那么,你用带段简单的python代码来演示一下SO-PMI算法吧?
时间: 2023-12-21 21:07:21 浏览: 33
好的,下面是一个简单的Python代码示例,用于计算SO-PMI:
``` python
import math
# 定义语料库
corpus = [
"this is a sentence",
"this is another sentence",
"yet another sentence",
"one more sentence"
]
# 计算单词频率
word_freq = {}
for doc in corpus:
words = doc.split()
for word in words:
if word in word_freq:
word_freq[word] += 1
else:
word_freq[word] = 1
# 计算单词共现频率
cooccur_freq = {}
for doc in corpus:
words = doc.split()
for i in range(len(words)):
for j in range(i+1, len(words)):
if words[i] != words[j]:
pair = tuple(sorted([words[i], words[j]]))
if pair in cooccur_freq:
cooccur_freq[pair] += 1
else:
cooccur_freq[pair] = 1
# 计算SO-PMI值
sopmi = {}
for pair in cooccur_freq:
word1 = pair[0]
word2 = pair[1]
freq1 = word_freq[word1]
freq2 = word_freq[word2]
cooccur = cooccur_freq[pair]
pmi = math.log((cooccur * len(corpus)) / (freq1 * freq2))
so_pmi = pmi - math.log(cooccur)
sopmi[pair] = so_pmi
# 输出结果
for pair in sopmi:
print(pair[0], pair[1], sopmi[pair])
```
在这个示例中,我们首先定义了一个包含几个简单句子的语料库。然后,我们计算每个单词在整个语料库中的频率,以及每个单词对(即每个单词与其他单词共同出现的频率)。最后,我们使用这些值来计算每个单词对的SO-PMI值,并输出结果。