根据单词中字符的位置，确定单词为句中第几个

<pre name="code" class="java">public static void main(String[] args) {      // TODO Auto-generated method stub      String text = "There was no difference in the effects of AzaC versus AzadC, as both increased the IRF-4 mRNA level"                      + "in CML-T1 cells as well (data not shown).";            int startIndex = 89;      int endIndex = 99;              String str = text.substring(0,startIndex);               //冒号和逗号      int count = str.split("\\s{1,}").                                 // "\\s{1,}" 表示一个以上的空白字符      String str2 = text.substring(startIndex,endIndex);      int count2 = str2.split("\\s{1,}").length;      for(int i=0 ; i<count2 ; i++){        System.out.println(count+i-1);       }}

其中要确定位置的是”mRNA level“是句中的第几个单词，我们所知道的信息是m字符在句中的位置是89，level后面空格的位置是99.

以上方式有个缺点：就是遇到标点符号和括号的时候，不能将之认为是一个“单词” ，于是进行改进后的算法如下，以下运用了斯坦福的分词器

package com.a2Process;import java.util.ArrayList;import java.util.List;import java.util.Properties;import java.util.regex.Matcher;import java.util.regex.Pattern;import edu.stanford.nlp.ling.CoreLabel;import edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation;import edu.stanford.nlp.ling.CoreAnnotations.TextAnnotation;import edu.stanford.nlp.ling.CoreAnnotations.TokensAnnotation;import edu.stanford.nlp.pipeline.Annotation;import edu.stanford.nlp.pipeline.StanfordCoreNLP;import edu.stanford.nlp.util.CoreMap;public class FindPosition2 {public static void main(String[] args) {// TODO Auto-generated method stub String text = "To define the requirements of Foxp3 with respect to inhibition of NF-kappaB-dependent transcription, we utilized a mutant of Foxp3 acking the FKH domain (Figure 2A) [16], similar to the scurfy mutant Foxp3 of mice, and a mutant Foxp3 protein from a patient with IPEX [4,11,14,17].";             int startIndex = 143;     int endIndex = 153;        String str = text.substring(0,startIndex-1);          int preCountWord = wordNum(str);          String selectedStr = text.substring(startIndex-1,endIndex);     int selectedCountWord = wordNum(selectedStr);          for(int i=0 ; i<selectedCountWord; i++){     System.out.println(preCountWord+i);     }         }/** * 获取一个字符串里包含多少个单词 */public static int wordNum(String str){Properties props = new Properties();props.put("annotators", "tokenize, ssplit");StanfordCoreNLP pipeline = new StanfordCoreNLP(props);     Annotation document = new Annotation(str);    pipeline.annotate(document);List<CoreMap> sentences = document.get(SentencesAnnotation.class);int wordnum = 0;for(CoreMap sentence : sentences){            for(CoreLabel token : sentence.get(TokensAnnotation.class)){              wordnum++;             String word = token.get(TextAnnotation.class);           System.out.println(word);    }    }return wordnum;}}