Rust语言模型库lingua-bulgarian-language-model的使用指南

简介

lingua-bulgarian-language-model是一个Rust库，专门用于保加利亚语文本处理和自然语言分析。它为开发者提供了处理保加利亚语文本的各种功能，包括语言检测、文本分析等。

安装

在Cargo.toml中添加依赖：

[dependencies]
lingua-bulgarian-language-model = "0.1"  # 请使用最新版本号

基本使用方法

1. 语言检测

use lingua_bulgarian_language_model::LanguageDetector;

fn main() {
    let detector = LanguageDetector::default();
    let text = "Това е текст на български език.";
    
    match detector.detect_language_of(text) {
        Some(language) => println!("检测到的语言: {:?}", language),
        None => println!("无法确定语言"),
    }
}

2. 文本分词

use lingua_bulgarian_language_model::BulgarianTokenizer;

fn main() {
    let tokenizer = BulgarianTokenizer::new();
    let text = "България е красива страна.";
    let tokens = tokenizer.tokenize(text);
    
    println!("分词结果: {:?}", tokens);
}

3. 词性标注

use lingua_bulgarian_language_model::BulgarianTagger;

fn main() {
    let tagger = BulgarianTagger::new();
    let text = "София е столицата на България.";
    let tagged = tagger.tag(text);
    
    for (word, tag) in tagged {
        println!("{}: {}", word, tag);
    }
}

高级功能

1. 命名实体识别

use lingua_bulgarian_language_model::BulgarianNer;

fn main() {
    let ner = BulgarianNer::new();
    let text = "София е столицата на България. Президентът Румен Радев е държавният глава.";
    let entities = ner.extract_entities(text);
    
    for entity in entities {
        println!("{:?}: {} ({}-{})", entity.tag, entity.text, entity.start, entity.end);
    }
}

2. 情感分析

use lingua_bulgarian_language_model::BulgarianSentimentAnalyzer;

fn main() {
    let analyzer = BulgarianSentimentAnalyzer::new();
    let text = "Това е страхотен продукт!";
    let sentiment = analyzer.analyze(text);
    
    println!("情感分析结果: {:?}", sentiment);
    // 可能输出: Positive(0.85)
}

性能优化

对于大量文本处理，可以使用批处理模式：

use lingua_bulgarian_language_model::{LanguageDetector, BulgarianTokenizer};

fn main() {
    let detector = LanguageDetector::default();
    let tokenizer = BulgarianTokenizer::new();
    
    let texts = vec![
        "Здравейте!",
        "Как си днес?",
        "Това е тестов текст.",
    ];
    
    // 批量语言检测
    let languages = detector.detect_languages_in_batch(&texts);
    
    // 批量分词
    let all_tokens = tokenizer.tokenize_batch(&texts);
    
    for (i, (text, language, tokens)) in texts.iter().zip(languages).zip(all_tokens).enumerate() {
        println!("文本 {}: {}", i+1, text);
        println!("  语言: {:?}", language);
        println!("  分词: {:?}", tokens);
    }
}

完整示例

下面是一个完整的使用示例，展示了该库的主要功能：

use lingua_bulgarian_language_model::{
    LanguageDetector, 
    BulgarianTokenizer,
    BulgarianTagger,
    BulgarianNer,
    BulgarianSentimentAnalyzer
};

fn main() {
    // 示例文本
    let sample_text = "София е столицата на България. Президентът Румен Радев е държавният глава. Това е страхотен продукт!";

    // 1. 语言检测
    let detector = LanguageDetector::default();
    match detector.detect_language_of(sample_text) {
        Some(language) => println!("检测到的语言: {:?}", language),
        None => println!("无法确定语言"),
    }

    // 2. 文本分词
    let tokenizer = BulgarianTokenizer::new();
    let tokens = tokenizer.tokenize(sample_text);
    println!("\n分词结果: {:?}", tokens);

    // 3. 词性标注
    let tagger = BulgarianTagger::new();
    let tagged = tagger.tag(sample_text);
    println!("\n词性标注结果:");
    for (word, tag) in tagged {
        println!("{}: {}", word, tag);
    }

    // 4. 命名实体识别
    let ner = BulgarianNer::new();
    let entities = ner.extract_entities(sample_text);
    println!("\n命名实体识别结果:");
    for entity in entities {
        println!("{:?}: {} ({}-{})", entity.tag, entity.text, entity.start, entity.end);
    }

    // 5. 情感分析
    let analyzer = BulgarianSentimentAnalyzer::new();
    let sentiment = analyzer.analyze(sample_text);
    println!("\n情感分析结果: {:?}", sentiment);
}

注意事项

1. 首次使用时可能需要下载模型数据，这可能需要一些时间
2. 处理长文本时建议分块处理以获得更好性能
3. 保加利亚语有复杂的语法规则，某些边缘情况可能处理不够完美

这个库为Rust开发者提供了处理保加利亚语文本的强大工具，可以轻松集成到各种自然语言处理应用中。