Examples of edu.ucla.sspace.wordsi.Wordsi

• edu.ucla.sspace.wordsi.Wordsi
  An interface for all Wordsi implementations. A complete {@link Wordsi}implementation will likely contain four parts: a {@link ContextExtractor}, a clustering method, and a {@link ContextAssignmentMap}, and a {@link AssignmentReporter}. The {@link Context Extractor} will genrate contextvectors for a set of words within a given BufferedReader and call {@code handleContextVector} for each context vector that is generated. Each contextvector can be index by two keys: the primary key, which is generally the focus word for the context vectors and the secondary key, which is either the same as the focus word or an additional value such as a SenseEval/SemEval instance identifier. The {@link ContextAssignmentMap} is reponsible forrecording which secondary keys and context id's are assigned to each focus term, in many cases, this is not neccesary, but if the exact clustering for each context is required, one should use a {@link ContextAssignmentMap}. The clustering method will assign the context vector to some cluster, either immediately or by storing the context vectors and performing a batch clustering. The {@link AssignmentReporter} is reponsible for reporting whichcontext vectors were assigned to which clusters. The three major components to {@link Wordsi} are separated so that each various context extractionalgorithms can be combined with various clustering algorithms and reporting methods.

  Implementations are suggested to subclass {@link BaseWordsi}, since it provides some methods for accepting and rejecting terms and dispatching the {@link ContextExtractor}. @see ContextExtractor @see AssignmentReporter @author Keith Stevens

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