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。
时间: 2024-03-26 14:41:29 浏览: 17
好的,下面是一个基于Python的示例代码,演示如何使用结巴分词库对CDIAL-BIAS-race文件进行分词和词性标注,并将语料库分成训练集和测试集,统计HMM中所需的参数:
```
import jieba.posseg as pseg
import random
# 读取CDIAL-BIAS-race文件
with open('CDIAL-BIAS-race.txt', 'r', encoding='utf-8') as f:
corpus = f.readlines()
# 将语料分成训练集和测试集
random.shuffle(corpus)
test_size = len(corpus) // 5
test_corpus = corpus[:test_size]
train_corpus = corpus[test_size:]
# 定义初始概率、发射概率和转移概率的计数器
start_count = {}
emit_count = {}
trans_count = {}
# 统计训练集中的初始概率、发射概率和转移概率
prev_word = ''
for sentence in train_corpus:
words = pseg.cut(sentence.strip())
for word, flag in words:
# 统计初始概率
if prev_word == '':
if flag in start_count:
start_count[flag] += 1
else:
start_count[flag] = 1
# 统计发射概率
if flag in emit_count:
if word in emit_count[flag]:
emit_count[flag][word] += 1
else:
emit_count[flag][word] = 1
else:
emit_count[flag] = {word: 1}
# 统计转移概率
if prev_word != '':
if prev_word in trans_count:
if flag in trans_count[prev_word]:
trans_count[prev_word][flag] += 1
else:
trans_count[prev_word][flag] = 1
else:
trans_count[prev_word] = {flag: 1}
prev_word = flag
# 计算概率
start_prob = {}
emit_prob = {}
trans_prob = {}
# 计算初始概率
total_count = sum(start_count.values())
for flag in start_count:
start_prob[flag] = start_count[flag] / total_count
# 计算发射概率
for flag in emit_count:
total_count = sum(emit_count[flag].values())
emit_prob[flag] = {}
for word in emit_count[flag]:
emit_prob[flag][word] = emit_count[flag][word] / total_count
# 计算转移概率
for prev_word in trans_count:
total_count = sum(trans_count[prev_word].values())
trans_prob[prev_word] = {}
for flag in trans_count[prev_word]:
trans_prob[prev_word][flag] = trans_count[prev_word][flag] / total_count
```
在上述代码中,我们使用了结巴分词库的`pseg.cut()`方法对CDIAL-BIAS-race文件进行分词和词性标注。然后,我们将语料库随机分成训练集和测试集,统计训练集中的初始概率、发射概率和转移概率,并计算它们的概率。
请注意,这只是一个示例代码,您可能需要根据自己的需求进行修改和优化。