Information Retrieval Models

Hiemstra, Djoerd

doi:10.1002/9780470033647.ch1

Cited by 36 publications

(27 citation statements)

References 36 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results may be filtered by clicking on a publication year or source document type c. NIST Digital Library of Mathematical Functions. Shown are results for a boolean query combining math and keywords [3,102] [62]). Data are shown in boxes, system processes in ovals, user interfaces in double ovals, and user elements with no surrounding shape.…”

Section: Mathematical Content Interpretation (Section 44)mentioning

confidence: 99%

“…Evaluation is discussed in Section 3.5. Mathematical Information Retrieval (MIR) is a relatively new research area, lying at the intersection of text-based information retrieval [62,125], content-based image retrieval [35,38,132] and Mathematical Knowledge Management (MKM [25]). Mathematical knowledge management is concerned with the representation, archiving, extraction, and use of mathematical information.…”

Section: Overview Of Mathematical Information Retrievalmentioning

confidence: 99%

“…In vector-space models, documents are represented by vectors in R n , where each dimension corresponds to an index term [62,95,125]. Index terms normally exclude stop words (very high frequency terms such as 'the' that carry little information) as well as highly infrequent terms, whose inclusion would have little effect on retrieval performace, while increasing the dimensionality of the vector space.…”

Section: Vector-space Modelsmentioning

confidence: 99%

“…Documents are represented by the weighted number of occurrences of each index term (the term frequencies). Commonly, term frequencies are weighted using some variation of inverse document frequency, to emphasize terms that appear in fewer documents in the collection, and thereby likely to be more informative [62,125]:…”

Section: Vector-space Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Recognition and retrieval of mathematical expressions

Zanibbi

Blostein

2011

IJDAR

233

112

View full text Add to dashboard Cite

Document recognition and retrieval technologies complement one another, providing improved access to increasingly large document collections. While recognition and retrieval of textual information is fairly mature, with wide-spread availability of Optical Character Recognition (OCR) and text-based search engines, recognition and retrieval of graphics such as images, figures, tables, diagrams, and mathematical expressions are in comparatively early stages of research. This paper surveys the state of the art in recognition and retrieval of mathematical expressions, organized around four key problems in math retrieval (query construction, normalization, indexing, and relevance feedback), and four key problems in math recognition (detecting expressions, detecting and classifying symbols, analyzing symbol layout, and constructing a representation of meaning). Of special interest is the machine learning problem of jointly optimizing the component algorithms in a math recognition system, and developing effective indexing, retrieval and relevance feedback algorithms for math retrieval. Another important open problem is developing user interfaces that seamlessly integrate recognition and retrieval. Activity in these important research areas is increasing, in part because math notation provides an excellent domain for studying problems common to many document and graphics recognition and retrieval applications, and also because mature applications will likely provide substantial benefits for education, research, and mathematical literacy.

show abstract

Section: Mathematical Content Interpretation (Section 44)mentioning

confidence: 99%

Section: Overview Of Mathematical Information Retrievalmentioning

confidence: 99%

Section: Vector-space Modelsmentioning

confidence: 99%

Section: Vector-space Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Recognition and retrieval of mathematical expressions

Zanibbi

Blostein

2011

IJDAR

233

112

View full text Add to dashboard Cite

show abstract

“…For the first years of its operation, Google used a PageRank model, metadata and font size to rank and return items (Brin & Page, 1998). The PageRank model used the hyperlink structure to judge the quality of a page, where pages linked from more places on the web were ranked higher in the list of search items (Hiemstra, 2009). Knight and Spink (2008) showed that web search engines contribute to more complex information search strategies than existed in traditional information seeking and therefore, a different set of information seeking behaviours is likely to evolve.…”

Section: Interactions With Expertisementioning

confidence: 99%

New Perspectives on Personalised Search Results: Expertise and Institutionalisation

Tran

Yerbury

2015

Australian Academic & Research Libraries

View full text Add to dashboard Cite

Embedded in literature about the personalisation of search engine results, one can see the sociological idea that institutionalised ideas and practices, filtered down from expert knowledge, have constructed the way that people understand information search, that personalised search engines have become acknowledged as socially accepted sources of knowledge and that their discursive practices are becoming dominant. This interpretivist study of 13 Google users sought to investigate how young people perceive personalisation of search results by Google and includes a focus on the information search strategies they used to identify quality and authority in personalised information. Through this, it explores processes of the institutionalisation of expertise. The results from this study demonstrate that although Google is becoming institutionalised as a source of expertise, users significantly control the way they use search results and conceptualise the authority of these results.

show abstract

Relevant Information Management in Microblogs

Cherichi¹,

Faiz²

2014

Information Systems for Knowledge Management

View full text Add to dashboard Cite

Information Retrieval Models

Cited by 36 publications

References 36 publications

Recognition and retrieval of mathematical expressions

Recognition and retrieval of mathematical expressions

New Perspectives on Personalised Search Results: Expertise and Institutionalisation

Relevant Information Management in Microblogs

Contact Info

Product

Resources

About