public class XSqrA_M extends WeightingModel
XSqrA_M has a high performance, and in particular has statistically significant better MAP performance than all other supervised models on the GOV2 collection. MAP for short (title only) and medium (title+description) topics, and comparative p values (two-tailed paired t-test) compared to supervised models (with optimal MAP parameter values) are as follows:
Queries | MAP of XSqrA_M | LGD | Dirichlet_LM | PL2 | BM25 | In_expB2 |
short | 0.3156 | p=0.3277 | p=0.0075 | p=0.0055 | p=0.0064 | p=0.0002 |
medium | 0.3311 | p=2.3E-07 | p=0.0002 | p=0.0395 | p=0.0025 | p=2.4E-10 |
References Frequentist and Bayesian approach to Information Retrieval. G. Amati. In Proceedings of the 28th European Conference on IR Research (ECIR 2006). LNCS vol 3936, pages 13--24.
averageDocumentLength, c, cs, documentFrequency, es, i, keyFrequency, numberOfDocuments, numberOfPointers, numberOfTokens, numberOfUniqueTerms, rq, termFrequency
Constructor and Description |
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XSqrA_M()
A default constructor to make this model.
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Modifier and Type | Method and Description |
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String |
getInfo()
Returns the name of the model, in this case "XSqrA_M"
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double |
score(double tf,
double docLength)
Uses XSqrA_M to compute a weight for a term in a document.
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clone, getOverflowed, getParameter, prepare, score, setCollectionStatistics, setEntryStatistics, setKeyFrequency, setParameter, setRequest
public final String getInfo()
getInfo
in interface Model
getInfo
in class WeightingModel
public final double score(double tf, double docLength)
score
in class WeightingModel
tf
- The term frequency of the term in the documentdocLength
- the document's lengthTerrier Information Retrieval Platform4.1. Copyright © 2004-2015, University of Glasgow