Name
Abstract | ||
|---|---|---|
Query expansion is a useful retrieval mechanism for creating more verbose queries from the users initial key word search. Query expansion generally have multiple parameters that allow the user to define how many terms and where those terms come from are introduced to the expanded query. However, the idea that query expansion may be introducing biases into the system by selecting terms from overly retrievable documents has never been formally evaluated. In this work, the relationship between performance and retrievability bias is explored when various query expansion methods are employed to aide retrieval. Several parameters are altered, independently, to identify those that have an impact on bias. Parameters altered include; Rocchio's beta, length normalisation parameters, the number of terms added and the number of documents those terms are extracted from. The evaluation performed here identifies a strong correlation between performance and retrievability bias, suggesting that performance is increased by making the system more biased thus more likely to pick terms from a set of overly retrievable documents. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3121050.3121097 | ICTIR'17: PROCEEDINGS OF THE 2017 ACM SIGIR INTERNATIONAL CONFERENCE THEORY OF INFORMATION RETRIEVAL |
Field | DocType | Citations |
Data mining,Information retrieval,Query expansion,Retrievability,Computer science,Correlation | Conference | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Colin Wilkie | 1 | 52 | 5.58 |
| Leif Azzopardi | 2 | 1919 | 133.10 |