Respect my authority!

Kurland, Oren; Lee, Lillian

doi:10.1145/1148170.1148188

Cited by 74 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proposing new ranking query-specific cluster strategies has been a hot research topic for many years (Leuski, 2001), and suggested solutions base on a cluster-against-query representation comparison (Liu & Croft, 2004) (Liu & Croft, 2008). Some document retrieval frameworks make use of extra features, including inter-cluster and clusterdocument similarities (Kurland & Lee, 2006) (Kurland & Domshlak, 2008) (Kurland & Krikon, 2011). Query expansion (QE) is another way to heighten document retrieval systems accuracy (Hiteshwar & Akshay, 2019).…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval

Ayed

Biskri

Meunier

2021

International Journal of Information Retrieval Research

View full text Add to dashboard Cite

In the context of big data and the 4.0 industrial revolution era, enhancing document/information retrieval frameworks efficiency to handle the ever‐growing volume of text data in an ever more digital world is a must. This article describes a double-stage system of document/information retrieval. First, a Lucene-based document retrieval tool is implemented, and a couple of query expansion techniques using a comparable corpus (Wikipedia) and word embeddings are proposed and tested. Second, a retention-fidelity summarization protocol is performed on top of the retrieved documents to create a short, accurate, and fluent extract of a longer retrieved single document (or a set of top retrieved documents). Obtained results show that using word embeddings is an excellent way to achieve higher precision rates and retrieve more accurate documents. Also, obtained summaries satisfy the retention and fidelity criteria of relevant summaries.

show abstract

Section: Introductionmentioning

confidence: 99%

An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval

Ayed

Biskri

Meunier

2021

International Journal of Information Retrieval Research

View full text Add to dashboard Cite

show abstract

On ranking relevant entities in heterogeneous networks using a language‐based model

Soulier

Jabeur

Tamine

et al. 2013

J Am Soc Inf Sci Tec

View full text Add to dashboard Cite

A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and networkbased scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSe-erX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.

show abstract