Improving Arabic information retrieval using word embedding similarities

Mahdaouy, Abdelkader El; Ouatik, Saïd El Alaoui; Gaussier, Éric

doi:10.1007/s10772-018-9492-y

Cited by 34 publications

(25 citation statements)

References 39 publications

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“…The results showed that the difference in terms of performance between the three word embedding models (Glove, CBOW and Skip-gram models) is not statistically significant. A straightforward path of future research is to study the impact of parameters that are used to learn word embedding, such as the context size and the dimension of word vectors, and to rely on other word-embedding-based IR models as the one proposed in El Mahdaouy et al [33]. We also plan on comparing our PRF approach to other query expansion methods as [5,19,20].…”

Section: Discussionmentioning

confidence: 99%

“…The results showed that incorporating the enhanced term frequency to standard probabilistic IR models significantly improves their baseline bag-of-words models on the standard Arabic collection TREC 2001/2002. Moreover, El Mahdaouy et al [33] have proposed a method to incorporate word embedding similarities into existing probabilistic IR models to deal with term mismatch for Arabic document retrieval. The main idea consists of selecting the most related terms, for each query term following the approach defined by Li and Gaussier [34] in the context of CLIR, either from the collection vocabulary or from each document.…”

Section: Related Workmentioning

confidence: 99%

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Word-embedding-based pseudo-relevance feedback for Arabic information retrieval

Mahdaouy

Alaoui

Gaussier

2018

Journal of Information Science

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Pseudo-relevance feedback (PRF) is a very effective query expansion approach, which reformulates queries by selecting expansion terms from top k pseudo-relevant documents. Although standard PRF models have been proven effective to deal with vocabulary mismatch between users’ queries and relevant documents, expansion terms are selected without considering their similarity to the original query terms. In this article, we propose a method to incorporate word embedding (WE) similarity into PRF models for Arabic information retrieval (IR). The main idea is to select expansion terms using their distribution in the set of top pseudo-relevant documents along with their similarity to the original query terms. Experiments are conducted on the standard Arabic TREC 2001/2002 collection using three neural WE models. The obtained results show that our PRF extensions significantly outperform their baseline PRF models. Moreover, they enhanced the baseline IR model by 22% and 68% for the mean average precision (MAP) and the robustness index (RI), respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Word-embedding-based pseudo-relevance feedback for Arabic information retrieval

Mahdaouy

Alaoui

Gaussier

2018

Journal of Information Science

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“…As a common data processing technology, 1 –3 information retrieval technology is the main way for users to query and obtain information and also the method and means to find information. The narrowly defined information retrieval refers only to information retrieval, 4,5 that is, the customer takes a specific method according to the needs and uses the search tool to find out the search process of the required information from the information collection. The generalized information retrieval is a process in which information is processed, organized, and stored in a certain way, and then the relevant information is accurately found according to the specific needs of the information user.…”

Section: Introductionmentioning

confidence: 99%

Probability model of sensitive similarity measures in information retrieval

Zhang

2020

International Journal of Advanced Robotic Systems

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In today’s Internet age, a lot of data is stored and used, which is very important. In people’s daily life, if these data are sorted, information retrieval technology will be used, and in information retrieval, some information retrieval inaccuracies often appear. Information retrieval model is an important framework and method for fast, complete, and accurate user information retrieval. With the rapid development of information technology, great changes have taken place in people’s production and life. Various information network technologies are widely used in people’s lives. The resulting flow of information shows explosive growth, information retrieval. User requirements are getting higher and higher. How to complete personalized information retrieval in a large amount of mixed information, so that retrieval technology can help us obtain effective retrieval results, has become a realistic problem worth exploring. In this article, the application of probability model based on sensitive similarity measure in information retrieval model is analyzed, and a similarity measure algorithm based on spectral clustering is proposed. By improving the similarity measure, the sensitivity problem of scale parameters is overcome and the retrieval precision is improved. In order to better reflect the superiority of the proposed algorithm, this article compares with ng-jordan-weiss (NJW) and deep sparse subspace clustering (DSSC) algorithms. The experimental results show that the proposed algorithm is superior to NJW and DSSC algorithms for different data sets in different evaluation indicators (Rand and F-measure).

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“…Various researchers have recently understood the power of using ontologies with word embeddings and have showcased their effectiveness in their works; some of them have been put here. WE-based Arabic IR models also use wordnet and embeddings and depict comparisons of working after incorporating embeddings as in [9]. QSST, a Quranic searching tool based on word embeddings gave a high performance with an average precision of 91.95% [10].…”

Section: Related Workmentioning

confidence: 99%

Query Expansion based on Word Embeddings and Ontologies for Efficient Information Retrieval

Rastogi¹,

Verma²,

Kumar³

2021

IJACSA

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Information retrieval has been an ever-going process for end users to fetch relevant data at one go. The problem intensifies more with unstructured data in a semantic web environment. It is also a promising area for researchers to dive in and refine it from time to time. Expanding the user query and reformulating it is one probable solution to increase the efficiency of the information retrieval system. In this paper we propose "WeOnto", a novel two-level query expansion algorithm that utilizes the combination of web ontologies and word embeddings for similarity calculation. In the first level, the Real estate Ontology (REO) is created using Protégé and Sparql queries are passed to retrieve probable semantic words from the given ontology for each inputted user query. The first level gave significant results and improved the information retrieval by 18%. The second level of algorithm uses word embedding enhanced with the domain knowledge that helps to retrieve similar meaningful words based on cosine similarity for the same user query. Word embeddings are implemented using Word2Vec method that follows two architectures namely CBOW or Skip Gram. Most similar semantic words are retrieved using the CBOW word embeddings method in the proposed algorithm and concatenated with the semantic keywords generated from the real estate ontology to form a powerful reformulated query that gives promising relevant results. Finally, two topmost words as per their similarity index are taken to reformulate the original user query. Experimental results depict that proposed algorithm has given distinct results and has showcased significant improvement of 93% over the initial user query.

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Improving Arabic information retrieval using word embedding similarities

Cited by 34 publications

References 39 publications

Word-embedding-based pseudo-relevance feedback for Arabic information retrieval

Word-embedding-based pseudo-relevance feedback for Arabic information retrieval

Probability model of sensitive similarity measures in information retrieval

Query Expansion based on Word Embeddings and Ontologies for Efficient Information Retrieval

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