数据模型

Question

我正在寻找一个Java库来从一个文本块中提取关键字。

该过程应如下：

停止清洁字词 - ＆gt;词干 - ＆gt;根据英语语言学统计信息搜索关键词 - 这意味着如果一个单词在文本中出现的次数多于在英语中出现的概率，那么它就是一个关键词候选者。

是否有执行此任务的库？

Answer 1

以下是使用Apache Lucene的可能解决方案。我没有使用最后一个版本，而是3.6.2 one，因为这是我最了解的版本。除了/lucene-core-x.x.x.jar之外，不要忘记将下载的存档中的/contrib/analyzers/common/lucene-analyzers-x.x.x.jar添加到项目中：它包含特定于语言的分析器（特别是您的英语分析器）。

请注意，这将仅根据各自的词干查找输入文本词的频率。将这些频率与英语语言统计数据进行比较后，（this answer可能会有所帮助）。

数据模型

一个词干的一个关键词。不同的单词可能具有相同的词干，因此terms集合。每次找到新术语时，关键字频率都会递增（即使已经找到它 - 一个集合会自动删除重复项）。

public class Keyword implements Comparable<Keyword> {

  private final String stem;
  private final Set<String> terms = new HashSet<String>();
  private int frequency = 0;

  public Keyword(String stem) {
    this.stem = stem;
  }

  public void add(String term) {
    terms.add(term);
    frequency++;
  }

  @Override
  public int compareTo(Keyword o) {
    // descending order
    return Integer.valueOf(o.frequency).compareTo(frequency);
  }

  @Override
  public boolean equals(Object obj) {
    if (this == obj) {
      return true;
    } else if (!(obj instanceof Keyword)) {
      return false;
    } else {
      return stem.equals(((Keyword) obj).stem);
    }
  }

  @Override
  public int hashCode() {
    return Arrays.hashCode(new Object[] { stem });
  }

  public String getStem() {
    return stem;
  }

  public Set<String> getTerms() {
    return terms;
  }

  public int getFrequency() {
    return frequency;
  }

}

实用程序

要说一句话：

public static String stem(String term) throws IOException {

  TokenStream tokenStream = null;
  try {

    // tokenize
    tokenStream = new ClassicTokenizer(Version.LUCENE_36, new StringReader(term));
    // stem
    tokenStream = new PorterStemFilter(tokenStream);

    // add each token in a set, so that duplicates are removed
    Set<String> stems = new HashSet<String>();
    CharTermAttribute token = tokenStream.getAttribute(CharTermAttribute.class);
    tokenStream.reset();
    while (tokenStream.incrementToken()) {
      stems.add(token.toString());
    }

    // if no stem or 2+ stems have been found, return null
    if (stems.size() != 1) {
      return null;
    }
    String stem = stems.iterator().next();
    // if the stem has non-alphanumerical chars, return null
    if (!stem.matches("[a-zA-Z0-9-]+")) {
      return null;
    }

    return stem;

  } finally {
    if (tokenStream != null) {
      tokenStream.close();
    }
  }

}

要搜索集合（将由潜在关键字列表使用）：

public static <T> T find(Collection<T> collection, T example) {
  for (T element : collection) {
    if (element.equals(example)) {
      return element;
    }
  }
  collection.add(example);
  return example;
}

核心

这是主要的输入法：

public static List<Keyword> guessFromString(String input) throws IOException {

  TokenStream tokenStream = null;
  try {

    // hack to keep dashed words (e.g. "non-specific" rather than "non" and "specific")
    input = input.replaceAll("-+", "-0");
    // replace any punctuation char but apostrophes and dashes by a space
    input = input.replaceAll("[\\p{Punct}&&[^'-]]+", " ");
    // replace most common english contractions
    input = input.replaceAll("(?:'(?:[tdsm]|[vr]e|ll))+\\b", "");

    // tokenize input
    tokenStream = new ClassicTokenizer(Version.LUCENE_36, new StringReader(input));
    // to lowercase
    tokenStream = new LowerCaseFilter(Version.LUCENE_36, tokenStream);
    // remove dots from acronyms (and "'s" but already done manually above)
    tokenStream = new ClassicFilter(tokenStream);
    // convert any char to ASCII
    tokenStream = new ASCIIFoldingFilter(tokenStream);
    // remove english stop words
    tokenStream = new StopFilter(Version.LUCENE_36, tokenStream, EnglishAnalyzer.getDefaultStopSet());

