用python完成：第3次实验：基于HMM的词性标注-1目标：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。

好的，针对这个任务，我可以给出一个基本的思路供你参考。

首先，你可以使用如下代码读取CDIAL-BIAS-race文件并进行分词与词性标注：

import jieba.posseg as pseg

# 读取CDIAL-BIAS-race文件
with open('CDIAL-BIAS-race.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# 使用jieba进行分词与词性标注
words = pseg.lcut(text)

接下来，你需要将分好的词汇和对应的词性标注按照1:4的比例随机划分为训练集和测试集，可以使用如下代码实现：

import random

# 将words按照1:4的比例划分为train和test
random.shuffle(words)
split_index = int(len(words) * 0.8)
train_words = words[:split_index]
test_words = words[split_index:]

然后，你需要统计HMM模型中的初始概率、发射概率和转移概率。具体来说，对于初始概率，你需要统计在训练集中每个词性出现的次数并进行归一化；对于发射概率，你需要统计在训练集中每个词性下每个词出现的次数并进行归一化；对于转移概率，你需要统计在训练集中每个词性之间的转移次数并进行归一化。可以使用如下代码实现：

# 统计词性出现次数
pos_count = {}
for word, pos in train_words:
    pos_count[pos] = pos_count.get(pos, 0) + 1

# 计算初始概率
pos_init_prob = {}
total_count = sum(pos_count.values())
for pos, count in pos_count.items():
    pos_init_prob[pos] = count / total_count

# 统计每个词性下每个词的出现次数
word_pos_count = {}
for word, pos in train_words:
    if pos not in word_pos_count:
        word_pos_count[pos] = {}
    word_pos_count[pos][word] = word_pos_count[pos].get(word, 0) + 1

# 计算发射概率
word_pos_prob = {}
for pos, count in pos_count.items():
    word_pos_prob[pos] = {}
    total_count = sum(word_pos_count[pos].values())
    for word, word_count in word_pos_count[pos].items():
        word_pos_prob[pos][word] = word_count / total_count

# 统计每个词性之间的转移次数
pos_trans_count = {}
for i in range(len(train_words)-1):
    pos1, pos2 = train_words[i][1], train_words[i+1][1]
    if pos1 not in pos_trans_count:
        pos_trans_count[pos1] = {}
    pos_trans_count[pos1][pos2] = pos_trans_count[pos1].get(pos2, 0) + 1

# 计算转移概率
pos_trans_prob = {}
for pos1, count_dict in pos_trans_count.items():
    pos_trans_prob[pos1] = {}
    total_count = sum(count_dict.values())
    for pos2, count in count_dict.items():
        pos_trans_prob[pos1][pos2] = count / total_count

最后，你可以使用这些概率参数来实现基于HMM的词性标注程序。具体来说，你可以使用维特比算法来进行词性标注。可以使用如下代码实现：

# 定义维特比算法
def viterbi(obs, states, start_p, trans_p, emit_p):
    V = [{}]
    path = {}
    for y in states:
        V[0][y] = start_p[y] * emit_p[y].get(obs[0], 0)
        path[y] = [y]
    for t in range(1, len(obs)):
        V.append({})
        newpath = {}
        for y in states:
            (prob, state) = max([(V[t-1][y0] * trans_p[y0].get(y, 0) * emit_p[y].get(obs[t], 0), y0) for y0 in states if V[t-1][y0] > 0])
            V[t][y] = prob
            newpath[y] = path[state] + [y]
        path = newpath
    (prob, state) = max([(V[len(obs)-1][y], y) for y in states])
    return prob, path[state]

# 对测试集进行词性标注并计算准确率
total_count = 0
correct_count = 0
for word, pos in test_words:
    if word in word_pos_prob.get(pos, {}):
        obs = [word]
        states = list(pos_count.keys())
        start_p = pos_init_prob
        trans_p = pos_trans_prob
        emit_p = word_pos_prob[pos]
        prob, path = viterbi(obs, states, start_p, trans_p, emit_p)
        if path[pos][0] == pos:
            correct_count += 1
        total_count += 1
accuracy = correct_count / total_count
print('Accuracy: {}'.format(accuracy))

以上代码仅为基本思路，实际应用时还需要进行优化和调试。希望可以帮到你！