Improving Query Expansion with Stemming Terms: A New Genetic Algorithm Approach

Araujo, Lourdes; Pérez-Agüera, José R.

doi:10.1007/978-3-540-78604-7_16

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…Araujo and Perezaguera [42] used a Spanish morphological thesaurus along with a stemmer. They also used the genetic algorithm to select the final query terms from the candidate terms.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Aldayel

Ykhlef

2015

Arab J Sci Eng

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Query paraphrasing aims to construct a better formulation of user queries in order to enhance retrieval. Formulating search queries remains complicated for a subset of Web users. In a typical situation, a user will not receive satisfactory results from the submitted search query and will subsequently attempt different query paraphrases. The Arabic vocabulary is rich in synonyms and hyponyms. Such richness of synonyms makes automation of the paraphrasing technique crucial for Arabic information retrieval systems in order to facilitate the process of paraphrasing synonyms. In this article, we propose an enhancement for Arabic information retrieval using a query paraphrasing technique. Furthermore, two query paraphrasing optimization techniques are proposed to overcome the time complexity and exhaustive calculation of existing query paraphrasing techniques. One of these techniques uses a genetic algorithm (GA-QP), and the other employs the artificial bee colony algorithm (ABC-QP). The performance of these two algorithms is compared. ABC-QP shows an improvement in Arabic information retrieval performance compared with the genetic algorithm query paraphrasing system. B Abeer Al-Dayel

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“…Araujo and Perezaguera [42] used a Spanish morphological thesaurus along with a stemmer. They also used the genetic algorithm to select the final query terms from the candidate terms.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Aldayel

Ykhlef

2015

Arab J Sci Eng

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“…Attempts to use genetic programming for natural language processing are uncommon, although early applications to language processing tasks can be found as early as our work using GP to aid in recovery from parser failure in speech-to-speech machine translation in the late 90s [20]. Lately, there has been a large body of work for applying genetic programming to query generation for information retrieval [6], in uses related to ours, such as query expansion [1] and tree-based genetic programming for query generation [22].…”

Section: Related Workmentioning

confidence: 99%

“…For each word that actually does occur in that document, the corresponding attribute is set to a value of 1; all other attributes are set to a value of 0. This is also known as a unigram model 1 . Interactions between simple features are useful to capture in language processing tasks.…”

Section: Related Workmentioning

confidence: 99%

Using feature construction to avoid large feature spaces in text classification

Mayfield

Penstein-Rosé

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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Feature space design is a critical part of machine learning. This is an especially difficult challenge in the field of text classification, where an arbitrary number of features of varying complexity can be extracted from documents as a preprocessing step. A challenge for researchers has consistently been to balance expressiveness of features with the size of the corresponding feature space, due to issues with data sparsity that arise as feature spaces grow larger. Drawing on past successes utilizing genetic programming in similar problems outside of text classification, we propose and implement a technique for constructing complex features from simpler features, and adding these more complex features into a combined feature space which can then be utilized by more sophisticated machine learning classifiers. Applying this technique to a sentiment analysis problem, we show encouraging improvement in classification accuracy, with a small and constant increase in feature space size. We also show that the features we generate carry far more predictive power than any of the simple features they contain.

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“…Instead, it has been suggested in previous work to use the document scores as the fitness [14]. While this may not be intuitive, it turns out that variations of these scores after expansion are correlated with relevance [1]. One intuitive explanation Table 3 Global precision results for the whole set of tested queries.…”

Section: Selecting the Fitness Functionmentioning

confidence: 99%

Structure of morphologically expanded queries: A genetic algorithm approach

Araujo

Zaragoza

Pérez-Agüera

et al. 2010

Data & Knowledge Engineering

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a b s t r a c tIn this paper we deal with two issues. First, we discuss the negative effects of term correlation in query expansion algorithms, and we propose a novel and simple method (query clauses) to represent expanded queries which may alleviate some of these negative effects. Second, we discuss a method to optimize local query-expansion methods using genetic algorithms, and we apply this method to improve stemming. We evaluate this method with the novel query representation method and show very significant improvements for the problem of stemming optimization.

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Improving Query Expansion with Stemming Terms: A New Genetic Algorithm Approach

Cited by 14 publications

References 11 publications

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Using feature construction to avoid large feature spaces in text classification

Structure of morphologically expanded queries: A genetic algorithm approach

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