C# Sdcb.PaddleInference 中文分词、词性标注

C# Sdcb.PaddleInference 中文分词、词性标注

目录

效果

项目

代码

下载

参考


效果

项目

代码

using Sdcb.PaddleNLP.Lac;

using System;

using System.Collections.Generic;

using System.Data;

using System.Linq;

using System.Windows.Forms;

namespace C__Sdcb.PaddleInference_中文分词_词性标注

{

public partial class Form1 : Form

{

public Form1()

{

InitializeComponent();

}

ChineseSegmenter segmenter;

private void button1_Click(object sender, EventArgs e)

{

string input = "我是中国人,我爱我的祖国。";

textBox1.Text = input;

string[] result = segmenter.Segment(input);

textBox2.Text = string.Join(",", result);

}

private void Form1_Load(object sender, EventArgs e)

{

segmenter = new ChineseSegmenter();

}

private void button2_Click(object sender, EventArgs e)

{

string input = "我爱北京天安门";

textBox1.Text = input;

textBox2.Text = "";

WordAndTag[] result = segmenter.Tagging(input);

string labels = string.Join(",", result.Select(x => x.Label));

string words = string.Join(",", result.Select(x => x.Word));

string tags = string.Join(",", result.Select(x => x.Tag));

textBox2.Text += "words:" + words + "\r\n";

textBox2.Text += "labels:" + labels + "\r\n";

textBox2.Text += "tags" + tags + "\r\n";

}

private void button3_Click(object sender, EventArgs e)

{

string input = "我爱北京天安门";

textBox1.Text = input;

textBox2.Text = "";

Dictionary<string, WordTag?> customizedWords = new Dictionary<string, WordTag?>();

customizedWords.Add("北京天安门", WordTag.LocationName);

LacOptions lacOptions = new LacOptions(customizedWords);

ChineseSegmenter segmenter_custom = new ChineseSegmenter(lacOptions);

WordAndTag[] result = segmenter_custom.Tagging(input);

string labels = string.Join(",", result.Select(x => x.Label));

string words = string.Join(",", result.Select(x => x.Word));

string tags = string.Join(",", result.Select(x => x.Tag));

textBox2.Text += "words:" + words + "\r\n";

textBox2.Text += "labels:" + labels + "\r\n";

textBox2.Text += "tags" + tags + "\r\n";

}

}

}

复制代码
using Sdcb.PaddleNLP.Lac;
using System;
using System.Collections.Generic;
using System.Data;
using System.Linq;
using System.Windows.Forms;

namespace C__Sdcb.PaddleInference_中文分词_词性标注
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        ChineseSegmenter segmenter;

        private void button1_Click(object sender, EventArgs e)
        {
            string input = "我是中国人,我爱我的祖国。";
            textBox1.Text = input;
            string[] result = segmenter.Segment(input);
            textBox2.Text = string.Join(",", result);

        }

        private void Form1_Load(object sender, EventArgs e)
        {
            segmenter = new ChineseSegmenter();
        }

        private void button2_Click(object sender, EventArgs e)
        {
            string input = "我爱北京天安门";
            textBox1.Text = input;
            textBox2.Text = "";
            WordAndTag[] result = segmenter.Tagging(input);
            string labels = string.Join(",", result.Select(x => x.Label));
            string words = string.Join(",", result.Select(x => x.Word));
            string tags = string.Join(",", result.Select(x => x.Tag));

            textBox2.Text += "words:" + words + "\r\n";
            textBox2.Text += "labels:" + labels + "\r\n";
            textBox2.Text += "tags" + tags + "\r\n";
        }

        private void button3_Click(object sender, EventArgs e)
        {
            string input = "我爱北京天安门";
            textBox1.Text = input;
            textBox2.Text = "";

            Dictionary<string, WordTag?> customizedWords = new Dictionary<string, WordTag?>();
            customizedWords.Add("北京天安门", WordTag.LocationName);

            LacOptions lacOptions = new LacOptions(customizedWords);

            ChineseSegmenter segmenter_custom = new ChineseSegmenter(lacOptions);

            WordAndTag[] result = segmenter_custom.Tagging(input);
            string labels = string.Join(",", result.Select(x => x.Label));
            string words = string.Join(",", result.Select(x => x.Word));
            string tags = string.Join(",", result.Select(x => x.Tag));

            textBox2.Text += "words:" + words + "\r\n";
            textBox2.Text += "labels:" + labels + "\r\n";
            textBox2.Text += "tags" + tags + "\r\n";
        }
    }
}

下载

源码下载

参考

https://github.com/sdcb/PaddleSharp/blob/master/docs/paddlenlp-lac.md

相关推荐
阿杰学AI7 小时前
AI核心知识135—大语言模型之 OpenClaw(简洁且通俗易懂版)
人工智能·ai·语言模型·自然语言处理·aigc·ai编程·openclaw
容智信息9 小时前
国家级算力底座+企业级智能体:容智Agent OS 获选入驻移动云能中心,联手赋能千行百业
大数据·人工智能·自然语言处理·智慧城市
阿杰学AI10 小时前
AI核心知识136—大语言模型之 自我蒸馏(简洁且通俗易懂版)
人工智能·语言模型·自然语言处理
Zzj_tju10 小时前
大语言模型部署实战:FP16、INT8、4bit 量化怎么选?吞吐、精度与显存的真实权衡
人工智能·语言模型·自然语言处理
周末也要写八哥10 小时前
浅谈:大语言模型中的逆转诅咒现象
人工智能·语言模型·自然语言处理
_冷眸_11 小时前
Voyago:龙虾(OpenClaw)驱动的一站式旅行规划套件
人工智能·自然语言处理·aigc·agent·claude code
大龄程序员狗哥11 小时前
第17篇:词向量(Word2Vec)解析——让文字拥有数学灵魂(原理解析)
人工智能·自然语言处理·word2vec
财经资讯数据_灵砚智能14 小时前
基于全球经济类多源新闻的NLP情感分析与数据可视化(日间)2026年4月23日
大数据·人工智能·python·信息可视化·自然语言处理
财经资讯数据_灵砚智能15 小时前
基于全球经济类多源新闻的NLP情感分析与数据可视化(夜间-次晨)2026年4月22日
大数据·人工智能·python·信息可视化·自然语言处理
Hui_AI72017 小时前
保险条款NLP解析与知识图谱搭建:让AI准确理解保险产品的技术方案
开发语言·人工智能·python·算法·自然语言处理·开源·开源软件