Package org.apache.lucene.search.spell

Class DirectSpellChecker

java.lang.Object
org.apache.lucene.search.spell.DirectSpellChecker
public class DirectSpellChecker extends Object
Simple automaton-based spellchecker.

Candidates are presented directly from the term dictionary, based on Levenshtein distance. This is an alternative to SpellChecker if you are using an edit-distance-like metric such as Levenshtein or JaroWinklerDistance.

A practical benefit of this spellchecker is that it requires no additional datastructures (neither in RAM nor on disk) to do its work.

See Also:

  • Field Details

    • INTERNAL_LEVENSHTEIN

      public static final StringDistance INTERNAL_LEVENSHTEIN
      The default StringDistance, Damerau-Levenshtein distance implemented internally via LevenshteinAutomata.

      Note: this is the fastest distance metric, because Damerau-Levenshtein is used to draw candidates from the term dictionary: this just re-uses the scoring.

  • Constructor Details

    • DirectSpellChecker

      public DirectSpellChecker()
      Creates a DirectSpellChecker with default configuration values

  • Method Details

    • getMaxEdits

      public int getMaxEdits()
      Get the maximum number of Levenshtein edit-distances to draw candidate terms from.

    • setMaxEdits

      public void setMaxEdits(int maxEdits)
      Sets the maximum number of Levenshtein edit-distances to draw candidate terms from. This value can be 1 or 2. The default is 2.

      Note: a large number of spelling errors occur with an edit distance of 1, by setting this value to 1 you can increase both performance and precision at the cost of recall.

    • getMinPrefix

      public int getMinPrefix()
      Get the minimal number of characters that must match exactly

    • setMinPrefix

      public void setMinPrefix(int minPrefix)
      Sets the minimal number of initial characters (default: 1) that must match exactly.

      This can improve both performance and accuracy of results, as misspellings are commonly not the first character.

    • getMaxInspections

      public int getMaxInspections()
      Get the maximum number of top-N inspections per suggestion

    • setMaxInspections

      public void setMaxInspections(int maxInspections)
      Set the maximum number of top-N inspections (default: 5) per suggestion.

      Increasing this number can improve the accuracy of results, at the cost of performance.

    • getAccuracy

      public float getAccuracy()
      Get the minimal accuracy from the StringDistance for a match

    • setAccuracy

      public void setAccuracy(float accuracy)
      Set the minimal accuracy required (default: 0.5f) from a StringDistance for a suggestion match.

    • getThresholdFrequency

      public float getThresholdFrequency()
      Get the minimal threshold of documents a term must appear for a match

    • setThresholdFrequency

      public void setThresholdFrequency(float thresholdFrequency)
      Set the minimal threshold of documents a term must appear for a match.

      This can improve quality by only suggesting high-frequency terms. Note that very high values might decrease performance slightly, by forcing the spellchecker to draw more candidates from the term dictionary, but a practical value such as 1 can be very useful towards improving quality.

      This can be specified as a relative percentage of documents such as 0.5f, or it can be specified as an absolute whole document frequency, such as 4f. Absolute document frequencies may not be fractional.

    • getMinQueryLength

      public int getMinQueryLength()
      Get the minimum length of a query term needed to return suggestions

    • setMinQueryLength

      public void setMinQueryLength(int minQueryLength)
      Set the minimum length of a query term (default: 4) needed to return suggestions.

      Very short query terms will often cause only bad suggestions with any distance metric.

    • getMaxQueryFrequency

      public float getMaxQueryFrequency()
      Get the maximum threshold of documents a query term can appear in order to provide suggestions.

    • setMaxQueryFrequency

      public void setMaxQueryFrequency(float maxQueryFrequency)
      Set the maximum threshold (default: 0.01f) of documents a query term can appear in order to provide suggestions.

      Very high-frequency terms are typically spelled correctly. Additionally, this can increase performance as it will do no work for the common case of correctly-spelled input terms.

      This can be specified as a relative percentage of documents such as 0.5f, or it can be specified as an absolute whole document frequency, such as 4f. Absolute document frequencies may not be fractional.

    • getLowerCaseTerms

      public boolean getLowerCaseTerms()
      true if the spellchecker should lowercase terms

    • setLowerCaseTerms

      public void setLowerCaseTerms(boolean lowerCaseTerms)
      True if the spellchecker should lowercase terms (default: true)

      This is a convenience method, if your index field has more complicated analysis (such as StandardTokenizer removing punctuation), its probably better to turn this off, and instead run your query terms through your Analyzer first.

      If this option is not on, case differences count as an edit!

    • getComparator

      public Comparator<SuggestWord> getComparator()
      Get the current comparator in use.

    • setComparator

      public void setComparator(Comparator<SuggestWord> comparator)
      Set the comparator for sorting suggestions. The default is SuggestWordQueue.DEFAULT_COMPARATOR

    • getDistance

      public StringDistance getDistance()
      Get the string distance metric in use.

    • setDistance

      public void setDistance(StringDistance distance)
      Set the string distance metric. The default is INTERNAL_LEVENSHTEIN

      Note: because this spellchecker draws its candidates from the term dictionary using Damerau-Levenshtein, it works best with an edit-distance-like string metric. If you use a different metric than the default, you might want to consider increasing setMaxInspections(int) to draw more candidates for your metric to rank.

    • suggestSimilar

      public SuggestWord[] suggestSimilar(Term term, int numSug, IndexReader ir) throws IOException
      Calls suggestSimilar(term, numSug, ir, SuggestMode.SUGGEST_WHEN_NOT_IN_INDEX)
      Throws:
      IOException

    • suggestSimilar

      public SuggestWord[] suggestSimilar(Term term, int numSug, IndexReader ir, SuggestMode suggestMode) throws IOException
      Calls suggestSimilar(term, numSug, ir, suggestMode, this.accuracy)
      Throws:
      IOException

    • suggestSimilar

      public SuggestWord[] suggestSimilar(Term term, int numSug, IndexReader ir, SuggestMode suggestMode, float accuracy) throws IOException
      Suggest similar words.

      Unlike SpellChecker, the similarity used to fetch the most relevant terms is an edit distance, therefore typically a low value for numSug will work very well.

      Parameters:
      term - Term you want to spell check on
      numSug - the maximum number of suggested words
      ir - IndexReader to find terms from
      suggestMode - specifies when to return suggested words
      accuracy - return only suggested words that match with this similarity
      Returns:
      sorted list of the suggested words according to the comparator
      Throws:
      IOException - If there is a low-level I/O error.