AI technology applied to document comparison

The benefit of document comparison through AI is that it can automatically detect and quickly compare changes and differences between documents, saving time and labor and reducing the risk of human error. In addition, AI can process large amounts of text data, improve processing efficiency and accuracy, and can compare different versions of documents to help users quickly find the latest version and changed content.

AI document comparison usually includes two main steps: text preprocessing and text comparison. First, the text needs to be preprocessed to convert it into a computer-processable form. Then, the differences between the texts are determined by comparing their similarity. The following will take the comparison of two text files as an example to introduce this process in detail.

Text preprocessing

First, we need to preprocess the text. This includes operations such as word segmentation, stop word removal, and stemming so that computers can process the text. In this example, we can use the NLTK library in Python for preprocessing. Here is a simple code example: ```python import nltk from nltk.corpus import stopwords from nltk.stem import PorterStemmer from nltk.tokenize import word_tokenize # Download stopword and stemmer resources nltk.download('stopwords') nltk.download('punkt') # Define stopwords and stemmers stop_words = set(stopwords.words('english')) stemmer = PorterStemmer() # define text text = "This is an example sentence. We need to preprocess it." # Participle tokens = word_tokenize(text) # Remove stop words and stemming filtered_text = [stemmer.stem(word) for word in

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

def preprocess(text):
    # 分词
    tokens = word_tokenize(text.lower())
    # 去除停用词
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [token for token in tokens if token not in stop_words]
    # 词干提取
    porter = PorterStemmer()
    stemmed_tokens = [porter.stem(token) for token in filtered_tokens]
    # 返回处理后的文本
    return stemmed_tokens

Calculating similarity

Next, we need to calculate the difference between the two texts similarity between. Commonly used methods include cosine similarity, Jaccard similarity, etc. In this example, we will use cosine similarity to compare the similarity of two texts. The following is a code example for calculating cosine similarity:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def compare(text1, text2):
    # 对文本进行预处理
    processed_text1 = preprocess(text1)
    processed_text2 = preprocess(text2)
    # 将文本转化为TF-IDF向量
    tfidf_vectorizer = TfidfVectorizer()
    tfidf_matrix = tfidf_vectorizer.fit_transform([text1, text2])
    #计算文本间的余弦相似度
    similarity = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])[0][0]
    # 返回相似度
    return similarity

Now, we can combine the above two functions to write a complete text comparison program. The following is a code example:

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def preprocess(text):
    # 分词
    tokens = word_tokenize(text.lower())
    # 去除停用词
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [token for token in tokens if token not in stop_words]
    # 词干提取
    porter = PorterStemmer()
    stemmed_tokens = [porter.stem(token) for token in filtered_tokens]
    # 返回处理后的文本
    return stemmed_tokens

def compare(text1, text2):
    # 对文本进行预处理
    processed_text1 = preprocess(text1)
    processed_text2 = preprocess(text2)
    # 将文本转化为TF-IDF向量
    tfidf_vectorizer = TfidfVectorizer()
    tfidf_matrix = tfidf_vectorizer.fit_transform([text1, text2])
    # 计算文本间的余弦相似度
    similarity = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])[0][0]
    # 返回相似度
    return similarity

if __name__ == '__main__':
    # 读取文件内容
    with open('file1.txt', 'r') as f1:
        text1 = f1.read()
    with open('file2.txt', 'r') as f2:
        text2 = f2.read()
    # 对比两个文件的文本相似度
    similarity = compare(text1, text2)
    print('The similarity between the two files is: ', similarity)

With the above code, we can read the contents of two text files and calculate the similarity between them.

It should be noted that the above program is just a simple example. Actual applications may require more complex text preprocessing and comparison methods, as well as the ability to process large amounts of text files. In addition, due to the complexity of text, text comparison does not always accurately reflect text differences, so sufficient testing and verification is required in practical applications.

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