Examples of edu.ucla.sspace.rri.ReflectiveRandomIndexing

• edu.ucla.sspace.rri.ReflectiveRandomIndexing
  An implementation of Reflective Random Indexing, which uses a two passes through the corpus to build semantic vectors that better approximate indirect co-occurrence. This implementation is based on the paper:

  This class defines the following configurable properties that may be set using either the System properties or using the {@link ReflectiveRandomIndexing#ReflectiveRandomIndexing(Properties)} constructor.

  Property: {@value #VECTOR_LENGTH_PROPERTY}
  Default: {@value #DEFAULT_VECTOR_LENGTH}

  This property sets the number of dimensions to be used for the index and semantic vectors.

  Property: {@value #USE_SPARSE_SEMANTICS_PROPERTY}
  Default: {@code true}

  This property specifies whether to use a sparse encoding for each word's semantics. Using a sparse encoding can result in a large saving in memory, while requiring more time to process each document.

  This class implements {@link Filterable}, which allows for fine-grained control of which semantics are retained. The {@link #setSemanticFilter(Set)}method can be used to speficy which words should have their semantics retained. Note that the words that are filtered out will still be used in computing the semantics of other words. This behavior is intended for use with a large corpora where retaining the semantics of all words in memory is infeasible.

  This class is thread-safe for concurrent calls of {@link #processDocument(BufferedReader) processDocument}. The {@link #getVector(String) getVector} method will only return valid reflectivevectors after the call to {@link #processSpace(Properties) processSpace}.

  @author David Jurgens

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