    List<Keyword> keywords = new LinkedList<Keyword>();
    CharTermAttribute token = tokenStream.getAttribute(CharTermAttribute.class);
    tokenStream.reset();
    while (tokenStream.incrementToken()) {
      String term = token.toString();
      // stem each term
      String stem = stem(term);
      if (stem != null) {
        // create the keyword or get the existing one if any
        Keyword keyword = find(keywords, new Keyword(stem.replaceAll("-0", "-")));
        // add its corresponding initial token
        keyword.add(term.replaceAll("-0", "-"));
      }
    }

    // reverse sort by frequency
    Collections.sort(keywords);

    return keywords;

  } finally {
    if (tokenStream != null) {
      tokenStream.close();
    }
  }

}

实施例

使用Java wikipedia article introduction part上的guessFromString方法，以下是找到的前10个最常用的关键字（即词干）：

java         x12    [java]
compil       x5     [compiled, compiler, compilers]
sun          x5     [sun]
develop      x4     [developed, developers]
languag      x3     [languages, language]
implement    x3     [implementation, implementations]
applic       x3     [application, applications]
run          x3     [run]
origin       x3     [originally, original]
gnu          x3     [gnu]

通过获取terms集（在上面的示例中显示在括号[...]之间），迭代输出列表以了解每个词干的原始找到的词。

下一步是什么

将词干频率/频率和比率与英语统计数据进行比较，如果您管理它，请让我保持循环：我可能也非常感兴趣:)

Answer 2

上面提出的更新且随时可用的代码版本此代码与Apache Lucene 5.x ... 6.x。

兼容

CardKeyword 类：

import java.util.HashSet;
import java.util.Set;

/**
 * Keyword card with stem form, terms dictionary and frequency rank
 */
class CardKeyword implements Comparable<CardKeyword> {

    /**
     * Stem form of the keyword
     */
    private final String stem;

    /**
     * Terms dictionary
     */
    private final Set<String> terms = new HashSet<>();

    /**
     * Frequency rank
     */
    private int frequency;

    /**
     * Build keyword card with stem form
     *
     * @param stem
     */
    public CardKeyword(String stem) {
        this.stem = stem;
    }

    /**
     * Add term to the dictionary and update its frequency rank
     *
     * @param term
     */
    public void add(String term) {
        this.terms.add(term);
        this.frequency++;
    }

    /**
     * Compare two keywords by frequency rank
     *
     * @param keyword
     * @return int, which contains comparison results
     */
    @Override
    public int compareTo(CardKeyword keyword) {
        return Integer.valueOf(keyword.frequency).compareTo(this.frequency);
    }

    /**
     * Get stem's hashcode
     *
     * @return int, which contains stem's hashcode
     */
    @Override
    public int hashCode() {
        return this.getStem().hashCode();
    }

    /**
     * Check if two stems are equal
     *
     * @param o
     * @return boolean, true if two stems are equal
     */
    @Override
    public boolean equals(Object o) {

        if (this == o) return true;

        if (!(o instanceof CardKeyword)) return false;

        CardKeyword that = (CardKeyword) o;

        return this.getStem().equals(that.getStem());
    }

    /**
     * Get stem form of keyword
     *
     * @return String, which contains getStemForm form
     */
    public String getStem() {
        return this.stem;
    }

    /**
     * Get terms dictionary of the stem
     *
     * @return Set<String>, which contains set of terms of the getStemForm
     */
    public Set<String> getTerms() {
        return this.terms;
    }

    /**
     * Get stem frequency rank
     *
     * @return int, which contains getStemForm frequency
     */
    public int getFrequency() {
        return this.frequency;
    }
}

KeywordsExtractor 类：

import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.core.LowerCaseFilter;
import org.apache.lucene.analysis.core.StopFilter;
import org.apache.lucene.analysis.en.EnglishAnalyzer;
import org.apache.lucene.analysis.en.PorterStemFilter;
import org.apache.lucene.analysis.miscellaneous.ASCIIFoldingFilter;
import org.apache.lucene.analysis.standard.ClassicFilter;
import org.apache.lucene.analysis.standard.StandardTokenizer;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;

import java.io.IOException;
import java.io.StringReader;
import java.util.*;

/**
 * Keywords extractor functionality handler
 */
class KeywordsExtractor {

    /**
     * Get list of keywords with stem form, frequency rank, and terms dictionary
     *
     * @param fullText
     * @return List<CardKeyword>, which contains keywords cards
     * @throws IOException
     */
    static List<CardKeyword> getKeywordsList(String fullText) throws IOException {

        TokenStream tokenStream = null;

        try {
            // treat the dashed words, don't let separate them during the processing
            fullText = fullText.replaceAll("-+", "-0");

            // replace any punctuation char but apostrophes and dashes with a space
            fullText = fullText.replaceAll("[\\p{Punct}&&[^'-]]+", " ");

