Paper Info
Title
On a combination of probabilistic and boolean ir models for WWW document retrieval
Abstract
Even though a Boolean query can express the information need precisely enough to select relevant documents, it is not easy to construct an appropriate Boolean query that covers all relevant documents. To utilize a Boolean query effectively, a mechanism to retrieve as many as possible relevant documents is therefore required. In accordance with this requirement, we propose a method for modifying a given Boolean query by using information from a relevant document set. The retrieval results, however, may deteriorate if some important query terms are removed by this reformulation. A further mechanism is thus required in order to use other query terms that are useful for finding more relevant documents, but are not strictly required in relevant documents. To meet this requirement, we propose a new method that combines the probabilistic IR and the Boolean IR models. We also introduce a new IR system---called appropriate Boolean query reformulation for information retrieval (ABRIR)---based on these two methods and the Okapi system. ABRIR uses both a word index and a phrase index formed from combinations of two adjacent noun words. The effectiveness of these two methods was confirmed according to the NTCIR-4 Web test collection.
Year
DOI
Venue
2005
10.1145/1111667.1111674
ACM Trans. Asian Lang. Inf. Process.
Keywords
Field
DocType
important query term,boolean ir model,probabilistic ir model,relevant document set,query term,experimentation additional key words and phrases: boolean ir model,general terms: information retrieval,appropriate boolean query reformulation,appropriate boolean query,relevant document,information retrieval,www document retrieval,possible relevant document,boolean query,algorithms,indexation,noun,information system,document retrieval,information need
Query optimization,Web search query,Query language,Information retrieval,Query expansion,Computer science,Sargable,Web query classification,Ranking (information retrieval),Artificial intelligence,Natural language processing,Standard Boolean model
Journal
Volume
Issue
Citations 
4
3
5
PageRank 
References 
Authors
0.50
15
2
Name
Order
Citations
PageRank
Masaharu Yoshioka136841.40
Makoto Haraguchi217332.53