AKEA: An Arabic Keyphrase Extraction Algorithm

Amer, Eslam; Foad, Khaled

doi:10.1007/978-3-319-48308-5_14

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…Preprocessing data is considered a vital stage in natural language processing applications such as keyphrase extraction [25][26][27] and search engine optimization [17,28]. Consequently, false news identification directly affects the model's efficacy concerning the data's complexity.…”

Section: Data Preprocessingmentioning

confidence: 99%

Context-Based Fake News Detection Model Relying on Deep Learning Models

2022

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Nowadays, due to the great accessibility to the internet, people seek out and consume news via social media due to its low cost, ease of access, and quick transmission of information. The tremendous leverage of social media applications in daily life makes them significant information sources. Users can post and share different types of information in all their forms with a single click. However, the cost becomes expensive and dangerous when non-experts say anything about anything. Fake news are rapidly dominating the dissemination of disinformation by distorting people’s views or knowledge to influence their awareness and decision-making. Therefore, we have to identify and prevent the problematic effects of falsified information as soon as possible. In this paper, we conducted three experiments with machine learning classifiers, deep learning models, and transformers. In all experiments, we relied on word embedding to extract contextual features from articles. Our experimental results showed that deep learning models outperformed machine learning classifiers and the BERT transformer in terms of accuracy. Moreover, results showed almost the same accuracy between the LSTM and GRU models. We showed that by combining an augmented linguistic feature set with machine or deep learning models, we can, with high accuracy, identify fake news.

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Section: Data Preprocessingmentioning

confidence: 99%

Context-Based Fake News Detection Model Relying on Deep Learning Models

2022

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“…In general, Experiments and comparison studies with widely used systems suggest that KP-Miner is effective and efficient. [27] introduced AKEA, a keyphrase extractionunsupervised-algorithm for single Arabic documents. They relied on heuristics that collaborate linguistic patterns based on Part-Of-Speech (POS) tags, statistical knowledge and the internal structural pattern of terms.…”

Section: Extraction Arabic Abstractmentioning

confidence: 99%

The Impact of Online Indexing in Improving Arabic Information Retrieval Systems

Dilekh¹,

Benharzallah²,

Behloul³

2018

IJCAI

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This paper suggests a new type of indexing Arabic Language text that contribute to improving the quality of IRS. The proposed method of indexing belongs to semi-automatic category of indexing and consists of two types. The first type conducts an online indexing and the output of this process give a rise to a Partial index. The second typeunder this method-is an offline indexing and the output of this process leads to a General index. We illustrate application and the performance of this new method of indexing using an Arabic text editor and Information Retrieval tool developed and designed for this purpose. We also illustrate the process of building a new form of Arabic corpus appropriate to conduct the necessary experiments. Our findings show that the online indexing model successfully identifies the descriptors most relevant to the document. In addition, this model is more efficient as it helps minimizing index storage size, consequently, improving the response time of the different requests. Finally, the paper proposes a solution to issues and deficiencies Arabic language processing suffers from, especially regarding corpora building and information retrieval evaluation systems. Povzetek: V prispevku je predlagan nov način indeksiranja arabskih besedil z namenom izboljšanja jezikovno-računalniških operacij.

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