx0208 = []
with open("unicode.txt", "r", encoding="utf-8_sig") as f:
for line in f:
line = line.strip()
jisx0208.append(line)
jisx0208.append('\n')
jisx0208.append(" ")
Jisx0208 = set(jisx0208)
"""
with open("Wikitexttrueb.full","r") as f, open("Wikitexttruea.full","a") as g:
for line in f:
#line = cnvk.convert(line,cnvk.Z_ASCII,cnvk.Z_KATA)
line=re.sub('[0-9０-９]+','*',line)
#line=re.sub("\*[\*]+","*",line)
g.write(line)
"""
with open("Random_Contentsadjusttrueb.full",
"r") as f, open("Random_Contentsadjusttrueaver2.full", "a") as g:
for line in f:
line = cnvk.convert(line, cnvk.Z_ASCII, cnvk.Z_KATA)
line = re.sub('[0-9０-９]+', '*', line)
#line=re.sub("\*[\*]+","*",line)
g.write(line)
# coding: utf-8
'''
Created on 2014/02/18
@author: nahki
'''
import MeCab
import cnvk
import unicodedata
from separatewords import MecabTokenize
tagger = MeCab.Tagger("-Ochasen")
# 明日のジェル検。めっちゃ緊張する…。お腹痛い＿（´；ω；‘＿）⌒）＿。ハンドモデルママンにお願いするけど。ママンの爪大丈夫かしら…？。いまさら不安…
text = cnvk.convert(u'明日ママ を 芦田愛菜 に空目。', cnvk.Z_NUM, cnvk.Z_ALPHA, cnvk.Z_KIGO,
cnvk.Z_KATA)
res = MecabTokenize.tokenize(text)
print res
for r in res:
print "result", r
"""
node = tagger.parseToNode(text.encode('utf-8')) #(´・ω・｀)
while node:
#print "%s %s" % (node.surface, node.feature)
print node.surface, node.feature
node = node.next
"""
fin = codecs.open(fin_name, "r", "utf-8")
fout = codecs.open(fout_name, "w", "utf-8")
for line in fin:
word = line.rstrip()
if len(word) <= 3: continue
if re.compile(r'^[-.0-9]+$').search(word) is not None: continue
if re.compile(r'/[0-9]{4}.').search(word) is not None: continue
if re.compile(r'^\.').search(word) is not None: continue
if re.compile(r',').search(word) is not None: continue
print word
word = word.upper()
# word = word.replace(u'"', u'""')
# word = word.replace(u'〜', u'~')
"""
score = [-36000.0 ,-400 *(title.size**1.5)].max.to_i
をちょっと変更することで、良い結果が得られました。
naist-jdicの名詞の標準的なスコアはだいたい6000点ぐらいだったので、
そこから16bitの符号付整数の最小値である-32768に向けてもうちょっと分布が広がるように調整してみました。
score = [-32768.0, (6000 - 200 *(title.size**1.3))].max.to_i
この数式だと日本語でだいたい20文字(utf-8で)ぐらいまでの名詞が 分布されるようになります。
"""
cost = int(max(-32768, 6000 - 200 * len(word)**1.3))
word = cnvk.convert(word.replace(u'_', u' '), cnvk.Z_NUM, cnvk.Z_ALPHA,
cnvk.Z_KIGO, cnvk.Z_KATA).strip()
fout.write(u"\"%s\",,,%d,名詞,一般,*,*,*,*,*,*,*,%s\n" %
(word, cost, u"Wikipedia"))
fin.close()
fout.close()
def sampling_detail(http, cou, number):
List = []
soup2 = BeautifulSoup(http.content, "html.parser")
[s.extract() for s in soup2('sup')]
[s.extract() for s in soup2('annotation')]
[s.extract() for s in soup2('.mw-editsection')]
[s.extract() for s in soup2.select('.gallerybox')]
[s.extract() for s in soup2.select('.mbox-text')]
