A Supervised Machine Learning Approach for Events Extraction out of Arabic Tweets
| Autor: | Mohammad Smadi, Omar Qawasmeh |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Event (computing) Arabic business.industry Feature extraction Noisy text Machine learning computer.software_genre language.human_language Task (project management) Identification (information) language Task analysis Artificial intelligence Representation (mathematics) business computer |
| Zdroj: | SNAMS |
| DOI: | 10.1109/snams.2018.8554560 |
| Popis: | Tweets provide a continuous update on daily events, however they are noisy text, personalized and challenging to be understood by machines. This shows a need for event extraction and representation approaches. This research describes a state-of-the-art supervised machine learning approach for extracting events out of Arabic tweets. The proposed approach focuses on three research tasks: Task 1: Event Trigger Extraction, Task 2: Event Time Expression Extraction, Task 3: Event Type Identification. The proposed approach was evaluated on a dataset of 2k Arabic tweets and the evaluation results were promising. The approach performance was compared to an unsupervised rule-based approach from previous work using the same dataset. Results show that the proposed approach outperforms the unsupervised rule-based approach in tasks T1:event trigger extraction (F-1= 92.6 vs. F-1= 78.7) and T2:event time expression extraction (F-1= 92.8 vs. F-1= 88.35), whereas is acting relatively worse in T3: event type identification (Accuracy= 80.1 vs. Accuracv= 95.9). |
| Databáze: | OpenAIRE |
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