A comparison of information retrieval models applied to Thai digital library

Khankasikam, Krisda

doi:10.1109/iccae.2010.5451939

Cited by 4 publications

(2 citation statements)

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“…Khankasikam [41]. A comparative study of retrieval models for Thai documents was performed by [41]. In this work, the traditional Boolean model, VSM, and the basic probabilistic Binary independence model (BIM) are used on a test collection of 5000 Thai documents and 100 queries.…”

Section: Empirical Comparisonmentioning

confidence: 99%

A Comparison between Term-Independence Retrieval Models for Ad Hoc Retrieval

Dang

Luk

Allan

2021

ACM Trans. Inf. Syst.

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In Information Retrieval, numerous retrieval models or document ranking functions have been developed in the quest for better retrieval effectiveness. Apart from some formal retrieval models formulated on a theoretical basis, various recent works have applied heuristic constraints to guide the derivation of document ranking functions. While many recent methods are shown to improve over established and successful models, comparison among these new methods under a common environment is often missing. To address this issue, we perform an extensive and up-to-date comparison of leading term-independence retrieval models implemented in our own retrieval system. Our study focuses on the following questions: (RQ1) Is there a retrieval model that consistently outperforms all other models across multiple collections; (RQ2) What are the important features of an effective document ranking function? Our retrieval experiments performed on several TREC test collections of a wide range of sizes (up to the terabyte-sized Clueweb09 Category B) enable us to answer these research questions. This work also serves as a reproducibility study for leading retrieval models. While our experiments show that no single retrieval model outperforms all others across all tested collections, some recent retrieval models, such as MATF and MVD, consistently perform better than the common baselines.

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Section: Empirical Comparisonmentioning

confidence: 99%

A Comparison between Term-Independence Retrieval Models for Ad Hoc Retrieval

Dang

Luk

Allan

2021

ACM Trans. Inf. Syst.

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“…There are various information model used .one of them is vector space model. It is a model for representing text documents or any other items as vectors of identifiers [17]. It is utilized as a part of information filtering, information retrieval, indexing and relevancy rankings.…”

Section: Introductionmentioning

confidence: 99%

Document Ranking using Customizes Vector Method

Mesariya¹,

Madia²

2017

IJTSRD

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Information retrieval (IR) system is about positioning reports utilizing client's question and get the important records from extensive dataset. Archive positioning is fundamentally looking the pertinent record as per their rank. Document ranking is basically search the relevant document according to their rank. Vector space model is traditional and widely applied information retrieval models to rank the web page based on similarity values. Term weighting schemes are the significant of an information retrieval system and it is query used in document ranking. Tf-idf ranked calculates the term weight according to users query on basis of term which is including in documents. When user enter query it will find the documents in which the query terms are included and it will count the term calculate the Tf-idf according to the highest weight of value it will gives the ranked documents. KEYWORD Information retrieval, term frequency-inverse frequency, vector space model, Cosine similarity I.

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