Hybrid query expansion model for text and microblog information retrieval

Zingla, Meriem Amina; Latiri, Chiraz; Mulhem, Philippe; Berrut, Catherine; Slimani, Yahya

doi:10.1007/s10791-017-9326-6

Cited by 22 publications

(11 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research articles are typical for this area as 33 articles and 27 conference proceeding papers were reported (see Table 5). Zingla et al [4] recently used query expansion in the medical area to integrate semantic and sequences of medical terms to improve biomedical search engine results for articles. The MEDLINE was selected to use as a corpus for the study.…”

Section: Rq1: What Are the Query Expansion Application Areas?mentioning

confidence: 99%

“…They considered Twitter because of large tweets information available that require search improvement to obtain a relevant one. In addition to that, [4] also presented how different methods can be integrated for microblog retrieval. This method shows how research in this area has been increasingly advancing.…”

Section: Rq1: What Are the Query Expansion Application Areas?mentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Query Expansion Methods to Improve Relevant Search Engine Results

Yusuf¹,

Yunus²,

Wahid³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

Due to large volumes of documents available for retrieval in a search database, an intelligent method is required to retrieve relevant search results. Query expansion is one of such methods widely used in retrieving pertinent results of various search domains. The increased amount of information stored in a search engine database requires the use of query expansion. A query expansion deals with expanding the query by adding additional information to the query for effective retrieving relevant results. Recently, many query expansion techniques have been proposed to addresses the vocabulary mismatch problem that may arise in the information retrieval system. However, these techniques still have low precision results. This paper presents a systematic review of query expansion research from 1999 to 2018. The paper reviewed and discussed 573 research papers on query expansion methods and their application areas. It focuses only on the query expansion in text retrieval of search engines. This review's primary goal is to provide a broad overview of query expansion research and view how research approaches changed. The research paper analyzed and presented the contributions of each query expansion study. It also identifies major application areas of query expansions and their future opportunities. The finding of this study indicates a trend towards using semantic-ontology and pseudo-relevant feedbacks methods. This work will be beneficial to query expansion researchers in extending future work on query expansion research.

show abstract

Section: Rq1: What Are the Query Expansion Application Areas?mentioning

confidence: 99%

Section: Rq1: What Are the Query Expansion Application Areas?mentioning

confidence: 99%

A Survey of Query Expansion Methods to Improve Relevant Search Engine Results

Yusuf¹,

Yunus²,

Wahid³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

show abstract

“…Four alternative heuristics are then suggested to select the most relevant clusters. Zingla et al [36] proposed HQE (Hybrid Query Expansion) approach that combined external hashtags resources and association-rule mining for retrieving the most relevant texts from microblogs. Association-rule extraction is first applied on the text microblogging collection to generate the candidates.…”

Section: B Data Mining Techniquesmentioning

confidence: 99%

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

et al. 2020

View full text Add to dashboard Cite

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users' queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy. INDEX TERMS Hashtag retrieval, pattern mining, scalability.

show abstract

“…This technique reduces the noise between the user's request and the set of the collected documents. Zingla et al [45] combined external hashtags resources and association rule mining for retrieving the most relevant texts from microblogs. Association-rule extraction is first applied to the text microblogging collection to generate the candidates.…”

Section: Pattern Miningmentioning

confidence: 99%

Cluster-based information retrieval using pattern mining

et al. 2020

View full text Add to dashboard Cite

This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information retrieval. For this purpose, a novel cluster-based information retrieval approach is proposed, named Cluster-based Retrieval using Pattern Mining (CRPM). This approach integrates various clustering and pattern mining algorithms. First, it generates clusters of objects that contain similar objects. Three clustering algorithms based on k-means, DBSCAN (Density-based spatial clustering of applications with noise), and Spectral are suggested to minimize the number of shared terms among the clusters of objects. Second, frequent and high-utility pattern mining algorithms are performed on each cluster to extract the pattern bases. Third, the clusters of objects are ranked for every query. In this context, two ranking strategies are proposed: i) Score Pattern Computing (SPC), which calculates a score representing the similarity between a user query and a cluster; and ii) Weighted Terms in Clusters (WTC), which calculates a weight for every term and uses the relevant terms to compute the score between a user query and each cluster. Irrelevant information derived from the pattern bases is also used to deal with unexpected user queries. To evaluate the proposed approach, extensive experiments were carried out on two use cases: the documents and tweets corpus. The results showed that the designed approach outperformed traditional and cluster-based information retrieval approaches in terms of the quality of the returned objects while being very competitive in terms of runtime.

show abstract

Hybrid query expansion model for text and microblog information retrieval

Cited by 22 publications

References 41 publications

A Survey of Query Expansion Methods to Improve Relevant Search Engine Results

A Survey of Query Expansion Methods to Improve Relevant Search Engine Results

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Cluster-based information retrieval using pattern mining

Contact Info

Product

Resources

About