Parameters are:
sourceIndex: The complete name of the index for the source language collection. This provides the background model for the source language. targetIndex: The complete name of the index for the target language collection. This is the collection that is searched.
textQuery: the query text stream, in the source language XLlambda: The smoothing parameter for mixing P(t|D) and P(s|GS). XLbeta: The Jelinik-Mercer lambda for estimating P(t|D).
sourceBackgroundModel: One of "term" or "doc". If term, background model for the source language is estimated as tf(s)/|V|. If doc, the background model for the source language is estimated as df(t)/sum_w_in_V df(w). Default is term.
targetBackgroundModel: One of "term" or "doc". If term, background model for the target language is estimated as tf(s)/|V|. If doc, the background model for the target language is estimated as df(t)/sum_w_in_V df(w). Default is term.
resultFile: the result file resultFormat: whether the result format should be of the TREC format (i.e., six-column) or just a simple three-column format <queryID, docID, score>. String value, either trec for TREC format or 3col for three column format. Default: TREC format. resultCount: the number of documents to return for each query
feedbackDocCount: the number of docs to use for pseudo-feedback (0 means no-feedback) feedbackTermCount: the number of terms to add to a query when doing feedback. Simple KL parameters:
smoothSupportFile: The name of the smoothing support file smoothMethod: One of the four: jelinikmercer or jm for Jelinek-Mercer dirichletprior or dir for Dirichlet prior absolutediscount or ad for Absolute discounting twostage or 2s for two stage.
smoothStrategy: Either interpolate for interpolate or backoff for backoff.
adjustedScoreMethod: Which type of score to output, one of:
JelinekMercerLambda: The collection model weight in the JM interpolation method. Default: 0.5
DirichletPrior: The prior parameter in the Dirichlet prior smoothing method. Default: 1000
discountDelta: The delta (discounting constant) in the absolute discounting method. Default 0.7. queryUpdateMethod: feedback method, one of: relevancemodel1 or rm1 for relevance model 1. relevancemodel2 or rm2 for relevance model 2. 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. feedbackTermCount, feedbackProbThresh, and feedbackProbSumThresh work conjunctively to control the truncation, i.e., the truncated model must satisfy all the three constraints.
1.3.4