Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model

2011 ◽  
Keyword(s):  
Retrieval Models ◽  
Probabilistic Retrieval

Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model

2009 ◽  
pp. 2156-2160 ◽  
Author(s):  
Thomas Roelleke ◽  
Jun Wang ◽  
Stephen Robertson
Keyword(s):  
Retrieval Models ◽  
Probabilistic Retrieval

Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model

2018 ◽  
pp. 2839-2845
Author(s):  
Thomas Roelleke ◽  
Jun Wang ◽  
Stephen Robertson
Keyword(s):  
Retrieval Models ◽  
Probabilistic Retrieval

Integrating Boolean queries in conjunctive normal form with probabilistic retrieval models

1988 ◽  
Vol 24 (3) ◽  
pp. 315-321 ◽  
Author(s):  
Robert M. Losee ◽  
Abraham Bookstein
Keyword(s):  
Normal Form ◽  
Conjunctive Normal Form ◽  
Retrieval Models ◽  
Probabilistic Retrieval ◽  
Boolean Queries

Truncated Models for Probabilistic Weighted Retrieval

2022 ◽  
Vol 40 (3) ◽  
pp. 1-24
Author(s):  
Jiaul H. Paik ◽  
Yash Agrawal ◽  
Sahil Rishi ◽  
Vaishal Shah
Keyword(s):  
Performance Improvement ◽  
Upper Bound ◽  
Probabilistic Models ◽  
Modeling Error ◽  
Large Magnitude ◽  
Retrieval Models ◽  
Document Collections ◽  
Probabilistic Retrieval ◽  
Two Factors ◽  
Significant Performance

Existing probabilistic retrieval models do not restrict the domain of the random variables that they deal with. In this article, we show that the upper bound of the normalized term frequency ( tf ) from the relevant documents is much smaller than the upper bound of the normalized tf from the whole collection. As a result, the existing models suffer from two major problems: (i) the domain mismatch causes data modeling error, (ii) since the outliers have very large magnitude and the retrieval models follow tf hypothesis, the combination of these two factors tends to overestimate the relevance score. In an attempt to address these problems, we propose novel weighted probabilistic models based on truncated distributions. We evaluate our models on a set of large document collections. Significant performance improvement over six existing probabilistic models is demonstrated.

On event space and rank equivalence between probabilistic retrieval models

2008 ◽  
Vol 11 (6) ◽  
pp. 539-561 ◽  
Author(s):  
Robert W. P. Luk
Keyword(s):  
Event Space ◽  
Retrieval Models ◽  
Probabilistic Retrieval

Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model

2016 ◽  
pp. 1-7
Author(s):  
Thomas Roelleke ◽  
Jun Wang ◽  
Stephen Robertson
Keyword(s):  
Retrieval Models ◽  
Probabilistic Retrieval

A Survey on Information Retrieval Models, Techniques and Applications

2017 ◽  
Vol 7 (7) ◽  
pp. 16 ◽  
Author(s):  
Ndengabaganizi Tonny James ◽  
Rajkumar Kannan
Keyword(s):  
Information Retrieval ◽  
Retrieval Models ◽  
Knowledge Based ◽  
Long Time

It has been long time many people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information; and finding useful information from such collections became a necessity. Over the last forty years, Information Retrieval (IR) has matured considerably. Several IR systems are used on an everyday basis by a wide variety of users. Information retrieval (IR) is generally concerned with the searching and retrieving of knowledge-based information from database. In this paper, we will discuss about the various models and techniques and for information retrieval. We are also providing the overview of traditional IR models.

Probabilistic retrieval: thresholding for automatic filtering

1999 ◽  
Author(s):  
S. Robertson
Keyword(s):  
Probabilistic Retrieval ◽  
Automatic Filtering
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