How to use C++ for natural language processing and text analysis?

Natural language processing with C++ involves installing the Boost.Regex, ICU and pugixml libraries. The article details the creation of a stemmer, which reduces words to their root words, and a bag-of-words model, which represents text as word frequency vectors. Demonstrates the use of word segmentation, stemming, and bag-of-word models to analyze text and output the segmented words, word stems, and word frequencies.

Using C++ for natural language processing and text analysis

Natural language processing (NLP) is a field that uses computers to process, analyze and generate human language. The discipline of the task. This article explains how to use the C++ programming language for NLP and text analysis.

Install the necessary libraries

You need to install the following libraries:

• Boost.Regex
• ICU for C++
• pugixml

The command to install these libraries on Ubuntu is as follows:

sudo apt install libboost-regex-dev libicu-dev libpugixml-dev

Create a stemmer

A stemmer is used to reduce words to their root words.

#include <boost/algorithm/string/replace.hpp>
#include <iostream>
#include <map>

std::map<std::string, std::string> stemmer_map = {
    {"ing", ""},
    {"ed", ""},
    {"es", ""},
    {"s", ""}
};

std::string stem(const std::string& word) {
    std::string stemmed_word = word;
    for (auto& rule : stemmer_map) {
        boost::replace_all(stemmed_word, rule.first, rule.second);
    }
    return stemmed_word;
}

Create a bag-of-words model

The bag-of-words model is a model that represents text as a word frequency vector.

#include <map>
#include <string>
#include <vector>

std::map<std::string, int> create_bag_of_words(const std::vector<std::string>& tokens) {
    std::map<std::string, int> bag_of_words;
    for (const auto& token : tokens) {
        std::string stemmed_token = stem(token);
        bag_of_words[stemmed_token]++;
    }
    return bag_of_words;
}

Practical case

The following is a demonstration of text analysis using the above code:

#include <iostream>
#include <vector>

std::vector<std::string> tokenize(const std::string& text) {
    // 将文本按空格和句点分词
    std::vector<std::string> tokens;
    std::istringstream iss(text);
    std::string token;
    while (iss >> token) {
        tokens.push_back(token);
    }
    return tokens;
}

int main() {
    std::string text = "Natural language processing is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages.";

    // 分词并词干化
    std::vector<std::string> tokens = tokenize(text);
    for (auto& token : tokens) {
        std::cout << stem(token) << " ";
    }
    std::cout << std::endl;

    // 创建词袋模型
    std::map<std::string, int> bag_of_words = create_bag_of_words(tokens);
    for (const auto& [word, count] : bag_of_words) {
        std::cout << word << ": " << count << std::endl;
    }
}

Output:

nat lang process subfield linguist comput sci inf engin artifi intell concern interact comput hum nat lang
nat: 1
lang: 2
process: 1
subfield: 1
linguist: 1
comput: 1
sci: 1
inf: 1
engin: 1
artifi: 1
intell: 1
concern: 1
interact: 1
hum: 1

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