多国语言处理

开发平台：
Java

UnknowWord.cpp：源码内容
							//////////////////////////////////////////////////////////////////////
//ICTCLAS简介：计算所汉语词法分析系统ICTCLAS(Institute of Computing Technology, Chinese Lexical Analysis System)，
//             功能有：中文分词；词性标注；未登录词识别。
//             分词正确率高达97.58%(973专家评测结果)，
//             未登录词识别召回率均高于90%，其中中国人名的识别召回率接近98%;
//             处理速度为31.5Kbytes/s。
//著作权：  Copyright?2002-2005中科院计算所 职务著作权人：张华平 刘群
//遵循协议：自然语言处理开放资源许可证1.0
//Email: zhanghp@software.ict.ac.cn
//Homepage:www.nlp.org.cn;mtgroup.ict.ac.cn
/****************************************************************************
 *
 * Copyright (c) 2000, 2001 
 *     Machine Group
 *     Software Research Lab.
 *     Institute of Computing Tech.
 *     Chinese Academy of Sciences
 *     All rights reserved.
 *
 * This file is the confidential and proprietary property of 
 * Institute of Computing Tech. and the posession or use of this file requires 
 * a written license from the author.
 * Filename: UnknowWord.cpp
 * Abstract:
 *           implementation of the CUnknowWord class.
 * Author:   Kevin Zhang 
 *          (zhanghp@software.ict.ac.cn)
 * Date:     2002-4-23
 *
 * Notes:   Unknown words recognition based on Role-tagging
 *                
 ****************************************************************************/
#include "stdafx.h"
#include "UnknowWord.h"
#include <string.h>
#include <math.h>
//////////////////////////////////////////////////////////////////////
// Construction/Destruction
//////////////////////////////////////////////////////////////////////
CUnknowWord::CUnknowWord()
{
	m_sUnknownFlags[0]=0;
}
CUnknowWord::~CUnknowWord()
{
}
//Unknown word recognition
//pWordSegResult:word Segmentation result;graphOptimum: The optimized segmentation graph
//graphSeg: The original segmentation graph
bool CUnknowWord::Recognition(PWORD_RESULT pWordSegResult, CDynamicArray &graphOptimum,CSegGraph &graphSeg,CDictionary &dictCore)
{	
	int nStartPos=0,j=0,nAtomStart,nAtomEnd,nPOSOriginal;
	ELEMENT_TYPE dValue;
	m_roleTag.POSTagging(pWordSegResult,dictCore,m_dict);
	//Tag the segmentation with unknown recognition roles according the core dictionary and unknown recognition dictionary
	for(int i=0;i<m_roleTag.m_nUnknownIndex;i++)
	{
		while((unsigned int)j<graphSeg.m_nAtomCount&&nStartPos<m_roleTag.m_nUnknownWords[i][0])
		{
			nStartPos+=graphSeg.m_nAtomLength[j++];
		}
		nAtomStart=j;
		while((unsigned int)j<graphSeg.m_nAtomCount&&nStartPos<m_roleTag.m_nUnknownWords[i][1])
		{
			nStartPos+=graphSeg.m_nAtomLength[j++];
		}
		nAtomEnd=j;
		if(nAtomStart<nAtomEnd)
		{
			graphOptimum.GetElement(nAtomStart,nAtomEnd,&dValue,&nPOSOriginal);
			if(dValue>m_roleTag.m_dWordsPossibility[i])//Set the element with less frequency
				graphOptimum.SetElement(nAtomStart,nAtomEnd,m_roleTag.m_dWordsPossibility[i],m_nPOS,m_sUnknownFlags);
		}
	}
	return true;
}
//Load unknown recognition dictionary
//Load context
//type: Unknown words type (including person,place,transliterion and so on)
bool CUnknowWord::Configure(char *sConfigFile,enum TAG_TYPE type)
{
	char sFilename[100];
	
	//Load the unknown recognition dictionary
	strcpy(sFilename,sConfigFile);
	strcat(sFilename,".dct");
	m_dict.Load(sFilename);
	
	//Load the unknown recognition context
	strcpy(sFilename,sConfigFile);
	strcat(sFilename,".ctx");
	m_roleTag.LoadContext(sFilename);
	
	//Set the tagging type
	m_roleTag.SetTagType(type);
	switch(type)
	{
		case TT_PERSON:
		case TT_TRANS_PERSON://Set the special flag for transliterations
			m_nPOS=-28274;//-'n'*256-'r';
			strcpy(m_sUnknownFlags,"未##人");
			break;
		case TT_PLACE:
			m_nPOS=-28275;//-'n'*256-'s';
			strcpy(m_sUnknownFlags,"未##地");
			break;
		default :
			m_nPOS=0;
			break;
	}
	return true;
}
//Judge whether the name is a given name
bool CUnknowWord::IsGivenName(char *sName)
{
	char sFirstChar[3],sSecondChar[3];
	ELEMENT_TYPE dGivenNamePossibility=0,dSingleNamePossibility=0;
	if(strlen(sName)!=4)
		return false;	
	
	strncpy(sFirstChar,sName,2);
	sFirstChar[2]=0;
	strncpy(sSecondChar,sName+2,2);
	sSecondChar[2]=0;
	//The possibility of P(Wi|Ti)
	dGivenNamePossibility+=log((double)m_dict.GetFrequency(sFirstChar,2)+1.0)-log(m_roleTag.m_context.GetFrequency(0,2)+1.0);
	dGivenNamePossibility+=log((double)m_dict.GetFrequency(sSecondChar,3)+1.0)-log(m_roleTag.m_context.GetFrequency(0,3)+1.0);
	//The possibility of conversion from 2 to 3
	dGivenNamePossibility+=log(m_roleTag.m_context.GetContextPossibility(0,2,3)+1.0)-log(m_roleTag.m_context.GetFrequency(0,2)+1.0);
	
	//The possibility of P(Wi|Ti)
	dSingleNamePossibility+=log((double)m_dict.GetFrequency(sFirstChar,1)+1.0)-log(m_roleTag.m_context.GetFrequency(0,1)+1.0);
	dSingleNamePossibility+=log((double)m_dict.GetFrequency(sSecondChar,4)+1.0)-log(m_roleTag.m_context.GetFrequency(0,4)+1.0);
	//The possibility of conversion from 1 to 4
	dSingleNamePossibility+=log(m_roleTag.m_context.GetContextPossibility(0,1,4)+1.0)-log(m_roleTag.m_context.GetFrequency(0,1)+1.0);
	if(dSingleNamePossibility>=dGivenNamePossibility)//张震||m_dict.GetFrequency(sFirstChar,1)/m_dict.GetFrequency(sFirstChar,2)>=10
	//The possibility being a single given name is more than being a 2-char given name
		return false;
	return true;
}

						