[s.extract() for s in soup2.select('.geo-multi-punct')]
[s.extract() for s in soup2.select('.geo-nondefault')]
[s.extract() for s in soup2.select('.geo-default')]
[s.extract() for s in soup2.select('.plainlist')]
block = soup2.select('h2 > span[class~=mw-headline]')
for i in block:
over = i.prettify()
if over.find('id="出典"') > -1 or over.find('id="注釈"') > -1 or over.find(
'id="脚注"') > -1 or over.find('id="註釈"') > -1 or over.find(
'id="外部リンク"') > -1:
break
item1 = i.get_text()
item1 = cnvk.convert(item1, cnvk.ZAC, cnvk.ZK, {u"⋯": u"…"},
{u"–": u"―"}, {u"—": u"―"}, {u"－": u"‐"},
{u"－": u"‐"}, {u"～": u"〜"}, {u"·": u"・"},
{u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"},
{u"‹": u"〈"}, {u"»": u"》"}, {u"«": u"《"},
{u"≥": u"≧"}, {u"≤": u"≦"}, {u"µ": u"μ"},
{u"〝": u"“"}, {u"〟": u"”"}, {u"⁄": u"／"},
{u"=": u"＝"})
item1, cou = filt(item1, cou)
if item1.find('注釈') > -1 or item1.find('脚注') > -1 or item1.find(
'註釈') > -1:
break
List.append('\n==' + item1 + '==\n')
texts = i.find_all_next(
['h2', 'h3', 'h4', 'p', 'li', 'dd', 'dt', 'blockquote'])
overlap = []
temp2_prev = texts[0].prettify()
temp2_tx_prev = ''
for j in texts:
temp2 = j.prettify()
if temp2.find('h2') > -1:
break
elif temp2.find('h3') > -1 and temp2.find('mw-headline') > -1:
heading2 = j.select('.mw-headline')
item2 = heading2[0].get_text()
item2 = cnvk.convert(item2, cnvk.ZAC, cnvk.ZK, {u"⋯": u"…"},
{u"–": u"―"}, {u"—": u"―"}, {u"－": u"‐"},
{u"－": u"‐"}, {u"～": u"〜"}, {u"·": u"・"},
{u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"},
{u"‹": u"〈"}, {u"»": u"》"}, {u"«": u"《"},
{u"≥": u"≧"}, {u"≤": u"≦"}, {u"µ": u"μ"},
{u"〝": u"“"}, {u"〟": u"”"}, {u"⁄": u"／"},
{u"=": u"＝"})
item2, cou = filt(item2, cou)
if item2 not in overlap:
List.append('\n===' + item2 + '===\n')
overlap.append(item2)
temp2_prev = temp2
elif temp2.find('h4') > -1 and temp2.find('mw-headline') > -1:
heading3 = j.select('.mw-headline')
item3 = heading3[0].get_text()
item3 = cnvk.convert(item3, cnvk.ZAC, cnvk.ZK, {u"⋯": u"…"},
{u"–": u"―"}, {u"—": u"―"}, {u"－": u"‐"},
{u"－": u"‐"}, {u"～": u"〜"}, {u"·": u"・"},
{u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"},
{u"‹": u"〈"}, {u"»": u"》"}, {u"«": u"《"},
{u"≥": u"≧"}, {u"≤": u"≦"}, {u"µ": u"μ"},
{u"〝": u"“"}, {u"〟": u"”"}, {u"⁄": u"／"},
{u"=": u"＝"})
item3, cou = filt(item3, cou)
if temp2_prev.find('h3') == -1 and item3 not in overlap:
List.append('\n===' + item3 + '===\n')
overlap.append(item3)
if temp2_prev.find('h3') > -1 and item3 not in overlap:
List.append('\n====' + item3 + '====\n')
overlap.append(item3)
temp2_prev = temp2
elif temp2.find('blockquote') > -1:
text0 = j.get_text()
text0 = cnvk.convert(text0, cnvk.ZAC, cnvk.ZK, {u"⋯": u"…"},
