Graph Based Automatic Domain Term Extraction

Hema Ala, Dipti Sharma


Abstract
We present a Graph Based Approach to automatically extract domain specific terms from technical domains like Biochemistry, Communication, Computer Science and Law. Our approach is similar to TextRank with an extra post-processing step to reduce the noise. We performed our experiments on the mentioned domains provided by ICON TermTraction - 2020 shared task. Presented precision, recall and f1-score for all experiments. Further, it is observed that our method gives promising results without much noise in domain terms.
Anthology ID:
2020.icon-termtraction.1
Volume:
Proceedings of the 17th International Conference on Natural Language Processing (ICON): TermTraction 2020 Shared Task
Month:
December
Year:
2020
Address:
Patna, India
Editors:
Dipti Misra Sharma, Asif Ekbal, Karunesh Arora, Sudip Kumar Naskar, Dipankar Ganguly, Sobha L, Radhika Mamidi, Sunita Arora, Pruthwik Mishra, Vandan Mujadia
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
1–4
Language:
URL:
https://aclanthology.org/2020.icon-termtraction.1
DOI:
Bibkey:
Cite (ACL):
Hema Ala and Dipti Sharma. 2020. Graph Based Automatic Domain Term Extraction. In Proceedings of the 17th International Conference on Natural Language Processing (ICON): TermTraction 2020 Shared Task, pages 1–4, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Graph Based Automatic Domain Term Extraction (Ala & Sharma, ICON 2020)
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PDF:
https://aclanthology.org/2020.icon-termtraction.1.pdf