            // replace most common English contractions
            fullText = fullText.replaceAll("(?:'(?:[tdsm]|[vr]e|ll))+\\b", "");

            StandardTokenizer stdToken = new StandardTokenizer();
            stdToken.setReader(new StringReader(fullText));

            tokenStream = new StopFilter(new ASCIIFoldingFilter(new ClassicFilter(new LowerCaseFilter(stdToken))), EnglishAnalyzer.getDefaultStopSet());
            tokenStream.reset();

            List<CardKeyword> cardKeywords = new LinkedList<>();

            CharTermAttribute token = tokenStream.getAttribute(CharTermAttribute.class);

            while (tokenStream.incrementToken()) {

                String term = token.toString();
                String stem = getStemForm(term);

                if (stem != null) {
                    CardKeyword cardKeyword = find(cardKeywords, new CardKeyword(stem.replaceAll("-0", "-")));
                    // treat the dashed words back, let look them pretty
                    cardKeyword.add(term.replaceAll("-0", "-"));
                }
            }

            // reverse sort by frequency
            Collections.sort(cardKeywords);

            return cardKeywords;
        } finally {
            if (tokenStream != null) {
                try {
                    tokenStream.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }
    }

    /**
     * Get stem form of the term
     *
     * @param term
     * @return String, which contains the stemmed form of the term
     * @throws IOException
     */
    private static String getStemForm(String term) throws IOException {

        TokenStream tokenStream = null;

        try {
            StandardTokenizer stdToken = new StandardTokenizer();
            stdToken.setReader(new StringReader(term));

            tokenStream = new PorterStemFilter(stdToken);
            tokenStream.reset();

            // eliminate duplicate tokens by adding them to a set
            Set<String> stems = new HashSet<>();

            CharTermAttribute token = tokenStream.getAttribute(CharTermAttribute.class);

            while (tokenStream.incrementToken()) {
                stems.add(token.toString());
            }

            // if stem form was not found or more than 2 stems have been found, return null
            if (stems.size() != 1) {
                return null;
            }

            String stem = stems.iterator().next();

            // if the stem form has non-alphanumerical chars, return null
            if (!stem.matches("[a-zA-Z0-9-]+")) {
                return null;
            }

            return stem;
        } finally {
            if (tokenStream != null) {
                try {
                    tokenStream.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }
    }

    /**
     * Find sample in collection
     *
     * @param collection
     * @param sample
     * @param <T>
     * @return <T> T, which contains the found object within collection if exists, otherwise the initially searched object
     */
    private static <T> T find(Collection<T> collection, T sample) {

        for (T element : collection) {
            if (element.equals(sample)) {
                return element;
            }
        }

        collection.add(sample);

        return sample;
    }
}

功能调用：

String text = "…";
List<CardKeyword> keywordsList = KeywordsExtractor.getKeywordsList(text);

Answer 3

基于RAKE算法和opennlp models库包装的rapidrake-java的相对简单的方法。

import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStream;

import org.apache.commons.io.IOUtils;

import io.github.crew102.rapidrake.model.RakeParams;
import io.github.crew102.rapidrake.model.Result;

public class KeywordExtractor {

    private static String delims = "[-,.?():;\"!/]";
    private static String posUrl = "model-bin/en-pos-maxent.bin";
    private static String sentUrl = "model-bin/en-sent.bin";

    public static void main(String[] args) throws IOException {

        InputStream stream = new FileInputStream("res/stopwords-terrier.txt");
        String[] stopWords = IOUtils.readLines(stream, "UTF-8").stream().toArray(String[]::new);
        String[] stopPOS = {"VBD"};
        RakeParams params = new RakeParams(stopWords, stopPOS, 0, true, delims);
        RakeAlgorithm rakeAlg = new RakeAlgorithm(params, posUrl, sentUrl);
        Result aRes = rakeAlg.rake("I'm looking for a Java library to extract keywords from a block of text.");
        System.out.println(aRes);
        // OUTPUT:
        // [looking (1), java library (4), extract keywords (4), block (1), text (1)]
    }
}

从示例输出中可以看到，您将获得一张带有相对权重的关键字图。

如https://github.com/crew102/rapidrake-java所述，您需要从opennlp download page下载文件en-pos-maxent.bin和model-bin/en-sent.bin。将它们放在项目根目录下的model-bin文件夹中（如果使用maven项目结构，则必须是src文件夹的同级文件）。停用词文件可取自https://github.com/terrier-org/terrier-desktop/blob/master/share/stopword-list.txt。

用于从输入文本中提取关键字的Java库

3 个答案:

数据模型

实用程序

核心

实施例

下一步是什么