{u"–": u"―"}, {u"—": u"―"}, {u"－": u"‐"},
{u"－": u"‐"}, {u"～": u"〜"}, {u"·": u"・"},
{u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"},
{u"‹": u"〈"}, {u"»": u"》"}, {u"«": u"《"},
{u"≥": u"≧"}, {u"≤": u"≦"}, {u"µ": u"μ"},
{u"〝": u"“"}, {u"〟": u"”"}, {u"⁄": u"／"},
{u"=": u"＝"})
text0, cou = filt(text0, cou)
if text0 not in overlap:
List.append('<block>' + text0 + '<block>\n')
overlap.append(text0)
temp2_tx_prev = text0
temp2_prev = temp2
elif temp2.find('<dt>') > -1:
item4 = j.get_text()
item4 = cnvk.convert(item4, cnvk.ZAC, cnvk.ZK, {u"⋯": u"…"},
{u"–": u"―"}, {u"—": u"―"}, {u"－": u"‐"},
{u"－": u"‐"}, {u"～": u"〜"}, {u"·": u"・"},
{u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"},
{u"‹": u"〈"}, {u"»": u"》"}, {u"«": u"《"},
{u"≥": u"≧"}, {u"≤": u"≦"}, {u"µ": u"μ"},
{u"〝": u"“"}, {u"〟": u"”"}, {u"⁄": u"／"},
{u"=": u"＝"})
item4, cou = filt(item4, cou)
if temp2_tx_prev.find(item4) == -1:
if temp2_prev.find('h3') == -1 and item4 not in overlap:
List.append('\n===' + item4 + '===\n')
overlap.append(item4)
if temp2_prev.find('h3') > -1 and item4 not in overlap:
List.append('\n====' + item4 + '====\n')
overlap.append(item4)
temp2_prev = temp2
elif temp2.find('<p>') > -1 or temp2.find(
'<li>') > -1 or temp2.find('<dd>') > -1:
if temp2.find('mwe-math-element') == -1:
text = j.get_text()
text = cnvk.convert(
text, cnvk.ZAC, cnvk.ZK, {u"⋯": u"…"}, {u"–": u"―"},
{u"—": u"―"}, {u"－": u"‐"}, {u"－": u"‐"}, {u"～": u"〜"},
{u"·": u"・"}, {u"⋅": u"・"}, {u" ": u" "}, {u"›": u"〉"},
{u"‹": u"〈"}, {u"»": u"》"}, {u"«": u"《"}, {u"≥": u"≧"},
{u"≤": u"≦"}, {u"µ": u"μ"}, {u"〝": u"“"}, {u"〟": u"”"},
{u"⁄": u"／"}, {u"=": u"＝"})
text, cou = filt(text, cou)
if temp2_tx_prev.find(text) == -1:
if text not in overlap:
List.append(text)
overlap.append(text)
elif temp2.find('mwe-math-element') > -1:
text = '<math-element>\n'
if temp2_tx_prev.find(text) == -1:
List.append(text)
temp2_prev = temp2
fo = codecs.open('Random_Contents' + '.txt', 'a', 'utf-8')
for line in List:
print(line)
line2 = line.strip()
Len = len(line2)
number += Len
fo.write(line)
fo.write('\n')
fo.close()
return cou
# coding: utf-8
'''
Created on 2014/02/18
@author: nahki
'''
import MeCab
import cnvk
import unicodedata
from separatewords import MecabTokenize
tagger = MeCab.Tagger("-Ochasen")
# 明日のジェル検。めっちゃ緊張する…。お腹痛い＿（´；ω；‘＿）⌒）＿。ハンドモデルママンにお願いするけど。ママンの爪大丈夫かしら…？。いまさら不安…
text = cnvk.convert(u'明日ママ を 芦田愛菜 に空目。',
cnvk.Z_NUM, cnvk.Z_ALPHA, cnvk.Z_KIGO, cnvk.Z_KATA)
res = MecabTokenize.tokenize(text)
print res
for r in res:
print "result",r
"""
node = tagger.parseToNode(text.encode('utf-8')) #(´・ω・｀)
while node:
#print "%s %s" % (node.surface, node.feature)
print node.surface, node.feature
node = node.next
"""