A TF-IDF and Co-occurrence Based Approach for Events Extraction from Arabic News Corpus

Chouigui, Amina; Khiroun, Oussama Ben; Elayeb, Bilel

doi:10.1007/978-3-319-91947-8_27

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…Some work have focused on sentence-level event detection and clustering [35]- [37]. For example, Naughton et al [35] grouped sentences in news articles that refer to the same event, where non-event sentences are removed before initiating the clustering process.…”

Section: B Open-domain Event Extractionmentioning

confidence: 99%

“…Following the task definition of the TDT program, some other researches have been conducted to detect whether new articles in various websites are related to some already identified event, without using the TDT corpus [32]- [37], [238]. For example, Naughton et al [35] proposed to vectorize sentences using the bag-of-words encoding from news articles and clustered sentences via using the agglomerative hierarchical clustering algorithm [239].…”

Section: A Event Mention Detection and Trackingmentioning

confidence: 99%

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A Survey of Event Extraction From Text

2019

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Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction. Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts. As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades. This article provides a comprehensive yet up-to-date survey for event extraction from text. We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches. In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses. Last, we also present our envisions about future research directions. INDEX TERMS Event extraction, event extraction tasks, event corpus, natural language processing.

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Section: B Open-domain Event Extractionmentioning

confidence: 99%

Section: A Event Mention Detection and Trackingmentioning

confidence: 99%

A Survey of Event Extraction From Text

2019

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“…In this section, we first present the test collections used to evaluate the proposed algorithms. As there is no gold test collection to be used for the evaluation of Arabic text summarizers [27], we choose to test with our ANT corpus [19][20][21] that we present in the next sub-section. We also test our summarizers using the collection EASC [27].…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

“…To evaluate our proposed methods for automatic extractive text summarization, we have used the dataset ANT corpus v2.1 (Arabic News Texts Corpus) which is freely available online 4 (other versions of ANT corpus are also available [19][20][21]). The ANT corpus v2.1 includes data from five different news sources: AlArabiya 5 , BBC 6 , CNN 7 , France24 8 and SkyNews 9 .…”

Section: Ant Corpus Data Collectionmentioning

confidence: 99%

Automatic Arabic Text Summarization Using Analogical Proportions

et al. 2020

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Automatic text summarization is the process of generating or extracting a brief representation of an input text. There are several algorithms for extractive summarization in the literature tested by using English and other languages datasets; however, only few extractive Arabic summarizers exist due to the lack of large collection in Arabic language. This paper proposes and assesses new extractive single-document summarization approaches based on analogical proportions which are statements of the form "a is to b as c is to d". The goal is to study the capability of analogical proportions to represent the relationship between documents and their corresponding summaries. For this purpose, we suggest two algorithms to quantify the relevance/irrelevance of an extracted keyword from the input text, to build its summary. In the first algorithm, the analogical proportion representing this relationship is limited to check the existence/non-existence of the keyword in any document or summary in a binary way without considering keyword frequency in the text, whereas the analogical proportion of the second algorithm considers this frequency. We have assessed and compared these two algorithms with some languageindependent summarizers (LexRank, TextRank, Luhn and LSA (Latent Semantic Analysis)) using our large corpus ANT (Arabic News Texts) and a small test collection EASC (Essex Arabic Summaries Corpus) by computing ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (BiLingual Evaluation Understudy) metrics. The best-achieved results are ROUGE-1 = 0.96 and BLEU-1 = 0.65 corresponding to educational documents from EASC collection which outperform the best LexRank algorithm. The proposed algorithms are also compared with three other Arabic extractive summarizers, using EASC collection, and show better results in terms of ROUGE-1 = 0.75 and BLEU-1 = 0.47 for the first algorithm, and ROUGE-1 = 0.74 and BLEU-1 = 0.49 for the second one. Experimental results show the interest of analogical proportions for text summarization. In particular, analogical summarizers significantly outperform three among four language-independent summarizers in the case of BLEU-1 for ANT collection and they are not significantly outperformed by any other summarizer in the case of EASC collection.

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“…Then, content was selected based on keyword information to improve the accuracy of extracted content. Chouigui et al (2018) applied this approach to extract information about events in Arabic news using co-occurring words, varying performance by corpus type and domain. Sriram et al (2010) also explored this approach.…”

Section: Feature Extractionmentioning

confidence: 99%

Fuzzy SVM With Mahalanobis Distance for Situational Awareness-Based Recognition of Public Health Emergencies

Li,

Qu,

Lyu

et al. 2024

International Journal of Fuzzy System Applications

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In public health emergencies, situational awareness is crucial for swift responses by governments and rescue organizations. In this manuscript, a novel framework is proposed to identify and classify event-specific information, aiming to comprehend concepts, characteristics, and classifications associated with situational awareness in social media emergencies. First, a statistical approach is employed to extract a set of standard features. Second, a category-based latent dirichlet allocation to vector (LDA2vec) model is leveraged to extract topic-based features to enhance accuracy, particularly for unbalanced datasets. Finally, a fuzzy support vector machine (FSVM) classifier utilizing the Mahalanobis distance kernel is introduced to improve the detection accuracy of event-specific information. The framework's effectiveness is evaluated using the social media public health dataset, achieving superior filtering capabilities for non-informative data with a precision of 89% and an F1-Score of 91%, surpassing other standard methods.

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A TF-IDF and Co-occurrence Based Approach for Events Extraction from Arabic News Corpus

Cited by 11 publications

References 8 publications

A Survey of Event Extraction From Text

A Survey of Event Extraction From Text

Automatic Arabic Text Summarization Using Analogical Proportions

Fuzzy SVM With Mahalanobis Distance for Situational Awareness-Based Recognition of Public Health Emergencies

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