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。

好的，请先确保已经安装好结巴分词库。

以下是基于HMM的词性标注程序的实现：

import jieba.posseg as pseg
import random

# 加载语料库
corpus_file = 'CDIAL-BIAS-race.txt'
corpus = []
with open(corpus_file, 'r', encoding='utf-8') as f:
    for line in f.readlines():
        corpus.append(line.strip())

# 划分训练集和测试集
random.shuffle(corpus)
train_size = int(len(corpus) * 0.8)
train_corpus = corpus[:train_size]
test_corpus = corpus[train_size:]

# 统计HMM所需的参数
states = set()
observations = set()
start_prob = {}
emit_prob = {}
trans_prob = {}

for sentence in train_corpus:
    words = pseg.cut(sentence)
    prev_state = None
    for w, s in words:
        states.add(s)
        observations.add(w)
        if prev_state is None:
            start_prob[s] = start_prob.get(s, 0) + 1
        else:
            trans_prob[(prev_state, s)] = trans_prob.get((prev_state, s), 0) + 1
        emit_prob[(s, w)] = emit_prob.get((s, w), 0) + 1
        prev_state = s

# 计算概率
for s in states:
    start_prob[s] = start_prob.get(s, 0) / len(train_corpus)
    for o in observations:
        emit_prob[(s, o)] = emit_prob.get((s, o), 0) / sum([emit_prob.get((s, w), 0) for w in observations])
    for s2 in states:
        trans_prob[(s, s2)] = trans_prob.get((s, s2), 0) / sum([trans_prob.get((s, s3), 0) for s3 in states])

# Viterbi算法
def viterbi(obs, states, start_p, trans_p, emit_p):
    V = [{}]
    path = {}
    for y in states:
        V[0][y] = start_p.get(y, 0) * emit_p.get((y, 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.get((y0, y), 0) * emit_p.get((y, obs[t]), 0), y0) for y0 in states])
            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])

# 评价程序
def evaluate(test_corpus, states, start_prob, trans_prob, emit_prob):
    total = 0
    correct = 0
    for sentence in test_corpus:
        words = pseg.cut(sentence)
        obs = [w for w, s in words]
        tags = [s for w, s in words]
        _, pred_tags = viterbi(obs, states, start_prob, trans_prob, emit_prob)
        total += len(tags)
        correct += sum([1 for i in range(len(tags)) if tags[i] == pred_tags[i]])
    return correct / total

# 计算词性标注准确率
accuracy = evaluate(test_corpus, states, start_prob, trans_prob, emit_prob)
print('词性标注准确率：', accuracy)

这个程序首先加载CDIAL-BIAS-race语料库，然后按照1:4的比例划分训练集和测试集。接着统计HMM所需的参数：初始概率、发射概率和转移概率，并计算它们的概率。最后，使用Viterbi算法对测试集进行词性标注，并使用评价程序计算HMM在测试集上的词性标注准确率。

注意，这个程序需要一些时间才能完成运行，因为要处理的语料库比较大。在运行期间，请耐心等待。