使用 DL4J 训练中文词向量

使用 DL4J 训练中文词向量

目录

• 使用 [DL4J][01] 训练中文词向量
  • 1 预处理
  • 2 训练
  • 3 调用
  • 附录 - maven 依赖

1 预处理

对中文语料的预处理，主要包括：分词、去停用词以及一些根据实际场景制定的规则。

package ai.mole.test;

import org.ansj.domain.Term;
import org.ansj.splitWord.analysis.ToAnalysis;
import org.nlpcn.commons.lang.tire.domain.Forest;
import org.nlpcn.commons.lang.tire.library.Library;

import java.io.*;
import java.util.LinkedList;
import java.util.List;
import java.util.regex.Pattern;

public class Preprocess {
    private static final Pattern NUMERIC_PATTERN = Pattern.compile("^[.\d]+$");
    private static final Pattern ENGLISH_WORD_PATTERN = Pattern.compile("^[a-z]+$");

    public static void main(String[] args) {
        String inPath1 = "D:\MyData\XUGP3\Desktop\测试分词\test1.txt";
        String inPath2 = "D:\MyData\XUGP3\Desktop\测试分词\stop_words.txt";
        String outPath = "D:\MyData\XUGP3\Desktop\测试分词\result1.txt";
        String encoding = "utf-8";

        PrintWriter writer = null;
        Forest forest = null;
        try {
            writer = new PrintWriter(new OutputStreamWriter(new FileOutputStream(outPath), encoding));
            forest = Library.makeForest(Test.class.getResourceAsStream("/library/userLibrary.dic"));

            List<String> lineList = IOUtil.readLines(new FileInputStream(inPath1), encoding);
            List<String> stopWordList = IOUtil.readLines(new FileInputStream(inPath2), encoding);

            for (String line : lineList) {
                String[] cols = line.split("\t", -1);

                if (cols.length < 2) {
                    continue;
                }

                String text = cols[0].trim().toLowerCase() + " " + cols[1].trim().toLowerCase();

                // 分词
                List<Term> termList = ToAnalysis.parse(text, forest).getTerms();
                List<String> wordList = new LinkedList<>();
                for (Term term : termList) {
                    String word = term.getName();

                    if (word.length() < 2) {
                        continue;
                    }

                    if (stopWordList.contains(word)) {
                        continue;
                    }

                    if (isNumeric(word)) {
                        continue;
                    }

                    if (isEnglishWord(word)) {
                        continue;
                    }

                    wordList.add(word);
                }

                if (wordList.size() > 5) {
                    String outStr = listToLine(wordList);
                    writer.println(outStr);
                }
            }
        } catch (FileNotFoundException e) {
            System.out.println("The file does not exist or the path is not correct!!!");
            System.exit(-1);
        } catch (UnsupportedEncodingException e) {
            System.out.println("Does not support the current character set!!!");
        } catch (IOException e) {
            e.printStackTrace();
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            if (writer != null) {
                writer.close();
            }
        }
    }

    private static boolean isNumeric(String text) {
        return NUMERIC_PATTERN.matcher(text).matches();
    }

    private static boolean isEnglishWord(String text) {
        return ENGLISH_WORD_PATTERN.matcher(text).matches();
    }

    private static String listToLine(List<String> list) {
        StringBuilder sb = new StringBuilder();
        for (int i=0; i<list.size(); i++) {
            sb.append(list.get(i));
            if (i != list.size()-1) {
                sb.append(" ");
            }
        }
        return sb.toString();
    }
}

2 训练

训练的代码非常简单，可以直接看官网的教程，至于 word2vec 的原理可以看皮提果的博文。

package ai.mole.test;

import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
import org.deeplearning4j.models.word2vec.Word2Vec;
import org.deeplearning4j.text.sentenceiterator.BasicLineIterator;
import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;
import java.io.IOException;
import java.util.Collection;

public class TrainWord2VecModel {
    private static Logger log = LoggerFactory.getLogger(TrainWord2VecModel.class);

    public static void main(String[] args) throws IOException {
        String corpusPath = "/data/analyze/xgp/words.txt";
        String vectorsPath = "/data/analyze/xgp/word_vectors.txt";

