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71 lines
2.2 KiB
71 lines
2.2 KiB
5 months ago
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# 根据词性提取实践课程数据中的关键词
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from ltp import LTP
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from jieba import posseg
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import pandas as pd
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from tqdm import tqdm
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import os
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import sys
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sys.path.append(os.getcwd())
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from config import ltp_model_path, subjects_data_path
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from config import user_dict_path, subjects_keywords_path
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tqdm.pandas()
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# 加载ltp模型
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ltp = LTP(ltp_model_path)
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# 读取所有实训数据
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data = pd.read_csv(subjects_data_path, sep='\t', encoding='utf-8')
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def ltp_seg_pos(text):
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"""
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使用ltp分词并根据词性提取出关键词
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"""
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keywords = []
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for sent in tqdm(list(text)):
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content = []
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content.append(sent)
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output = ltp.pipeline(content, tasks=['cws', 'pos'])
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seg_list = output['cws'][0]
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pos_list = output['pos'][0]
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for seg, pos in zip(seg_list, pos_list):
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if pos in ['b', 'n', 'nr', 'ns', 'nh', 'nt', 'vn', 'nz']:
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keywords.append(seg)
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keywords = list(set(keywords))
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return keywords
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def build_keyword(sku_path, to_file, token_method='ltp'):
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"""
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通过词性标注提取关键词
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"""
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print('Start build keywords')
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key_words = []
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if token_method == 'jieba':
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tokens = []
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tokens = data['subject_name'].dropna().progress_apply(
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lambda x: [token for token, pos in posseg.cut(x)
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if pos in ['b', 'n', 'nr', 'ns', 'nh', 'nt', 'vn', 'nz']])
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key_words = [set([tk for idx, sample in tokens.iteritems()
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for tk in sample if len(tk) > 1])]
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key_words = [word for sentence in key_words for word in sentence]
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elif token_method == 'ltp':
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key_words = [key_word for key_word in ltp_seg_pos(data['subject_name'].dropna())]
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print("Building keywords finished")
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if not os.path.exists(os.path.dirname(to_file)):
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os.mkdir(os.path.dirname(to_file))
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if to_file is not None:
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with open(to_file, 'w', encoding='utf-8') as f:
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for word in key_words:
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f.write(word + '\n')
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return key_words
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if __name__ == '__main__':
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build_keyword(user_dict_path, to_file=subjects_keywords_path, token_method='jieba')
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