Using Semi-Supervised Learning and Wikipedia to Train an Event Argument Extraction System

Zajec, Patrik; Mladenić, Dunja

doi:10.31449/inf.v46i1.3577

Cited by 2 publications

(4 citation statements)

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“…The accuracy of their model ranged from 78% to 83%, which is typical when examining the hundreds of tweets that supported and endorsed the Islamic State (commonly known as ISIS). Using Wikipedia, Wikidata, and semi-supervised learning, Zajec et al [26] created a system that tests earthquakes and terrorist attacks. The study automatically generated a small noisy, labeled dataset and a large unlabeled dataset using Wikipedia and Wikidata.…”

Section: A Machine Learning Approach For Enhancing Defense Against Gl...mentioning

confidence: 99%

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Abdalsalam,

Li,

Dahou

et al. 2024

CMES

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One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database (GTD) can influence the accuracy of the model's classification of terrorist attacks, where each part of the data can provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomy has one or more tags attached to it, referred as "related tags." We applied machine learning classifiers to classify terrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts and learns contextual features from text attributes to acquire more information from text data. The extracted contextual features are combined with the "key features" of the dataset and used to perform the final classification. The study explored different experimental setups with various classifiers to evaluate the model's performance. The experimental results show that the proposed framework outperforms the latest techniques for classifying terrorist attacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

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Section: A Machine Learning Approach For Enhancing Defense Against Gl...mentioning

confidence: 99%

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Abdalsalam,

Li,

Dahou

et al. 2024

CMES

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“…In this framework, sentences in the unannotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. Zajec and Mladenić (2022) iteratively labeled unlabeled data using semi-supervised learning combined with probabilistic soft logic, inferring the pseudo-tokens of each instance from the predictions of multiple base learners. The proposed methodology is applied to Wikipedia pages about earthquakes and terrorist attacks in a cross-lingual setting.…”

Section: Semi-supervised and Distant-supervised Event Extractionmentioning

confidence: 99%

“…Our work fully considers all the meta-events related to the topic in the document to avoid this situation. (Zajec & Mladenić, 2022) used semi-supervised method and integrates cross-language data into the learning process, enhancing the pseudo-annotation supported by probabilistic soft logic. Moreover, to avoid manually annotating data when extracting event argument, (Zajec & Mladenić, 2022) combined Wikipedia and Wikidata to obtain the labeled data.…”

Section: Theoretical and Practical Contributionsmentioning

confidence: 99%

“…(Zajec & Mladenić, 2022) used semi-supervised method and integrates cross-language data into the learning process, enhancing the pseudo-annotation supported by probabilistic soft logic. Moreover, to avoid manually annotating data when extracting event argument, (Zajec & Mladenić, 2022) combined Wikipedia and Wikidata to obtain the labeled data. The method takes into account the subject, language, and argument in the annotation corpus, but unfortunately this work focuses only on argument extraction and ignores what we think is the most important and fundamental work of event extraction-event detection.…”

Section: Theoretical and Practical Contributionsmentioning

confidence: 99%

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Semi-Supervised Event Extraction Incorporated With Topic Event Frame

Miao

et al. 2023

Journal of Database Management

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Supervised Meta-event extraction suffers from two limitations: (1) The extracted meta-events only contain local semantic information and do not present the core content of the text; (2) model performance is easily degraded because of labeled samples with insufficient number and poor quality. To overcome these limitations, this study presents an approach called frame-incorporated semi-supervised topic event extraction (FISTEE), which aims to extract topic events containing global semantic information. Inspired by the frame-based knowledge representation, a topic event frame is developed to integrate multiple meta-events into a topic event. Combined with the tri-training algorithm, a strategy for selecting unlabeled samples is designed to expand the training sets, and labeling models based on conditional random field (CRF) are constructed to label meta-events. The experimental results show that the event extraction performance of FISTEE is better than supervised learning-based approaches. Furthermore, the extracted topic events can present the core content of the text.

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Using Semi-Supervised Learning and Wikipedia to Train an Event Argument Extraction System

Cited by 2 publications

References 0 publications

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Semi-Supervised Event Extraction Incorporated With Topic Event Frame

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