在python环境下进行,编写出实验代码,编写一个基于HMM的词性标注程序。任务:利用结巴对CDIAL-BIAS-race文件进行分词与词性标注,将语料分成测试集与训练集(一般为1:4的比例)。在训练集上统计HMM中初始概率、发射概率、转移概率估算所需的参数,利用Viterbi算法,实现基于HMM的词性标注程序。 编写评价程序,计算HMM在测试集上的词性标注准确率。
时间: 2023-05-23 15:07:15 浏览: 159
以下是基于HMM的词性标注程序的代码,使用结巴分词对CDIAL-BIAS-race文件进行分词和词性标注:
```python
import jieba.posseg as pseg
import random
def load_corpus(filename):
"""
读取语料文件并返回分词和词性标注结果列表
"""
sentences = []
with open(filename, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if line:
words = pseg.cut(line)
sentences.append([(word.word, word.flag) for word in words])
return sentences
def split_data(corpus, ratio=0.25):
"""
将语料分成训练集和测试集
"""
random.seed(1)
random.shuffle(corpus)
n = int(len(corpus) * ratio)
test_data = corpus[:n]
train_data = corpus[n:]
return train_data, test_data
def train_hmm(train_data, start_p=0.0, beta=1.0):
"""
训练HMM模型,返回初始概率、发射概率和转移概率
"""
tag_count = {} # 统计每个标记出现的次数
word_count = {} # 统计每个词出现的次数
tag_word_count = {} # 统计每个标记-词对出现的次数
tag_transition_count = {} # 统计每个标记之间的转移次数
start_count = 0 # 记录起始标记出现的次数
# 统计训练集中每个标记、词、标记-词对和标记转移出现的次数
for sentence in train_data:
prev_tag = None
for word, tag in sentence:
tag_count[tag] = tag_count.get(tag, 0) + 1
word_count[word] = word_count.get(word, 0) + 1
tag_word_count[(tag, word)] = tag_word_count.get((tag, word), 0) + 1
if prev_tag is not None:
tag_transition_count[(prev_tag, tag)] = tag_transition_count.get((prev_tag, tag), 0) + 1
else:
start_count += 1
prev_tag = tag
# 计算初始概率、发射概率和转移概率
total = sum(tag_count.values())
start_p = start_p or start_count/total
emit_p = { (tag, word): (count+beta)/(tag_count[tag]+beta*len(word_count)) for (tag, word), count in tag_word_count.items() }
trans_p = { (tag1, tag2): (count+beta)/(tag_count[tag1]+beta*len(tag_count)) for (tag1, tag2), count in tag_transition_count.items() }
return start_p, emit_p, trans_p
def viterbi(sentence, start_p, emit_p, trans_p):
"""
使用Viterbi算法计算给定句子的最优标记序列
"""
N = len(sentence)
states = list(emit_p.keys())
# 初始化
dp = [{}]
for state in states:
dp[0][state] = start_p * emit_p.get((state[0], sentence[0]), 0)
# 前向计算
for t in range(1, N):
dp.append({})
for state in states:
max_prob = max(dp[t-1][prev_state] * trans_p.get((prev_state[0], state[0]), 0) * emit_p.get((state[0], sentence[t]), 0) for prev_state in states)
dp[t][state] = max_prob
# 回退
seq = []
max_prob = max(dp[-1].values())
current = None
for state, prob in dp[-1].items():
if prob == max_prob:
seq.append(state[0])
current = state
break
for t in range(N-2, -1, -1):
for state in states:
if dp[t][state] * trans_p.get((state[0], current[0]), 0) * emit_p.get((current[0], sentence[t+1]), 0) == max_prob:
seq.append(state[0])
current = state
break
seq.reverse()
return seq
def evaluate(test_data, start_p, emit_p, trans_p):
"""
测试HMM在测试集上的词性标注准确率
"""
total = 0
correct = 0
for sentence in test_data:
words, tags = zip(*sentence)
pred_tags = viterbi(words, start_p, emit_p, trans_p)
total += len(tags)
correct += sum(1 for i in range(len(tags)) if tags[i] == pred_tags[i])
return correct/total
if __name__ == '__main__':
# 读取语料文件并分成训练集和测试集
corpus = load_corpus('CDIAL-BIAS-race.txt')
train_data, test_data = split_data(corpus)
# 训练HMM模型并测试准确率
start_p, emit_p, trans_p = train_hmm(train_data)
accuracy = evaluate(test_data, start_p, emit_p, trans_p)
print('HMM的词性标注准确率为:', accuracy)
```
注:由于结巴分词的词性标注结果跟CDIAL-BIAS-race语料的格式不太一样,所以需要稍微修改一下load_corpus函数的返回值。