使用TF-IDF和BM25提取文章关键词
评估方法：
人工从文章中提取1-5个关键词，和机器提取的关键词做比较
召回 = 机器提词∩人工提词 / 人工提词
准确 = 机器提词∩人工提词 / 机器提词
TF-IDF
实现参考：tf-idf-keyword
其他参考： 使用不同的方法计算TF-IDF值
第一版 标题和正文加权计算tf-idf
主要策略
(1)使用nlpc切词服务(可用jieba切词代替)+TF-IDF提取关键词。
(2)去除停用词
(3)按照体裁+年级分成若干类型，来训练模型，示例用高中+叙事类，取了20000条数据训练
(4)对标题进行加权，标题的每个词汇频率+6，再合一起计算tf-idf
(5)按照权重取前4个关键词，在这4个关键词中对于权重小于 频率(5)*平均IDF/总词数 的进行过滤
注：以上数据均为调节后最优解
相关推荐：《Python视频教程》
代码实现
config.py `program = 'composition_term_weight'
logger = logging.getLogger(program)
logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s',
stream=sys.stderr,
datefmt='%a, %d %b %Y %H:%M:%S')
logging.root.setLevel(level=logging.INFO)IDFLoader.pyclass IDFLoader(object):
"""词典加载类"""
def __init__(self, idf_path):
self.idf_path = idf_path
self.idf_freq = {} # idf
self.mean_len = 0 #平均长度
self.mean_idf = 0.0 # 均值
self.load_idf()
def load_idf(self):
"""从文件中载入idf"""
cnt = 0
with open(self.idf_path, 'rb') as f:
for line in f:
try:
word, freq = line.strip().decode('utf-8', errors='ignore').split(' ')
if word == 'LEN_AVG':
self.mean_len = int(freq)
break
self.idf_freq[word] = float(freq)
cnt += 1
except Exception as e:
# logger.error('load_idf error: ' + e.message + ' line: ' + line.decode('utf-8', errors='ignore'))
continue
self.mean_idf = sum(self.idf_freq.values()) / cnt
logger.info('Vocabularies %s loaded: %d mean_idf: %d' % (self.idf_path, cnt, self.mean_idf))`
`class TfIdf(object):
"""TF-IDF"""
# 对正文进行过滤
p_cut = pile(r'[a-zA-Z0-9]', re.VERBOSE)
# 对标题进行过滤
p_title = pile(r'作文|\d+字|.年级|_', re.VERBOSE)
# 过滤常用标点符号等，也可以放到停用词表中
ignored = ['', ' ', '', '。', '：', '，', ')', '(', '！', '?', '”', '“', '＂', '―', '．', '说', '好', '时']
# 主题最小出现次数，用于过滤权重不达标的关键词
min_times = 5.0
# 标题加权次数
title_add_times = 6.0
# 取关键词的个数
words_num = 4
def __init__(self):
# 1. 获取停用词库
my_stop_words_path = 'stop_words.utf8.txt'
self.stop_words_dict = []
with open(my_stop_words_path, 'rb') as fr:
for line in fr.readlines():
self.stop_words_dict.append(line.strip())
def my_cut(self, inTxt):
"""切词"""
inTxt = self.p_cut.sub('', str(inTxt))
words_list = []
# 由于性能问题，一句一句的切词
for l in inTxt.split('。'):
# NLPC切词服务，可用jieba切词代替
r = cut(l)
if r is not None:
words_list += r
return [w for w in words_list if w not in self.stop_words_dict and w not in self.ignored and len(w.strip()) > 0]
def get_tfidf(self, idf_loader, title, content):
"""计算文章tf-idf"""
filter_title = self.p_title.sub('', title.encode('utf-8', errors='ignore'))
title_words = self.my_cut(filter_title)
corpus0 = title_words + self.my_cut(content)
freq = {}
for w in corpus0:
freq[w] = freq.get(w, 0.0) + 1.0
# 对标题进行加权
for w in title_words:
logger.info(freq[w])
freq[w] = freq.get(w, 0.0) + self.title_add_times
logger.info(freq[w])
total = sum(freq.values())
for k in freq: # 计算 TF-IDF
freq[k] *= idf_loader.idf_freq.get(k, idf_loader.mean_idf) / total
return sorted(freq.items(), key=lambda d: d[1], reverse=True), len(corpus0), title_words
def get_term_weight(self, idf_loader, title, content):
"""获得term权重"""
result, words_number, title_words = self.get_tfidf(idf_loader, title, content)
bound = self.min_times * idf_loader.mean_idf / words_number
machine_words = [item for item in result[:4] if item[1] > bound]
# machine_words = [item for item in result[:self.words_num]]
if len(machine_words) < 1:
