def word_frequencies(text):
from manabi.apps.reading_level.word_frequencies import WORD_FREQUENCIES
mecab = MeCab()
frequencies = []
for node in mecab.parse(text.encode('utf8'), as_nodes=True):
frequency = WORD_FREQUENCIES.get(node.surface.decode('utf8'))
if frequency is None:
continue
frequencies.append(frequency)
return frequencies
class MecabTagger(object):
"""docstring, MecabTagger"""
# TAGSET = set("""NNG NNP NNB NNBC NR NP VV VA VX VCP VCN MM MAG MAJ IC
# JKS JKC JKG JKO JKB JKV JKQ JX JC EP EF EC ETN ETM
# XPN XSN XSV XSA XR SF SE SSO SSC SC SY SL SH SN
# UNKNOWN EOS""".split())
def __init__(self, **kwargs):
self.tagger = MeCab(kwargs)
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
del self.tagger
@staticmethod
def tagged_tuple(node):
surface = node.surface
features = node.feature.split(',')
first_pos = features[0].partition('+')[0]
lemma = (features[7].partition('/')[0]
if features[4].startswith('Inflect')
else surface.lower())
return Word(decode(surface, True), decode(lemma, True),
first_pos.decode('ascii'), node.cost)
def parse(self, text): # follow NLTK naming
return [MecabTagger.tagged_tuple(node)
for node in self.tagger.parse(text.encode(settings.DEFAULT_ENCODING), as_nodes=True)
if not node.is_eos()]
if __name__ == '__main__':
path = '../data/hanreiDB'
vocab = defaultdict(lambda: len(vocab))
# open the DB
db = hanrei_db.SQLite3(path)
cur = db.open_db()
# read the data
sql = "select id, syubunPart from hanrei where id<=150"
# sql = u"select id, syubunPart, riyuPart from hanrei"
rows = db.exe_to_db(cur, sql)
train_data = []
test_data = []
nm = MeCab()
for doc_id, syubunPart in rows:
print "--------------"
print "id:", doc_id
# 改行、空白削除
syubunPart = re.sub(r'(\n|\t| | )', '', syubunPart)
# 文分割
sensp = sensplit.SenSplit(syubunPart)
syubun_list = sensp()
for sentence in syubun_list:
if sentence == '':
continue
morph_list = [] # 文を形態素で分割したリスト
sentence = sentence.encode('utf_8') # unicode→str(utf-8)
for n in nm.parse(sentence, as_nodes=True):
def main():
parser = argparse.ArgumentParser(
description='Chainer example: convolutional seq2seq')
parser.add_argument('--batchsize',
'-b',
type=int,
default=48,
help='Number of images in each mini-batch')
parser.add_argument('--epoch',
'-e',
type=int,
default=100,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--unit',
'-u',
type=int,
default=512,
help='Number of units')
parser.add_argument('--layer',
'-l',
type=int,
default=6,
help='Number of layers')
parser.add_argument('--head',
type=int,
default=8,
help='Number of heads in attention mechanism')
parser.add_argument('--dropout',
'-d',
type=float,
default=0.1,
help='Dropout rate')
parser.add_argument('--model', type=str, help='trained model')
parser.add_argument('--input',
'-i',
type=str,
default='./',
help='Input directory')
parser.add_argument('--source',
'-s',
type=str,
default='europarl-v7.fr-en.en',
help='Filename of train data for source language')
parser.add_argument('--target',
'-t',
type=str,
default='europarl-v7.fr-en.fr',
help='Filename of train data for target language')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
parser.add_argument('--source-vocab',
type=int,
default=40000,
help='Vocabulary size of source language')
parser.add_argument('--target-vocab',
type=int,
default=40000,
help='Vocabulary size of target language')
parser.add_argument('--no-bleu',
'-no-bleu',
action='store_true',
help='Skip BLEU calculation')
parser.add_argument('--use-label-smoothing',
action='store_true',
help='Use label smoothing for cross entropy')
parser.add_argument('--embed-position',
action='store_true',
help='Use position embedding rather than sinusoid')
parser.add_argument('--use-fixed-lr',
action='store_true',
help='Use fixed learning rate rather than the ' +
