Estimating Measurement Uncertainty for Information Retrieval Effectiveness Metrics

2018 ◽  
Vol 10 (3) ◽  
pp. 1-22
Author(s):  
Alistair Moffat ◽  
Falk Scholer ◽  
Ziying Yang
Keyword(s):  
Information Retrieval ◽  
Measurement Uncertainty ◽  
Retrieval Effectiveness

Post-retrieval search hit clustering to improve information retrieval effectiveness: Two digital forensics case studies

2011 ◽  
Vol 51 (4) ◽  
pp. 732-744 ◽  
Author(s):  
Nicole Lang Beebe ◽  
Jan Guynes Clark ◽  
Glenn B. Dietrich ◽  
Myung S. Ko ◽  
Daijin Ko
Keyword(s):  
Information Retrieval ◽  
Case Studies ◽  
Digital Forensics ◽  
Retrieval Effectiveness

UTILIZING LOCAL CONTEXT FOR EFFECTIVE INFORMATION RETRIEVAL

2008 ◽  
Vol 07 (01) ◽  
pp. 5-21 ◽  
Author(s):  
TANVEER J. SIDDIQUI ◽  
UMA SHANKER TIWARY
Keyword(s):  
Information Retrieval ◽  
Similarity Measure ◽  
Question Answering ◽  
Graph Matching ◽  
Information Filtering ◽  
Similarity Measures ◽  
Local Context ◽  
Structural Variations ◽  
Retrieval Effectiveness ◽  
Relational Similarity

Our research focuses on the use of local context through relation matching to improve retrieval effectiveness. An information retrieval (IR) model that integrates relation and keyword matching has been used in this work. The model takes advantage of any existing relational similarity between documents and query to improve retrieval effectiveness. It gives high rank to a document in which the query concepts are involved in similar relationships as in the query, as compared to those in which they are related differently. A conceptual graph (CG) representation has been used to capture relationship between concepts. A simplified form of graph matching has been used to keep our model computationally tractable. Structural variations have been captured during matching through simple heuristics. Four different CG similarity measures have been proposed and used to evaluate performance of our model. We observed a maximum improvement of 7.37% in precision with the second CG similarity measure. The document collection used in this study is CACM-3204. CG similarity measure proposed by us is simple, flexible and scalable and can find application in many IR related tasks like information filtering, information extraction, question answering, document summarization, etc.

scholarly journals When time meets information retrieval: Past proposals, current plans and future trends

2016 ◽  
Vol 42 (6) ◽  
pp. 725-747 ◽  
Author(s):  
Bilel Moulahi ◽  
Lynda Tamine ◽  
Sadok Ben Yahia
Keyword(s):  
Information Retrieval ◽  
Web Search ◽  
Specific Information ◽  
Time Dimension ◽  
Retrieval Models ◽  
Document Ranking ◽  
Common Information ◽  
Retrieval Effectiveness ◽  
Ranking Models ◽  
Tremendous Amount

With the advent of Web search and the large amount of data published on the Web sphere, a tremendous amount of documents become strongly time-dependent. In this respect, the time dimension has been extensively exploited as a highly important relevance criterion to improve the retrieval effectiveness of document ranking models. Thus, a compelling research interest is going on the temporal information retrieval realm, which gives rise to several temporal search applications. In this article, we intend to provide a scrutinizing overview of time-aware information retrieval models. We specifically put the focus on the use of timeliness and its impact on the global value of relevance as well as on the retrieval effectiveness. First, we attempt to motivate the importance of temporal signals, whenever combined with other relevance features, in accounting for document relevance. Then, we review the relevant studies standing at the crossroads of both information retrieval and time according to three common information retrieval aspects: the query level, the document content level and the document ranking model level. We organize the related temporal-based approaches around specific information retrieval tasks and regarding the task at hand, we emphasize the importance of results presentation and particularly timelines to the end user. We also report a set of relevant research trends and avenues that can be explored in the future.

In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

2019 ◽  
Vol 119 (2) ◽  
pp. 987-1008 ◽  
Author(s):  
Maryam Yaghtin ◽  
Hajar Sotudeh ◽  
Mahdieh Mirzabeigi ◽  
Seyed Mostafa Fakhrahmad ◽  
Mehdi Mohammadi
Keyword(s):  
Information Retrieval ◽  
Retrieval Effectiveness

Retrieval Effectiveness of Google on Reverse Image Search

2021 ◽  
Author(s):  
Yıltan Bitirim
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Search Performance ◽  
Image Search ◽  
Relevance Judgment ◽  
Retrieval Effectiveness ◽  
Binary Relevance ◽  
Similar Images

This study investigates the reverse image search performance of Google, in terms of Average Precisions (APs) and Average Normalized Recalls (ANRs) at various cut-off points,on finding out similar images by using fresh Image Queries (IQs) from the five categories “Fashion,” “Computer,” “Home,” “Sports,” and “Toys.” The aim is to have an insight about retrieval effectiveness of Google on reverse image search and then motivate researchers and inform users. Five fresh IQs with different main concepts were created for each of the five categories. These 25 IQs were run on the search engine, and for each, the first 100 images retrieved were evaluated with binary relevance judgment. At the cut-off points 20, 40, 60, 80, and 100, both APs and ANRs were calculated for each category and for all 25 IQs. The AP range is from 41.60% (Toys—cut-off point 100) to 71% (Home—cut-off point 20). The ANR range is from 47.21% (Toys—cut-off point 20) to 71.31% (Computer—cut-off point 100). If the categories are ignored; when more images were evaluated, the performance of displaying relevant images in higher ranks increased, whereas the performance of retrieving relevant images decreased. It seems that the information retrieval effectiveness of Google on reverse image search needs to be improved.

Legal Information Retrieval Study – Lexis Professional and Westlaw UK

2006 ◽  
Vol 6 (4) ◽  
pp. 246-250 ◽  
Author(s):  
Dean Mason
Keyword(s):  
Information Retrieval ◽  
Information Science ◽  
Legal Research ◽  
Legal Information ◽  
Retrieval Effectiveness ◽  
Retrieval Study ◽  
Masters Degree ◽  
Research Tools

This article by Dean Mason looks at the retrieval effectiveness of the online legal research tools Lexis Professional and Westlaw UK and is the result of research carried out for his Masters Degree in Information Science.

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