Bi-Gram Term Collocations-based Query Expansion Approach for Improving Arabic Information Retrieval

Moawad, Ibrahim F.; Alromima, Waseem; Elgohary, Rania

doi:10.1007/s13369-018-3145-y

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…In Arabic text retrieval, two sub-fields were found within our reviews, which are Quran and hadith. Moawad, Alromima, and Elgohary [7] present a query expansion for Arabic retrieval. They concentrated on Quran Arabic retrieval.…”

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

confidence: 99%

“…Out of the ten papers stated, six papers were reported based on conference proceedings while four reported on research articles (see Table 9). Query expansion has been stated as useful for improving programming code search [7]. This state shows how this area will significantly attract researchers because thousands of source codes are now available in a database for retrieval.…”

Section: ) Programming Code Searchmentioning

confidence: 99%

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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

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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.

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

confidence: 99%

Section: ) Programming Code Searchmentioning

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

“…It also focuses on the roles of actors on social networks and annotating the topics based on topic taxonomy. Moawad et al (2018) proposed a semantic oriented information extraction approach using bi-gram model for query expansion in the Arabic language. The proposed model is tested against the stem-based method showing remarkable results in terms of precision and recall.…”

Section: Related Workmentioning

confidence: 99%

Computational linguistic retrieval framework using negative bootstrapping for retrieving transliteration variants

Shekhar

Sharma

Beg

2020

IJCVR

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In NLP, one of the imperative and relatively less mature area is transliteration. During transliteration, issues like language identification, script specification arise in mixed script queries. To overwhelm these issues, we propose a new technique called negative bootstrapping with frequent matrix apriori for transliteration. Roman script is widely used in web search query for searching contents. The major challenge that the system face to process transliterated word is because of its existence in more than one form. The experimental evaluation has been done to check transliteration accuracy along with language identification against established methods. The paper offers a high-principled answer to handle multiple scripts used in a document leading to the problems of term matching and committing variations in spelling while searching the contents. The problem is modelled collectively with the deep-learning design and achieves significantly better results when applied to n-gram approach on the benchmark dataset.

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“…The Arabic language document has grown rapidly over time. This is evidenced by more than 100 million Arabic web page content [1]. The field of Arabic Natural Language Processing (ANLP) has poor attention compared to English that make Arabic has scarce resource, such as corpora and semantic [2].…”

Section: Introductionmentioning

confidence: 99%

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Maryamah¹,

Arifin²,

Sarno³

et al. 2019

IJIES

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Document searching using queries that can understand the context can affect the intent and purpose of the user's desire when searching documents. Many studies have been conducted on understanding the context of the query, but differences in terms of language can lead to different methods of context understanding; therefore, methods implemented in the previous studies need to be improved. In this paper, we proposed a query expansion method based on BabelNet search and Word Embedding (BabelNet Embedding). Query expansion method focuses on developing queries based on semantic relationships on queries to understand the context of the query. Candidate queries were developed by finding synonyms, measuring similarity using WordNet, Word Embedding on all articles of Wikipedia, and BableNet Embedding on articles Wikipedia Online. We compared our proposed method with the existing semantic query expansion. Our result provided better result in retrieving relevant document with accuracy of 89% in searching Arabic documents.

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Bi-Gram Term Collocations-based Query Expansion Approach for Improving Arabic Information Retrieval

Cited by 16 publications

References 25 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

Computational linguistic retrieval framework using negative bootstrapping for retrieving transliteration variants

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

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