ubuntu(linux)环境下C++调用LTP

环境：ubuntu(linux)+gcc+Cmake

LTP源码：https://github.com/HIT-SCIR/ltp/releases
LTP模型：https://pan.baidu.com/share/link?shareid=1988562907&uk=2738088569#list/path=%2Fltp-models

下载ltp源码ltp-3.4.0解压并进入

LTP的C++源码编译及测试

./configure
make

编译成功后，会在bin目录下生成一下二进制文件

程序名	说明
cws_cmdline	分词模块命令行程序
pos_cmdline	词性标注模块命令行程序
ner_cmdline	命名实体识别模块命令行程序
par_cmdline	依存句法分析模块命令行程序
在 examples 目录下有C++源码和Makefile文件
使用pos_cmdline完成词性标注测试

$ cat input
这 是 测试 样本 ，中文 句法 分析 。
$ cat input | ./bin/examples/pos_cmdline --postagger-model ./ltp_model/pos.model
TRACE: Model is loaded
TRACE: Running 1 thread(s)
WARN: Cann't open file! use stdin instead.
这_r    是_v    测试_v  样本_n  ，中文_nz       句法_n  分析。_v
TRACE: consume 0.162231 seconds.

在Linux下使用动态库生成可执行程序

（1）分词cws.cpp代码如下

#include <iostream>
#include <string>
#include "ltp/segment_dll.h"
 
int main(int argc, char * argv[])
{
  if (argc < 2)    //命令行参数，没有分词模型的情况下输出
  {
    std::cerr << "cws [model path] [lexicon_file]" << std::endl;
    return 1;
  }
  void * engine = 0;     //声明一个指向模型的指针
  if (argc == 2)     //第一个命令行参数，为分词模型
  {
    engine = segmentor_create_segmentor(argv[1]);  //分词接口，初始化分词器
  } 
  else if (argc == 3) //第二个命令行参数，可以外加词典文件
  {
    engine = segmentor_create_segmentor(argv[1], argv[2]); //分词接口，初始化分词器
  }
  if (!engine) 
  {
    return -1;
  }
  std::vector<std::string> words;   //将分词结果存入vector中
  //分词的文本
  const char * suite[2] = {
    "What's wrong with you? 别灰心! http://t.cn/zQz0Rn", "台北真的是天子骄子吗？",};
  
  for (int i = 0; i < 2; ++ i) {
    words.clear();
    int len = segmentor_segment(engine, suite[i], words);  //分词接口，对句子分词。
    for (int i = 0; i < len; ++ i) {
      std::cout << words[i];
      if (i+1 == len) std::cout <<std::endl;
      else std::cout<< "|";
    }
  }
  segmentor_release_segmentor(engine);   //分词接口，释放分词器
  return 0;
}

（2）生成cws可执行程序

将下载的LTP置于 ltp-project 目录下，编译命令如下

$ g++ -o cws cws.cpp -I ../include/ -I ../thirdparty/boost/include/ -Wl,-dn -L ../lib/ -lsegmentor -lboost_regex -Wl,-dy

运行生成的可执行程序

$ cws ../ltp_model/cws.model

运行结果如下：

• What's|wrong|with|you|?|别|灰心|!|http://t.cn/zQz0Rn
• 台北|真|的|是|天子骄子|吗|？

构建基于LTP的句法分析类Parsing

#include <iostream>
#include <vector>
#include "ltp/segment_dll.h"
#include "ltp/postag_dll.h"
#include "ltp/parser_dll.h"
using namespace std;
 
//构建LTP句法分析类
class Parsing
{
    public:
		void* cws_engine = 0;
		void* pos_engine = 0;
		void* par_engine = 0;
		vector<string> words;
		vector<string> postags;
		vector<int> heads;
		vector<string> deprels;
    public:
		void get_models(char* cws, char* pos, char* par);
		void get_words(string str);
		void get_postags(string str);
		void get_parsing(string str);
		void release_model();
 
};
//加载模型文件
void Parsing::get_models(char* cws,char* pos,char* par)
{
	cws_engine = segmentor_create_segmentor(cws);
	pos_engine = postagger_create_postagger(pos);
	par_engine = parser_create_parser(par);
}
//分词
void Parsing::get_words(string str)
{
	words.clear();
	segmentor_segment(cws_engine, str, words);
}
//词性标注
void Parsing::get_postags(string str)
{
	words.clear();
	postags.clear();
	segmentor_segment(cws_engine, str, words);
	postagger_postag(pos_engine, words, postags);
}
//句法分析 
void Parsing::get_parsing(string str)
{
	words.clear();
	postags.clear();
	heads.clear();
	deprels.clear();
	segmentor_segment(cws_engine, str, words);
	postagger_postag(pos_engine, words, postags);
	parser_parse(par_engine, words, postags, heads, deprels);
}
//释放模型
void Parsing::release_model()
{
	segmentor_release_segmentor(cws_engine);
	postagger_release_postagger(pos_engine);
	parser_release_parser(par_engine);
}
 
int main(int argc, char * argv[]) 
{
	Parsing pars;
	//pars.get_models("/mnt/f/ltp_project/ltp_model/cws.model","/mnt/f/ltp_project/ltp_model/pos.model","/mnt/f/ltp_project//ltp_model/parser.model");
	pars.get_models(argv[1],argv[2],argv[3]);
	//cout<<"测试代码"<<endl;
	pars.get_parsing("Welcome to my blog!");
	cout<<pars.words.size()<<endl;
	for (int i = 0; i < pars.words.size(); i++)
	{
		cout << pars.words[i]<<'t'<<pars.postags[i]<<'t'<<pars.heads[i]<<'t'<<pars.deprels[i]<< endl;
		//cout << pars.postags[i] << endl;
		//cout << pars.heads[i] << endl;
		//cout << pars.deprels[i] << endl;
	}
	pars.release_model();
	return 0;
}

使用Parsing类实现句法分析

生成Parsing可执行程序

$ g++ -o parsing parsing.cpp -I ../include/ -I ../thirdparty/boost/include/ -Wl,-dn -L ../lib/ -lsegmentor -lpostagger -lparser -lboost_regex -Wl,-dy

运行Parsing可执行程序

$ ./parsing ../ltp_model/cws.model ../ltp_model/pos.model ../ltp_model/parser.model

运行结果如下