'annealing proposed in the paper')
parser.add_argument('--disable-mecab',
'--dm',
action='store_true',
help='disalbe mecab toknize')
args = parser.parse_args()
print(json.dumps(args.__dict__, indent=4))
# Check file
en_path = os.path.join(args.input, args.source)
source_vocab = ['<eos>', '<unk>', '<bos>'] + \
preprocess.count_words(en_path, args.source_vocab)
source_data = preprocess.make_dataset(en_path, source_vocab)
fr_path = os.path.join(args.input, args.target)
target_vocab = ['<eos>', '<unk>', '<bos>'] + \
preprocess.count_words(fr_path, args.target_vocab)
# print('Original training data size: %d' % len(source_data))
# print('Filtered training data size: %d' % len(train_data))
source_ids = {word: index for index, word in enumerate(source_vocab)}
target_ids = {word: index for index, word in enumerate(target_vocab)}
target_words = {i: w for w, i in target_ids.items()}
source_words = {i: w for w, i in source_ids.items()}
m = MeCab('-Owakati')
# Define Model
model = net.Transformer(args.layer,
min(len(source_ids), len(source_words)),
min(len(target_ids), len(target_words)),
args.unit,
h=args.head,
dropout=args.dropout,
max_length=500,
use_label_smoothing=args.use_label_smoothing,
embed_position=args.embed_position)
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
model.to_gpu(args.gpu)
chainer.serializers.load_npz(args.model, model)
def translate_one(source, target):
words = preprocess.split_sentence(source)
print('# source : ' + ' '.join(words))
x = model.xp.array([source_ids.get(w, 1) for w in words], 'i')
ys = model.translate([x], beam=5)[0]
words = [target_words[y] for y in ys]
print('# result : ' + ' '.join(words))
print('# expect : ' + target)
def tokenize(source, target):
if args.disable_mecab:
return source, target
return m.parse(source), m.parse(target)
while True:
source = input('source> ')
target = input('target> ')
source, target = tokenize(source, target)
translate_one(source, target)
def __init__(self, vocab: Vocab):
self.vocab = vocab
MeCab = try_mecab_import() # type: ignore[func-returns-value]
self.mecab_tokenizer = MeCab("-F%f[0],%f[7]")
def tokenize(mecab: MeCab, sentence: str) -> List[str]:
return [
node.surface for node in mecab.parse(sentence, as_nodes=True)
if node.surface
]
def _non_wrapped_insert_mode(self, session, *, is_develop_mode=True):
with MeCab() as mecab:
manalysis.insert(session, mecab, is_develop_mode=is_develop_mode)
morph.insert(session, is_develop_mode=is_develop_mode)
def __init__(self, **kwargs):
self.tagger = MeCab(kwargs)
# -*- coding: utf-8 -*-
from natto import MeCab
mc = MeCab()
# テキストは cookbiz.jp より
text = "お仕事については基本的には店舗に配属してからのOJTが中心となりますが、先輩スタッフがしっかりとサポートしてくれるので、どなたも安心してお仕事していただけます。2013年には本社内に開発室を設置。店舗配属前にもトレーニングを行なってから実際の店舗に配属されるなど、サポート体制がしっかりと整っているのも当社の魅力。実際、経験が浅い方や未経験スタートのスタッフも多数活躍中！"
print ('Input text:\n'+text)
print('====================================================')
# -F / --node-format オプションでノードの出力フォーマットを指定する
#
# %m ... 形態素の表層文
# %f[0] ... 品詞
# %h ... 品詞 ID (IPADIC)
# %f[8] ... 発音
#
words = []
with MeCab('-F%m,%f[0],%h') as nm:
for n in nm.parse(text, as_nodes=True):
node = n.feature.split(',');
if len(node) != 3:
continue
if node[1] == '名詞':
# if True:
words.append(node[0])
print(words)
if __name__ == '__main__':
path = '../data/hanreiDB'
vocab = defaultdict(lambda: len(vocab))
# open the DB
db = hanrei_db.SQLite3(path)
cur = db.open_db()
# read the data
sql = "select id, syubunPart from hanrei where id<=150"
# sql = u"select id, syubunPart, riyuPart from hanrei"
rows = db.exe_to_db(cur, sql)
train_data = []
test_data = []
nm = MeCab()
for doc_id, syubunPart in rows:
print "--------------"
print "id:", doc_id
# 改行、空白削除
syubunPart = re.sub(r'(\n|\t| | )', '', syubunPart)
# 文分割
sensp = sensplit.SenSplit(syubunPart)