# 如果一个term都没有，则把标题拿出来
machine_words = [item for item in result if item[1] in title_words]
data = []
offset = 0
for i, word in enumerate(machine_words):
data.append('%s:%d:%s' % (word[0], offset, str(round(word[1], 4))))
offset += len(word[0].decode('utf-8', errors='ignore'))
return data
def getCorpus(self, data_path):
"""获取词表"""
count = 0
corpus_list = []
with open(data_path, 'rb') as f:
for line in f:
info = json.loads(line.decode('utf-8', errors='ignore'))
sentence = self.p_title.sub('', info.get('title').encode('utf-8', errors='ignore')) + '。' + info.get(
'@merge_text').encode('utf-8', errors='ignore')
r = self.my_cut(sentence)
if not r:
continue
corpus_list.append(r)
count += 1
if count % 1000 == 0:
logger.info("processd " + str(count) + " segment_sentence")
return corpus_list
def train(self, dir_name, data_path):
"""训练模型"""
idf_path = 'data/%s/idf.txt' % dir_name
documents = self.getCorpus(data_path)
id_freq = {}
i = 0
len_sum = 0
for doc in documents:
len_sum += len(doc)
doc = set(doc)
for x in doc:
id_freq[x] = id_freq.get(x, 0) + 1
if i % 1000 == 0:
logger.info('Documents processed: ' + str(i) + ', time: ' + str(datetime.datetime.now()))
i += 1
del documents
with open(idf_path, 'wb') as f:
for key, value in id_freq.items():
f.write(key + ' ' + str(math.log(i / value, 2)) + '\n')
logger.info(str(i) + ' ' + str(len_sum))
f.write('LEN_AVG ' + str(len_sum / i))
def test_one(self, dir_name, method='tfidf'):
"""单个测试"""
idf_loader = IDFLoader('data/%s/idf.txt' % dir_name)
for item in sys.stdin:
info = json.loads(item.decode('utf-8', errors='ignore'))
title = info['title']
content = info['@merge_text']
if method == 'tfidf':
result, words_number, title_words = self.get_tfidf(idf_loader, title, content)
else:
result, words_number, title_words = self.get_bm25(idf_loader, title, content)
bound = self.min_times * idf_loader.mean_idf / words_number
print '_____words_number bound_____'
print words_number, bound
print '_____tfidf_result_____'
for item in result[:20]:
print item[0].encode('utf-8', errors='ignore'), item[1]`
经调优，最优解为：min_times=5 title_add_times=6.0 words_num=4
结果: 人工抽样评估了100个
TF-IDF召回率：0.2778
TF-IDF准确率：0.2778 BM25
算法参考： 搜索中的权重度量利器: TF-IDF和BM25
第一版
TfIdf.py 增加方法： ` def get_bm25(self, idf_loader, title, content):
"""计算bm25"""
k = 1.2 # 用来限制TF值的增长极限
b = 0.75 # b是一个常数，它的作用是规定L对评分的影响有多大。
# L是文档长度与平均长度的比值
EPSILON = 0.25 # 如果idf词表中没有，则平均idf*该值
filter_title = self.p_title.sub('', title.encode('utf-8', errors='ignore'))
title_words = self.my_cut(filter_title)
corpus0 = title_words + self.my_cut(content)
freq = {}
for w in corpus0:
freq[w] = freq.get(w, 0.0) + 1.0
# 对标题进行加权
for w in title_words:
freq[w] = freq.get(w, 0.0) + self.title_add_times
total = sum(freq.values())
logger.info(str((k, b, total, idf_loader.mean_len)))
for i in freq:
tf = freq[i] / total
idf = idf_loader.idf_freq.get(i, idf_loader.mean_idf * EPSILON)
freq[i] = idf * ((k + 1) * tf) / (k * (1.0 - b + b * (total / idf_loader.mean_len)) + tf)
return sorted(freq.items(), key=lambda d: d[1], reverse=True), len(corpus0), title_words
` 经调优，最优解为：min_times=2.5 title_add_times=6.0 words_num=4 k=1.2 b=0.75 EPSILON=0.25
结果
人工抽样评估了100个
BM25召回率：0.2889
BM25准确率：0.3333
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