TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Towards Improving Causality Mining using BERT with Multi-level Feature Networks |
| 영문제목(English Title) |
Towards Improving Causality Mining using BERT with Multi-level Feature Networks |
| 저자(Author) |
Wajid Ali
Wanli Zuo
Rahman Ali
Gohar Rahman
Xianglin Zuo
Inam Ullah
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| 원문수록처(Citation) |
VOL 16 NO. 10 PP. 3230 ~ 3255 (2022. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multilevel Feature Networks (MFN) for causality recognition, called BERT MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques. |
| 키워드(Keyword) |
Causality Mining
Relation Network
Multi-level Relation Network
Relation Classification
Cause-effect Relation Classification
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| 파일첨부 |
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