问题：
我试验了一下文章中提到的 stemming 和 lemmatization
- 将单词缩减为词根形式，如“cars”到“car”等。这种操作称为：stemming。
- 将单词转变为词根形式，如“drove”到“drive”等。这种操作称为：lemmatization。
试验没有成功
代码如下：
| public class TestNorms {
public void createIndex() throws IOException {
Directory d = new SimpleFSDirectory(new File("d:/falconTest/lucene3/norms"));
IndexWriter writer = new IndexWriter(d, new StandardAnalyzer(Version.LUCENE_30),
true, IndexWriter.MaxFieldLength.UNLIMITED);
Field field = new Field("desc", "", Field.Store.YES, Field.Index.ANALYZED);
Document doc = new Document();
field.setValue("Hello students was drive");
doc.add(field);
writer.addDocument(doc);
writer.optimize();
writer.close();
}
public void search() throws IOException {
Directory d = new SimpleFSDirectory(new File("d:/falconTest/lucene3/norms"));
IndexReader reader = IndexReader.open(d);
IndexSearcher searcher = new IndexSearcher(reader);
TopDocs docs = searcher.search(new TermQuery(new Term("desc","drove")), 10);
System.out.println(docs.totalHits);
}
public static void main(String[] args) throws IOException {
TestNorms test= new TestNorms();
test.createIndex();
test.search();
}
} |
不管是单复数，还是单词的变化，都是没有体现的
不知道是不是分词器的原因？
回答：
的确是分词器的问题，StandardAnalyzer并不能进行stemming和lemmatization，因而不能够区分单复数和词型。
文章中讲述的是全文检索的基本原理，理解了他，有利于更好的理解Lucene，但不代表Lucene是完全按照此基本流程进行的。
(1) 有关stemming
作为stemming，一个著名的算法是The Porter Stemming Algorithm，其主页为http://tartarus.org/~martin/PorterStemmer/，也可查看其论文http://tartarus.org/~martin/PorterStemmer/def.txt。
通过以下网页可以进行简单的测试：Porter's Stemming Algorithm Online[http://facweb.cs.depaul.edu/mobasher/classes/csc575/porter.html]
cars –> car
driving –> drive
tokenization –> token
然而
drove –> drove
可见stemming是通过规则缩减为词根的，而不能识别词型的变化。
在最新的Lucene 3.0中，已经有了PorterStemFilter这个类来实现上述算法，只可惜没有Analyzer向匹配，不过不要紧，我们可以简单实现：
| public class PorterStemAnalyzer extends Analyzer
{
@Override
public TokenStream tokenStream(String fieldName, Reader reader) {
return new PorterStemFilter(new LowerCaseTokenizer(reader));
}
} |
把此分词器用在你的程序中，就能够识别单复数和规则的词型变化了。
| public void createIndex() throws IOException {
Directory d = new SimpleFSDirectory(new File("d:/falconTest/lucene3/norms"));
IndexWriter writer = new IndexWriter(d, new PorterStemAnalyzer(), true, IndexWriter.MaxFieldLength.UNLIMITED); Field field = new Field("desc", "", Field.Store.YES, Field.Index.ANALYZED);
Document doc = new Document();
field.setValue("Hello students was driving cars professionally");
doc.add(field); writer.addDocument(doc);
writer.optimize();
writer.close();
} public void search() throws IOException {
Directory d = new SimpleFSDirectory(new File("d:/falconTest/lucene3/norms"));
IndexReader reader = IndexReader.open(d);
IndexSearcher searcher = new IndexSearcher(reader);
TopDocs docs = searcher.search(new TermQuery(new Term("desc", "car")), 10);
System.out.println(docs.totalHits);
docs = searcher.search(new TermQuery(new Term("desc", "drive")), 10);
System.out.println(docs.totalHits);
docs = searcher.search(new TermQuery(new Term("desc", "profession")), 10);
System.out.println(docs.totalHits);
} |
(2) 有关lemmatization
至于lemmatization，一般是有字典的，方能够由"drove"对应到"drive".
