我想查出两篇文本中有没有两句高度相似的句子,即逐句查重
时间: 2023-11-22 22:05:28 浏览: 34
可以使用文本相似度算法来逐句查重。其中,常用的算法包括余弦相似度、Jaccard相似度等。这里以余弦相似度为例,给出一种基于Python的实现方式。
首先,需要将两篇文本分别按照句子进行分割,并去除停用词和标点符号。可以使用NLTK库中的分词和停用词功能来实现:
```python
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
stop_words = set(stopwords.words('english'))
def preprocess(text):
sentences = sent_tokenize(text)
words = [word_tokenize(sentence.lower()) for sentence in sentences]
words = [[word for word in sentence if word.isalnum() and not word in stop_words] for sentence in words]
return words
```
接下来,可以计算每个句子的TF-IDF向量,并将其转化为单位向量,以便计算余弦相似度:
```python
from collections import Counter
from math import log, sqrt
def compute_tfidf(word_counts, all_word_counts, num_docs):
tfidf = {}
for word, count in word_counts.items():
tf = count / len(word_counts)
idf = log(num_docs / all_word_counts[word])
tfidf[word] = tf * idf
return tfidf
def compute_unit_vector(vector):
norm = sqrt(sum([value**2 for value in vector.values()]))
return {word: value / norm for word, value in vector.items()}
def compute_sentence_vectors(words):
sentence_vectors = []
all_word_counts = Counter([word for sentence in words for word in sentence])
num_docs = len(words)
for sentence in words:
word_counts = Counter(sentence)
tfidf = compute_tfidf(word_counts, all_word_counts, num_docs)
unit_vector = compute_unit_vector(tfidf)
sentence_vectors.append(unit_vector)
return sentence_vectors
```
最后,可以计算两篇文本中每个句子的余弦相似度,并判断是否有高度相似的句子:
```python
def cosine_similarity(vector1, vector2):
dot_product = sum([vector1[word] * vector2.get(word, 0) for word in vector1])
norm1 = sqrt(sum([value**2 for value in vector1.values()]))
norm2 = sqrt(sum([value**2 for value in vector2.values()]))
return dot_product / (norm1 * norm2)
def find_similar_sentences(text1, text2, threshold=0.9):
words1 = preprocess(text1)
words2 = preprocess(text2)
sentence_vectors1 = compute_sentence_vectors(words1)
sentence_vectors2 = compute_sentence_vectors(words2)
similar_pairs = []
for i, vector1 in enumerate(sentence_vectors1):
for j, vector2 in enumerate(sentence_vectors2):
similarity = cosine_similarity(vector1, vector2)
if similarity >= threshold:
similar_pairs.append((i, j, similarity))
return similar_pairs
```
使用以上代码,可以找到两篇文本中高度相似的句子。其中,threshold参数控制相似度的阈值,可以根据实际情况进行调整。