Feedback can be based on true relevance judgments or any previously returned retrieval results.
Two important notes:
index: The complete name of the index table-of-content file for the database index.
smoothSupportFile: The name of the smoothing support file (e.g., one generated by GenerateSmoothSupport).
textQuery: the original query text stream
initQuery: the file with a saved initial query model. When this parameter is set to a non-empty string, the model stored in this file will be used for expansion; otherwise, the original query text is used the initial query model for expansion.
feedbackDocuments: the file of feedback documents to be used for feedback. In the case of pseudo feedback, this can be a result file generated from an initial retrieval process. In the case of relevance feedback, this is usually a 3-column relevance judgment file. Note that this means you can NOT use a TREC-style judgment file directly; you must remove the second column to convert it to three-column.
resultFormat: whether the feedback document file (given by
feedbackDocumentsis of the TREC format (i.e., six-column) or just a simple three-column format
<queryID, docID, score>. String value, either
trecfor TREC format or
3colfor three column format. The integer values, zero for non-TREC format, and non-zero for TREC format used in previous versions of lemur are accepted. Default: TREC format.
expandedQuery: the file to store the query clarity scores.
feedbackDocCount: the number of docs to use for pseudo-feedback (0 means no-feedback)
queryUpdateMethod: feedback method, one of:
mixor 0 for mixture.
divor 1 for div min
mcor 2 for markov chain
rm1or 3 for relevance model 1.
rm2or 4 for relevance model 2.
For all interpolation-based approaches (i.e., the new query model is an interpolation of the original model with a (feedback) model computed based on the feedback documents), the following four parameters apply:
feedbackCoefficient: the coefficient of the feedback model for interpolation. The value is in [0,1], with 0 meaning using only the original model (thus no updating/feedback) and 1 meaning using only the feedback model (thus ignoring the original model).
feedbackTermCount: Truncate the feedback model to no more than a given number of words/terms.
feedbackProbThresh: Truncate the feedback model to include only words with a probability higher than this threshold. Default value: 0.001.
feedbackProbSumThresh: Truncate the feedback model until the sum of the probability of the included words reaches this threshold. Default value: 1.
feedbackProbSumThresh work conjunctively to control the truncation, i.e., the truncated model must satisfy all the three constraints.
All the three feedback methods also recognize the parameter
feedbackMixtureNoise (default value :0.5), but with different interpretations.
feedbackMixtureNoiseis the collection model selection probability in the mixture model. That is, with this probability, a word is picked according to the collection language model, when a feedback document is "generated".
feedbackMixtureNoisemeans the weight of the divergence from the collection language model. (The higher it is, the farther the estimated model is from the collection model.)
feedbackMixtureNoiseis the probability of not stopping, i.e.,
1- alpha, where alpha is the stopping probability while walking through the chain.
In addition, the collection mixture model also recognizes the parameter
emIterations, which is the maximum number of iterations the EM algorithm will run. Default: 50. (The EM algorithm can terminate earlier if the log-likelihood converges quickly, where convergence is measured by some hard-coded criterion. See the source code in
SimpleKLRetMethod.cpp for details. )