自然语言算法c语言举例

当谈到自然语言算法的示例时，C语言可以用于实现各种自然语言处理任务。以下是一些常见的自然语言算法在C语言中的示例：

1. 文本分类：将文本按照预定义的类别进行分类。可以使用C语言实现朴素贝叶斯分类算法来进行文本分类。

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

typedef struct {
    char *text;
    char *category;
} TextSample;

typedef struct {
    char *category;
    double probability;
} CategoryProbability;

int main() {
    TextSample trainingData[] = {
        {"I love this movie", "positive"},
        {"This is a great product", "positive"},
        {"I hate this book", "negative"},
        {"This is a terrible experience", "negative"}
    };

    // 计算每个类别的概率
    int numCategories = 2;
    CategoryProbability categoryProbabilities[numCategories];

    int numSamples = sizeof(trainingData) / sizeof(TextSample);
    int numSamplesPerCategory[numCategories] = {0};

    for (int i = 0; i < numSamples; i++) {
        for (int j = 0; j < numCategories; j++) {
            if (strcmp(trainingData[i].category, categoryProbabilities[j].category) == 0) {
                numSamplesPerCategory[j]++;
                break;
            }
        }
    }

    for (int i = 0; i < numCategories; i++) {
        categoryProbabilities[i].category = trainingData[i].category;
        categoryProbabilities[i].probability = (double)numSamplesPerCategory[i] / numSamples;
    }

    // 进行文本分类
    char inputText[] = "I like this product";

    double maxProbability = 0.0;
    char *predictedCategory = NULL;

    for (int i = 0; i < numCategories; i++) {
        double probability = categoryProbabilities[i].probability;

        char *token = strtok(inputText, " ");
        while (token != NULL) {
            // 在训练数据中计算单词在当前类别中的概率
            // 并将所有单词的概率相乘得到文本属于当前类别的概率
            probability *= calculateWordProbability(token, categoryProbabilities[i].category);

            token = strtok(NULL, " ");
        }

        if (probability > maxProbability) {
            maxProbability = probability;
            predictedCategory = categoryProbabilities[i].category;
        }
    }

    printf("Predicted category: %s\n", predictedCategory);

    return 0;
}

2. 情感分析：判断文本中的情感倾向，如积极、消极或中性。可以使用C语言编写一个简单的情感分析算法，通过预定义的情感词典来判断文本情感。

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

typedef struct {
    char *word;
    int sentimentScore;
} SentimentWord;

int main() {
    SentimentWord sentimentDictionary[] = {
        {"love", 1},
        {"great", 1},
        {"hate", -1},
        {"terrible", -1}
    };

    char inputText[] = "I love this movie. It's great!";

    int totalSentimentScore = 0;
    char *token = strtok(inputText, " ");
    while (token != NULL) {
        for (int i = 0; i < sizeof(sentimentDictionary) / sizeof(SentimentWord); i++) {
            if (strcmp(token, sentimentDictionary[i].word) == 0) {
                totalSentimentScore += sentimentDictionary[i].sentimentScore;
                break;
            }
        }

        token = strtok(NULL, " ");
    }

    if (totalSentimentScore > 0) {
        printf("Positive sentiment\n");
    } else if (totalSentimentScore < 0) {
        printf("Negative sentiment\n");
    } else {
        printf("Neutral sentiment\n");
    }

    return 0;
}

这只是一些自然语言算法在C语言中的示例。实际上，C语言可以用于实现更复杂的自然语言处理任务，如命名实体识别、文本生成等。