Stemming and Lemmatization for Information Retrieval Systems in Amazigh Language

Amri, Samir; Zenkouar, Lahbib

doi:10.1007/978-3-319-96292-4_18

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Morphology and syntactic variation of words should be normalized in an efficient way in order to reduce the curse of dimensionality and increasing performance. Authors in [25] did not handled the morphology, while in [6] stemming has been used, but studies in [28], and [29] show that lemmatization provides good performance as compared to the stemming. After preprocessing, some tweets become too short to contain sufficient information (e.g., zero or single word length).…”

Section: Related Workmentioning

confidence: 99%

RweetMiner: Automatic identification and categorization of help requests on twitter during disasters

Ullah,

Khan,

Imran

et al. 2023

Preprint

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Catastrophic events create uncertain environments in which it becomes very difficult to locate affected people and provide aids. People turn to Twitter during disasters for requesting help and/or providing relief to others than their friends and family. A huge number of posts issued online for seeking help could not properly be detected and remained concealed because tweets are noisy and stinky. Existing systems lack in planning an effective strategy for tweet preprocessing and grasping the contexts of tweets. This research first of all formally define request tweet in the context of social networking sites, so-called rweets, along with its different primary types and sub-types. Then the work delves into tweets for identification and categorization of rweets. For rweet identification, the precision of 99.7% achieved using the rule-based approach and F1measure of 82.38% achieved using logistic regression. Logistic regression also outperformed by gaining an excellent F1-measure of 94.95% in rweet categorization by classifying rweets into medical, volunteer, cloth, food, shelter, and money. Compared to the previous studies, a significant performance improvement is achieved for both identification and classification of rweets. We also introduced an architecture to store intermediate data to accelerate the machine learning classifiers' development process.

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Section: Related Workmentioning

confidence: 99%

RweetMiner: Automatic identification and categorization of help requests on twitter during disasters

Ullah,

Khan,

Imran

et al. 2023

Preprint

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show abstract

“…24 Then remove stop-word removal by removing the common words which do not have any discriminative information from a feature space. 25 Finally we apply lemmatization process 26 that is the process of transforming the terms of a sentence to its base form. To eliminate inflectional endings and return the basic form of a term which is referred to as lemma, it is important to examine the morphological analysis of each term.…”

Section: Text Preprocessingmentioning

confidence: 99%

A new feature selection method based on frequent and associated itemsets for text classification

Farghaly

El‐Hafeez

2022

Concurrency and Computation

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SummaryFeature selection is one of the major issues in pattern recognition. The quality of selected features is important for classification as the low‐quality data can degrade the model construction performance. Due to the difficulty of dealing with the problem that selected features always contain redundant information, this article focuses on the association analysis theory in data mining to select important features. In this study, a novel feature selection method based on frequent and associated itemsets (FS‐FAI) for text classification is proposed. FS‐FAI seeks to find relevant features and also takes feature interaction into account. Moreover, it uses association as a metric to evaluate the relativity between the target concept and feature(s). To evaluate the efficacy of the proposed method, several experiments were conducted on a BBC dataset from the BBC news website and SMS spam collection dataset from the UCI machine learning repository. The obtained results were compared to well‐known feature selection methods. The reported results demonstrated the effectiveness of the proposed feature selection method in selecting high‐quality features and in handling redundant information in text classification.

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“…processes for generating root words. The root type of an inflected word is produced by both stemming and lemmatization (Samir & Lahbib, 2018). Stemming computation works by removing the word's postfix.…”

Section: Deriving Root Wordmentioning

confidence: 99%

Untitled

2022

3C TIC

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In this paper, we investigate the existence of fixed points for Suzuki nonexpansive mappings in the setting of Banach spaces using the asymptotic center technique. We also establish the convergence of regular approximate fixed point sequence to the fixed points of Suzuki nonexpansive mappings. Examples are also given to illustrate the results. Our theorems generalize several results in the literature.

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Stemming and Lemmatization for Information Retrieval Systems in Amazigh Language

Cited by 11 publications

References 5 publications

RweetMiner: Automatic identification and categorization of help requests on twitter during disasters

RweetMiner: Automatic identification and categorization of help requests on twitter during disasters

A new feature selection method based on frequent and associated itemsets for text classification

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