TY  - JOUR
Y1  - 2023///
UR  - 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
JF  - Lecture Notes in Networks and Systems
A1  - Abdullah, M.H.A.
A1  - Aziz, N.
A1  - Abdulkadir, S.J.
A1  - Akhir, E.A.P.
A1  - Talpur, N.
VL  - 573 LN
EP  - 127
AV  - none
N1  - cited By 2; Conference of 2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022 ; Conference Date: 2 September 2022 Through 3 September 2022; Conference Code:287819
N2  - 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.
ID  - scholars19418
TI  - Event Detection and Information Extraction Strategies from Text: A Preliminary Study Using GENIA Corpus
SP  - 118
ER  -