Differences between revisions 4 and 5

Deletions are marked like this. | Additions are marked like this. |

Line 7: | Line 7: |

The tuning method by measuring normalisation effect estimates the free-parameters of ["BM25'sNormalisation"] and the ["Normalisation2"] of the ["DivergenceFromRandomness"] models (He & Ounis, ECIR2005). The notion of ''normalisation effect'' refers to the variation of the term frequency with respect to the ["DocumentLength"] distribution, as defined in (He & Ounis, ECIR2005). The ''optimal normalisation effect'' stands for the normalisation effect measure that corresponding to the parameter setting that gives the highest mean average precision, which is a collection-independent constant. The tuning methodology can be summarised as follows: | The tuning method by measuring normalisation effect estimates the free-parameters of ["BM25'sNormalisation"] and the ["Normalisation2"] of the ["DivergenceFromRandomness"] models (He & Ounis, ECIR2005). The notion of ''normalisation effect'' refers to the variation of the term frequency with respect to the ["Document Length"] distribution, as defined in (He & Ounis, ECIR2005). The ''optimal normalisation effect'' stands for the normalisation effect measure that corresponding to the parameter setting that gives the highest mean average precision, which is a collection-independent constant. The tuning methodology can be summarised as follows: |

### Introduction

The *term frequency normalisation tuning* estimates the free-parameter of a TermFrequencyNormalisation method. It is a crucial issue in InformationRetrieval that significantly affects robustness and effectiveness of retrieval performance.

### Term frequency normalisation tuning by measuring normalisation effect

The tuning method by measuring normalisation effect estimates the free-parameters of BM25'sNormalisation and the Normalisation2 of the DivergenceFromRandomness models (He & Ounis, ECIR2005). The notion of *normalisation effect* refers to the variation of the term frequency with respect to the Document Length distribution, as defined in (He & Ounis, ECIR2005). The *optimal normalisation effect* stands for the normalisation effect measure that corresponding to the parameter setting that gives the highest mean average precision, which is a collection-independent constant. The tuning methodology can be summarised as follows:

Obtain the optimal normalisation effect on a training collection using relevance assessment.

On a given new collection, apply the parameter setting such that it gives the optimal normalisation effect.

On a given new collection, the normalisation effect is computed over a set of queries that are generated by the QuerySimulation based on the DFR model.