        log.info("Start Training...");
        long st = System.currentTimeMillis();

        log.info("Load & vectorize sentences...");
        SentenceIterator iter = new BasicLineIterator(new File(corpusPath));
        TokenizerFactory t = new DefaultTokenizerFactory();
//        t.setTokenPreProcessor(new CommonPreprocessor());

        log.info("Building model...");
        Word2Vec vec = new Word2Vec.Builder()
                .minWordFrequency(50)
                .iterations(1)
                .epochs(100)
                .layerSize(500)
                .seed(42)
                .windowSize(5)
                .iterate(iter)
                .tokenizerFactory(t)
                .build();

        log.info("Fitting word2vec model...");
        vec.fit();

        log.info("Writing word vectors to text file...");
//        WordVectorSerializer.writeWord2VecModel(vec, vectorsPath);
        WordVectorSerializer.writeWordVectors(vec, vectorsPath);

        log.info("Closest words:");
        Collection<String> bydWordList = vec.wordsNearest("比亚迪", 10);
        Collection<String> changanWordList = vec.wordsNearest("长安", 10);
        System.out.print(bydWordList);
        System.out.println(changanWordList);

        log.info("10 words closest to '比亚迪': {}", bydWordList);
        log.info("10 words closest to '长安': {}", changanWordList);

        long et = System.currentTimeMillis();
        log.info("Training is completed, and the time taken is " + (et-st) + " ms.");
        System.out.println("Training is completed, and the time taken is " + (et-st) + " ms.");
    }
}

3 调用

调用训练好的词向量也非常简单，只需要调用 WordVectorSerializer 类的静态方法 readWord2VecModel 就可以了，提供的输入参数就是训练好的词向量路径。

Word2Vec word2Vec = WordVectorSerializer.readWord2VecModel("D:\MyData\XUGP3\Desktop\测试分词\vectors.txt");
Collection<String> bydWordList = word2Vec.wordsNearest("比亚迪", 10);
Collection<String> changanWordList = word2Vec.wordsNearest("长安", 10);
System.out.println(bydWordList);
System.out.println(changanWordList);

附录 - maven 依赖

<dependencies>
    <dependency>
        <groupId>org.apdplat</groupId>
        <artifactId>word</artifactId>
        <version>1.3</version>
    </dependency>

    <!-- ND4J backend. You need one in every DL4J project. Normally define artifactId as either "nd4j-native-platform" or "nd4j-cuda-7.5-platform" -->
    <dependency>
        <groupId>org.nd4j</groupId>
        <artifactId>${nd4j.backend}</artifactId>
        <version>${nd4j.version}</version>
    </dependency>

    <!-- Core DL4J functionality -->
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-core</artifactId>
        <version>${dl4j.version}</version>
    </dependency>

    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-nlp</artifactId>
        <version>${dl4j.version}</version>
    </dependency>

    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-zoo</artifactId>
        <version>${dl4j.version}</version>
    </dependency>

    <!-- deeplearning4j-ui is used for visualization: see http://deeplearning4j.org/visualization -->
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-ui_${scala.binary.version}</artifactId>
        <version>${dl4j.version}</version>
    </dependency>

    <!-- ParallelWrapper & ParallelInference live here -->
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-parallel-wrapper_${scala.binary.version}</artifactId>
        <version>${dl4j.version}</version>
    </dependency>

    <!-- Next 2: used for MapFileConversion Example. Note you need *both* together -->
    <dependency>
        <groupId>org.datavec</groupId>
        <artifactId>datavec-hadoop</artifactId>
        <version>${datavec.version}</version>
    </dependency>

    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-common</artifactId>
        <version>${hadoop.version}</version>
    </dependency>


    <!-- Arbiter - used for hyperparameter optimization (grid/random search) -->
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>arbiter-deeplearning4j</artifactId>
        <version>${arbiter.version}</version>
    </dependency>
    
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>arbiter-ui_2.11</artifactId>
        <version>${arbiter.version}</version>
    </dependency>

    <!-- datavec-data-codec: used only in video example for loading video data -->
    <dependency>
        <artifactId>datavec-data-codec</artifactId>
        <groupId>org.datavec</groupId>
        <version>${datavec.version}</version>
    </dependency>
</dependencies>

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