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Towards Idea Mining: Problem-Solution Phrase Extraction fromText

Liu, Haixia; Brailsford, Tim; Goulding, James; Maul, Tomas; Tan, Tao; Chaudhuri, Debanjan

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Haixia Liu

Tim Brailsford

Tomas Maul

Tao Tan

Debanjan Chaudhuri


This paper investigates the feasibility of problem-solution phrases extraction from scientific publications using neural network approaches. Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM-CRFs) and Bidirectional Encoder Representations from Transformers (BERT) were evaluated on two datasets, one of which was created by University of Cambridge Computer Laboratory containing 1000 positive examples of problems and solutions (UCCL1000) with the corresponding phrases annotated. The F1-scores computed on the UCCL1000 dataset indicate that BERT is an effective approach to extract solution phrases (with an F1-score of 97%) and problem phrases (with an F1-score of 83%). To test the model’s robustness on a different corpus with a different annotation scheme, a dataset consisting of 488 problem-solution samples from the Conference on Neural Information Processing Systems (NIPS488) was collected and annotated by human readers. Both Bi-LSTM-CRFs and BERT performances were dramatically lower for NIPS488 in comparison with UCCL1000.

Conference Name 18th International Conference Advanced Data Mining and Applications (ADMA 2022)
Start Date Nov 28, 2022
End Date Nov 30, 2022
Acceptance Date Nov 10, 2022
Online Publication Date Nov 24, 2022
Publication Date Nov 24, 2022
Deposit Date Jan 16, 2023
Publicly Available Date Nov 25, 2023
Publisher Springer
Volume 13726 LNAI
Pages 3-14
Series Title Lecture notes in computer science
Series ISSN 1611-3349
Book Title Advanced Data Mining and Applications 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28–30, 2022: Proceedings, Part II
ISBN 9783031221361
Keywords Text mining, Problem-solution extraction, NLP
Public URL
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