syubun_list = sensp()
for sentence in syubun_list:
if sentence == '':
continue
morph_list = [] # 文を形態素で分割したリスト
sentence = sentence.encode('utf_8') # unicode→str(utf-8)
for n in nm.parse(sentence, as_nodes=True):
node.surface, #表層
feature[0], #品詞1
feature[1], #品詞2
feature[2], #品詞3
feature[3], #品詞4
feature[6], #原型
node.cost, #コスト
node.posid #品詞番号
],
index=df.columns)
df = df.append(series, ignore_index=True)
return df
if __name__ == '__main__':
parser = MeCab(
"-d /usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd")
#parser = MeCab()
nodes = parser.parse("私は人間です。", as_nodes=True)
for node in nodes:
if not node.is_eos():
print(node.surface, " : ", node.feature, node.cost, node.posid)
text = """
私は人間です。
呼吸と食事ができます。
私は望遠鏡で泳ぐ少女を見た。
"""
df = mecab_parse2df(text, parser)
print(df)
def __init__(self):
self.mc = MeCab()
# author: alex
from natto import MeCab
# 31 + 32
with open("verbs.txt", "w+"):
pass
text = open("neko.txt", "r+")
res_file = open("verbs.txt", "a+")
reader = text.readlines()
for line in reader:
with MeCab('-F%f[0],%f[6]') as nm:
for n in nm.parse(line, as_nodes=True):
if not n.is_eos() and n.is_nor():
klass, word = n.feature.split(',', 1)
if klass in ['動詞']: #['名詞', '形容詞', '形容動詞','動詞']:
print word
res_file.write(word + ' ')
res_file.write('\n')
text.close()
res_file.close()
# 33
with open("neko_hen.txt", "w+"):
pass
text = open("neko.txt", "r+")
res_file = open("neko_hen.txt", "a+")
reader = text.readlines()
for line in reader:
with MeCab('-F%f[1],%f[6]') as nm:
for n in nm.parse(line, as_nodes=True):
if not n.is_eos() and n.is_nor():
""" Sample program for perse sites sentences to generate keywords for them. """ from natto import MeCab nm = MeCab() text = "this is a test" print nm.parse(text)
# -*- coding:utf-8 -*-
from natto import MeCab
import collections as cl
import random
from bs4 import BeautifulSoup
import Lib_py.params as params
import Lib_py.study as study
# global instance
mc_g = MeCab('-F%m,%f[0],%h')
def get_next_search_candidate(html_src):
target_str = get_str_from_http(html_src)
if len(target_str) == 0:
raise ("can't extract strings from html source")
parsed_words_l = get_words_list(target_str)
print(parsed_words_l)
study.dump_get_words(parsed_words_l)
common_words_l = study.get_common_words()
common_removed_words_l = [
w for w in parsed_words_l if not w in common_words_l
]
freq_words_l = get_freq_words_list(common_removed_words_l)
return select_next_search_word_candidate(freq_words_l)
def get_str_from_http(html_src):
soup = BeautifulSoup(html_src)
# -*- coding: utf-8 -*-
from natto import MeCab
from numpy import array
from gensim import corpora, matutils
nm = MeCab('-F%m,%f[0],%h')
def get_meishi(sentence):
"""名詞だけを取り出す。
:param sentence: String
:return words: list of String.
入力例) get_meishi("ピンチ、ピンチの時には必ずヒーローが現れる。")
==> ['ピンチ', 'ピンチ', '時', 'ヒーロー']
"""
# -F / --node-format オプションでノードの出力フォーマットを指定する
#
# %m ... 形態素の表層文
# %f[0] ... 品詞
# %h ... 品詞 ID (IPADIC)
# %f[8] ... 発音
#
words = []
for n in nm.parse(sentence, as_nodes=True):
node = n.feature.split(',')
if len(node) != 3:
continue
if node[1] == '名詞':
import os
from natto import MeCab
# bug of wordsを作成するためのライブラリ読み込み
from gensim import corpora, matutils
mc = MeCab()
txt_word_list = []
# テキストファイルを格納しているフォルダを読み込み
files = os.listdir(os.path.dirname(__file__)+'/path/txt')
# フォルダ配下のテキストファイルを1つずつ読み込み
for file in files:
# テキストファイルから名詞と動詞の単語を取り出したリスト作成（Q11-1の処理と同じ）
with open(os.path.dirname(__file__) + '/path/txt/'+file, 'r') as f:
txt = f.read()
word_list = []
for n in mc.parse(txt, as_nodes=True):
if not (n.is_bos() or n.is_eos()):
part, word = n.feature.split(',', 1)
if part == "名詞" or part == "動詞":
word_list.append(n.surface)
# テキストファイルごとの単語リストを追加
txt_word_list.append(word_list)
