Book search using social information, user profiles and query expansion with Pseudo Relevance Feedback

Kumar, Ritesh; Bhanodai, Guggilla; Pamula, Rajendra

doi:10.1007/s10489-018-1383-z

Cited by 17 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with previous studies (Deveaud et al ., 2012; Kim et al ., 2012; Wu et al ., 2016; Hamad and Al-Shboul, 2017; Kumar et al ., 2019), the results confirmed the informativeness and hence effectiveness of users' comments in improving the performance of an information system. The results of this study are also aligned with the previous research findings on books, in that they showed that user reviews provide a better signal for topical relevance and effectively improve the search results, compared to professional metadata (Koolen, 2014; Koolen et al ., 2012; Zhang et al ., 2016; Kumar et al ., 2019). Social information effectively improve the retrieval of relevant books (Kumar and Pamula, 2020).…”

Section: Discussionmentioning

confidence: 99%

Comment-enriched index terms improve the relevance and novelty of the ranking of the commented medical articles retrieved by an NLP system

Rashidi

Sotudeh

Nikseresht

2022

OIR

View full text Add to dashboard Cite

PurposeThis study aimed to investigate how the enrichment of medical documents' index terms by their comments improves the relevance and novelty of the top-ranked results retrieved by an NLP system.Design/methodology/approachA semi-experimental pre-test and post-test research was designed to compare NLP-based indexes before and after being expanded by the comment terms. The experiments were conducted on a test collection of 13,957 documents commented by F1000-Prime reviewers. They were indexed at title, abstract, body and full-text levels. In total, 100 seed documents were randomly selected and served as queries. The textual similarity of the documents and queries was calculated using Lucene-more-like-this function and evaluated by the semantic similarity of their MeSH. The results novelty was measured using maximal marginal relevance and evaluated by their MeSH novelties. Normalized discounted cumulative gain was used to compare the basic and expanded indexes' precisions at 10, 20 and 50 top ranks.FindingsThe relevance and novelty of the results ranked at the top precision points was improved after expanding the indexes by the comment terms. The finding implies that meta-texts are effective in representing their mother documents, by adding dynamic elements to their rather static contents. It also provides further evidence about the merits of the application of social intelligence and collective wisdom reflected in the actions and reactions of users in tackling the challenges faced by NLP-based systems.Originality/valueThis is the first study to confirm that social comments on scientific papers improve the performance of information systems in terms of relevance and novelty.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0283.

show abstract

Section: Discussionmentioning

confidence: 99%

Comment-enriched index terms improve the relevance and novelty of the ranking of the commented medical articles retrieved by an NLP system

Rashidi

Sotudeh

Nikseresht

2022

OIR

View full text Add to dashboard Cite

show abstract

“…car and driver). Here, expansion terms are usually identified from i) user feedback: extracting frequent terms occurring in previous results deemed relevant by the user [41,66], and/or ii) query logs: identifying frequent terms in the document collection based on the associations between past queries and the documents downloaded by the user [30,74]. Yet, the extensive training and huge corpora requirements of corpusbased methods makes them less practical in the context of Web search applications, which has led to a growing interest in knowledge-based solutions [35,61].…”

Section: Query Semantic Analysis and Disambiguationmentioning

confidence: 99%

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Tekli

Chbeir

2023

COMSIS J

View full text Add to dashboard Cite

Many efforts have been deployed by the IR community to extend free-text query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach.

show abstract

“…Pseudo-relevance feedback (PRF) is a refinement of relevance feedback which adds an automated step to avoid the user contribution in the process of query expansion [10]. The data flow from the user query to the retrieved documents includes the variation of the weights of query terms assuming that the top retrieved documents are relevant and using the position of the documents as a feedback for relevance (this makes automatic the step c. in Fig.…”

Section: Query Expansion Techniquesmentioning

confidence: 99%

Evaluation of Semantic Relations Impact in Query Expansion-Based Retrieval Systems

Massai

2023

Preprint

View full text Add to dashboard Cite

Book search using social information, user profiles and query expansion with Pseudo Relevance Feedback

Cited by 17 publications

References 33 publications

Comment-enriched index terms improve the relevance and novelty of the ranking of the commented medical articles retrieved by an NLP system

Comment-enriched index terms improve the relevance and novelty of the ranking of the commented medical articles retrieved by an NLP system

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Evaluation of Semantic Relations Impact in Query Expansion-Based Retrieval Systems

Contact Info

Product

Resources

About