在网上搜了一下，找到European languages lemmatizer[http://lemmatizer.org/]，只不过是在linux下面C++开发的，有兴趣可以试验一下。
首先按照网站的说明下载，编译，安装：
| libMAFSA is the core of the lemmatizer. All other libraries depend on it. Download the last version from the following page, unpack it and compile: # tar xzf libMAFSA-0.2.tar.gz
# cd libMAFSA-0.2/
# cmake .
# make
# sudo make install After this you should install libturglem. You can download it at the same place. # tar xzf libturglem-0.2.tar.gz
# cd libturglem-0.2
# cmake .
# make
# sudo make install Next you should install english dictionaries with some additional features to work with. # tar xzf turglem-english-0.2.tar.gz
# cd turglem-english-0.2
# cmake .
# make
# sudo make install |
安装完毕后：
- /usr/local/include/turglem是头文件，用于编译自己编写的代码
- /usr/local/share/turglem/english是字典文件，其中lemmas.xml中我们可以看到"drove"和"drive"的对应，"was"和"be"的对应。
- /usr/local/lib中的libMAFSA.a libturglem.a libturglem-english.a libtxml.a是用于生成应用程序的静态库
| <l id="DRIVE" p="6" /> <l id="DROVE" p="6" /> <l id="DRIVING" p="6" /> |
在turglem-english-0.2目录下有例子测试程序test_utf8.cpp
| #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>
#include <turglem/lemmatizer.h>
#include <turglem/lemmatizer.hpp>
#include <turglem/english/charset_adapters.hpp> int main(int argc, char **argv)
{
char in_s_buf[1024];
char *nl_ptr; tl::lemmatizer lem; if(argc != 4)
{
printf("Usage: %s words.dic predict.dic flexias.bin\n", argv[0]);
return -1;
} lem.load_lemmatizer(argv[1], argv[3], argv[2]); while (!feof(stdin))
{
fgets(in_s_buf, 1024, stdin);
nl_ptr = strchr(in_s_buf, '\n');
if (nl_ptr) *nl_ptr = 0;
nl_ptr = strchr(in_s_buf, '\r');
if (nl_ptr) *nl_ptr = 0; if (in_s_buf[0])
{
printf("processing %s\n", in_s_buf);
tl::lem_result pars;
size_t pcnt = lem.lemmatize<english_utf8_adapter>(in_s_buf, pars);
printf("%d\n", pcnt);
for (size_t i = 0; i < pcnt; i++)
{
std::string s;
u_int32_t src_form = lem.get_src_form(pars, i);
s = lem.get_text<english_utf8_adapter>(pars, i, 0);
printf("PARADIGM %d: normal form '%s'\n", (unsigned int)i, s.c_str());
printf("\tpart of speech:%d\n", lem.get_part_of_speech(pars, (unsigned int)i, src_form));
}
}
} return 0;
} |
编译此文件，并且链接静态库：注意链接顺序，否则可能出错。
| g++ -g -o output test_utf8.cpp -L/usr/local/lib/ -lturglem-english -lturglem -lMAFSA –ltxml |
运行编译好的程序：
| ./output /usr/local/share/turglem/english/dict_english.auto /usr/local/share/turglem/english/prediction_english.auto /usr/local/share/turglem/english/paradigms_english.bin |
做测试，虽然对其机制尚不甚了解，但是可以看到lemmatization的作用：
| drove
processing drove
3
PARADIGM 0: normal form 'DROVE'
part of speech:0
PARADIGM 1: normal form 'DROVE'
part of speech:2
PARADIGM 2: normal form 'DRIVE'
part of speech:2 was
processing was
3
PARADIGM 0: normal form 'BE'
part of speech:3
PARADIGM 1: normal form 'BE'
part of speech:3
PARADIGM 2: normal form 'BE'
part of speech:3 |