# bug of wordsを作成するため全種類の単語を把握し、単語IDを付与した辞書を作成
corpus_dic = corpora.Dictionary(txt_word_list)
# 各文章の単語リストをコーパス（辞書の単語IDと単語の出現回数）リストに変換
def main():
nm = MeCab('-Owakati')
word = "MeCabは 京都大学情報学研究科−日本電信電話株式会社コミュニケーション科学基礎研究所 共同研究ユニットプロジェクトを通じて開発されたオープンソース 形態素解析エンジンです。 言語, 辞書,コーパスに依存しない汎用的な設計を 基本方針としています。 パラメータの推定に Conditional Random Fields (CRF) を用 いており, ChaSenが採用している 隠れマルコフモデルに比べ性能が向上しています。また、平均的に ChaSen, Juman, KAKASIより高速に動作します。 ちなみに和布蕪(めかぶ)は, 作者の好物です。"
print(nm.parse(word))
lis = [n.surface for n in nm.parse(word, as_nodes=True) if n.is_nor()]
print(lis)
# coding:utf-8
import pandas as pd
from natto import MeCab
mc = MeCab()
select = pd.read_csv('./input/keyword.csv', encoding='SHIFT-JIS', header=None)
select = select[0].values.tolist()
tango_retu = []
score_retu = []
# 日本語評価極性辞書（用言編）ver.1.0（2008年12月版）
# ポジの用語は 1 ,ネガの用語は -1 と数値化する
with open("./dictionary/wago.121808.pn.txt", 'r') as f:
for l in f.readlines():
l = l.split('\t')
l[1] = l[1].replace(" ", "").replace('\n', '')
value = 1 if l[0].split('（')[0] == "ポジ" else -1
tango_retu.append(l[1])
score_retu.append(value)
wago_dic = dict(zip(tango_retu, score_retu))
tango_retu = []
score_retu = []
# 日本語評価極性辞書（名詞編）ver.1.0（2008年12月版）
# pの用語は 1 eの用語は 0 ,nの用語は -1 と数値化する
with open("./dictionary/pn.csv.m3.120408.trim", 'r') as f:
for l in f.readlines():
# -*- coding: utf-8 -*-
from sklearn.feature_extraction.text import CountVectorizer
from natto import MeCab
_morpheme_type = ['NNG', 'NNP']
_escape_pattern = ['\n']
_nm = MeCab()
def filter_by_type(text):
terms = []
for term_info in str(_nm.parse(text)).split('\n'):
_term_info = term_info.split('\t')
if len(_term_info) < 2:
continue
surface = _term_info[0]
analysis = _term_info[1].split(',')
if analysis[0] in _morpheme_type:
terms.append(surface)
return terms
def generate_corpus2(data_path):
_corpus = []
fp = open(data_path, 'r')
for line in fp.readlines():
if line not in _escape_pattern:
terms = filter_by_type(line)
_corpus.append(' '.join(terms))
from natto import MeCab
import os
from gensim import corpora
mc = MeCab()
txt_list = []
files = os.listdir(os.path.dirname(__file__) + '/path/txt')
for file in files:
with open(os.path.dirname(__file__) + '/path/txt/' + file, 'r') as f:
txt = f.read()
word_list = []
for n in mc.parse(txt, as_nodes=True):
if not (n.is_bos() or n.is_eos()):
part, word = n.feature.split(',', 1)
if part == "名詞" or part == "動詞":
word_list.append(n.surface)
txt_list.append(word_list)
dictionary = corpora.Dictionary(txt_list)
corpus_list = [dictionary.doc2bow(txt) for txt in txt_list]
# 下の行から本書スタート
from gensim import matutils, models
# corpus_listを準備するコードは省略
# TF-IDFのモデルを生成
tfidf_model = models.TfidfModel(corpus_list, normalize=True)
# corpusにTF-IDFを適用
# MeCabをPythonで利用するためのライブラリ読み込み
import os
from natto import MeCab
# merosには、メロスの文章データが格納
# MeCabを実行するオブジェクトを生成
mc = MeCab()
# 下記のコードはテキスト時は、下記のようにする
with open(os.path.dirname(__file__) + '/path/txt/meros.txt', 'r') as f:
txt = f.read()
word_list = []
# MeCabを用いて、形態素解析を実行
for part_and_word in mc.parse(txt, as_nodes=True):
# 形態素解析結果のpart_and_wordが開始/終了オブジェクトでないことを判定
if not (part_and_word.is_bos() or part_and_word.is_eos()):
# 形態素解析結果から品詞と単語を取得
part, word = part_and_word.feature.split(',', 1)
# 名詞と動詞の単語を抽出
if part == '名詞' or part == '動詞':
word_list.append(part_and_word.surface)
# %%
from IPython.display import HTML
from natto import MeCab
nm = MeCab()
a = ""
text = "こんにちは！野球は走る、打つ、投げるスポーツです。"
print(text)
with MeCab('-F%m,%f[0],%h,%f[8]') as nm:
for n in nm.parse(text, as_nodes=True):
lis = n.feature.split(",")
try:
if lis[1] == "動詞":
b = (
"<span style='background-color:#ffcc99'>{0}</span>".format(
lis[0]))
else:
b = (
"<span style='background-color:#ffffff'>{0}</span>".format(
lis[0]))
a = a + b
except IndexError:
pass
display(HTML(a))