A retrospective study of a hybrid document-context based retrieval model

Wu, H. C.; Luk, Robert W. P.; Wong, Kam-Fai; Kwok, K. L.

doi:10.1016/j.ipm.2006.10.009

Cited by 28 publications

(29 citation statements)

References 40 publications

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“…Recently, Wu et al [2007] proposed a novel retrieval model that achieved high retrieval effectiveness (i.e., between 60% and 80% mean average precision) in their retrospective experiments using several ad hoc test collections. The authors implicitly distinguish two types of relevance: the common document-wide 13:4…”

Section: General Modelmentioning

confidence: 99%

“…Typically, these real values are normalized between 0 and 1, without loss of generality. The model by Wu et al [2007] simulates a human evaluator who scans the document for local relevance information (see Figure 1). Scanning involves iterating through every document location, and deciding for each whether local relevance information is found.…”

Section: General Modelmentioning

confidence: 99%

“…In this article, we assume that the first location of any document starts at 1. According to Wu et al [2007], the outcome of a local relevance decision at location k of document d is determined by the information in the context that is denoted by c (d , k, n). This context has n terms on the left, and another n terms on the right from location k in d .…”

Section: General Modelmentioning

confidence: 99%

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Interpreting TF-IDF term weights as making relevance decisions

Luk

Wong

et al. 2008

ACM Trans. Inf. Syst.

650

311

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A novel probabilistic retrieval model is presented. It forms a basis to interpret the TF-IDF term weights as making relevance decisions. It simulates the local relevance decision-making for every location of a document, and combines all of these "local" relevance decisions as the "documentwide" relevance decision for the document. The significance of interpreting TF-IDF in this way is the potential to: (1) establish a unifying perspective about information retrieval as relevance decision-making; and (2) develop advanced TF-IDF-related term weights for future elaborate retrieval models. Our novel retrieval model is simplified to a basic ranking formula that directly corresponds to the TF-IDF term weights. In general, we show that the term-frequency factor of the ranking formula can be rendered into different term-frequency factors of existing retrieval systems. In the basic ranking formula, the remaining quantity − log p(r|t ∈ d ) is interpreted as the probability of randomly picking a nonrelevant usage (denoted byr) of term t. Mathematically, we show that this quantity can be approximated by the inverse document-frequency (IDF). Empirically, we show that this quantity is related to IDF, using four reference TREC ad hoc retrieval data collections.

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Section: General Modelmentioning

confidence: 99%

Section: General Modelmentioning

confidence: 99%

Section: General Modelmentioning

confidence: 99%

See 1 more Smart Citation

Interpreting TF-IDF term weights as making relevance decisions

Luk

Wong

et al. 2008

ACM Trans. Inf. Syst.

650

311

View full text Add to dashboard Cite

show abstract

“…A document's field structure is commonly used to improve retrieval performance in practice. The most commonly used approach for structured document retrieval is a score/rank linear combination [11,12,13,14], which treats each field as a separate document and computes a combined scores/ranks. In computing scores for each field, any ranking function for unstructured document retrieval can be adopted.…”

Section: Related Workmentioning

confidence: 99%

Effectively Searching Maps in Web Documents

Tan

Mitra

Giles

2009

Lecture Notes in Computer Science

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Maps are an important source of information in archaeology and other sciences. Users want to search for historical maps to determine recorded history of the political geography of regions at different eras, to find out where exactly archaeological artifacts were discovered, etc. Currently, they have to use a generic search engine and add the term map along with other keywords to search for maps. This crude method will generate a significant number of false positives that the user will need to cull through to get the desired results. To reduce their manual effort, we propose an automatic map identification, indexing, and retrieval system that enables users to search and retrieve maps appearing in a large corpus of digital documents using simple keyword queries. We identify features that can help in distinguishing maps from other figures in digital documents and show how a Support-Vector-Machine-based classifier can be used to identify maps. We propose map-level-metadata e.g., captions, references to the maps in text, etc. and document-level metadata, e.g., title, abstract, citations, how recent the publication is, etc. and show how they can be automatically extracted and indexed. Our novel ranking algorithm weights different metadata fields differently and also uses the document-level metadata to help rank retrieved maps. Empirical evaluations show which features should be selected and which metadata fields should be weighted more. We also demonstrate improved retrieval results in comparison to adaptations of existing methods for map retrieval. Our map search engine has been deployed in an online map-search system that is part of the Blind-Review digital library system.

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“…Note that only lists for single query terms are shown. Document-contexts are used because we believe that relevant information is located around query terms (Wu, Luk, Wong, & Kwok, 2007, 2008. By splitting the retrieval list into sub-lists, we hope that the proportion of relevant documents in a particular sub-list will be higher than that of the others.…”

Section: Introductionmentioning

confidence: 99%