Event Detection and Information Extraction Strategies from Text: A Preliminary Study Using GENIA Corpus
In the world we live today, data is the new oil. Data can reveal hidden knowledge that gives us an advantage over our competitors. However, data that are present in an unstructured form such as text documents are difficult to be processed by conventional machine learning algorithms. Therefore, in th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Springer Science and Business Media Deutschland GmbH
2023
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| Online Access: | http://scholars.utp.edu.my/id/eprint/34246/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145000035&doi=10.1007%2f978-3-031-20429-6_12&partnerID=40&md5=343151f1085f32b2981696e042b54683 |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | In the world we live today, data is the new oil. Data can reveal hidden knowledge that gives us an advantage over our competitors. However, data that are present in an unstructured form such as text documents are difficult to be processed by conventional machine learning algorithms. Therefore, in this study, we attempted to perform information extraction from textual data using current and state-of-the-art models to understand their working mechanisms. To perform this study, we have chosen the GENIA corpus for evaluating the performance of each model. These selected event extraction models are evaluated based on specific measures which are precision, recall, and F-1 measure. The result of our study shows that the DeepEventMine model has scored the highest for trigger detection with a precision of 79.17, recall at 82.93, and F-1 measure at 81.01. Similarly, for event detection, the DeepEventMine model has scored highest among other models with a precision of 65.24, recall at 55.93, and F-1 measure at 60.23 based on the selected corpus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
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