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Identifying problems and solutions in scientific text

Author

Listed:
  • Kevin Heffernan

    (University of Cambridge)

  • Simone Teufel

    (University of Cambridge)

Abstract

Research is often described as a problem-solving activity, and as a result, descriptions of problems and solutions are an essential part of the scientific discourse used to describe research activity. We present an automatic classifier that, given a phrase that may or may not be a description of a scientific problem or a solution, makes a binary decision about problemhood and solutionhood of that phrase. We recast the problem as a supervised machine learning problem, define a set of 15 features correlated with the target categories and use several machine learning algorithms on this task. We also create our own corpus of 2000 positive and negative examples of problems and solutions. We find that we can distinguish problems from non-problems with an accuracy of 82.3%, and solutions from non-solutions with an accuracy of 79.7%. Our three most helpful features for the task are syntactic information (POS tags), document and word embeddings.

Suggested Citation

  • Kevin Heffernan & Simone Teufel, 2018. "Identifying problems and solutions in scientific text," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1367-1382, August.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:2:d:10.1007_s11192-018-2718-6
    DOI: 10.1007/s11192-018-2718-6
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    1. Kathy McKeown & Hal Daume III & Snigdha Chaturvedi & John Paparrizos & Kapil Thadani & Pablo Barrio & Or Biran & Suvarna Bothe & Michael Collins & Kenneth R. Fleischmann & Luis Gravano & Rahul Jha & B, 2016. "Predicting the impact of scientific concepts using full-text features," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(11), pages 2684-2696, November.
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    Cited by:

    1. Bowen Ma & Chengzhi Zhang & Yuzhuo Wang & Sanhong Deng, 2022. "Enhancing identification of structure function of academic articles using contextual information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 885-925, February.
    2. Guillaume Cabanac & Ingo Frommholz & Philipp Mayr, 2018. "Bibliometric-enhanced information retrieval: preface," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1225-1227, August.
    3. Yonghe Lu & Jiayi Luo & Ying Xiao & Hou Zhu, 2021. "Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6937-6963, August.
    4. Saeed-Ul Hassan & Naif R. Aljohani & Mudassir Shabbir & Umair Ali & Sehrish Iqbal & Raheem Sarwar & Eugenio Martínez-Cámara & Sebastián Ventura & Francisco Herrera, 2020. "Tweet Coupling: a social media methodology for clustering scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 973-991, August.
    5. Iqra Safder & Saeed-Ul Hassan, 2019. "Bibliometric-enhanced information retrieval: a novel deep feature engineering approach for algorithm searching from full-text publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 257-277, April.
    6. Pengcheng Li & Wei Lu & Qikai Cheng, 2022. "Generating a related work section for scientific papers: an optimized approach with adopting problem and method information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4397-4417, August.
    7. Nasrin Asadi & Kambiz Badie & Maryam Tayefeh Mahmoudi, 2019. "Automatic zone identification in scientific papers via fusion techniques," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 845-862, May.
    8. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.
    9. Luo, Zhuoran & Lu, Wei & He, Jiangen & Wang, Yuqi, 2022. "Combination of research questions and methods: A new measurement of scientific novelty," Journal of Informetrics, Elsevier, vol. 16(2).
    10. Biao Zhang & Yunwei Chen, 2024. "Automated recognition of innovative sentences in academic articles: semi-automatic annotation for cost reduction and SAO reconstruction for enhanced data," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5403-5432, September.
    11. Yingyi Zhang & Chengzhi Zhang, 2024. "Extracting problem and method sentence from scientific papers: a context-enhanced transformer using formulaic expression desensitization," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3433-3468, June.
    12. Shiyun Wang & Jin Mao & Yujie Cao & Gang Li, 2022. "Integrated knowledge content in an interdisciplinary field: identification, classification, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6581-6614, November.
    13. Gaizka Garechana & Rosa Río-Belver & Enara Zarrabeitia & Izaskun Alvarez-Meaza, 2022. "TeknoAssistant : a domain specific tech mining approach for technical problem-solving support," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5459-5473, September.
    14. Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).
    15. Fabian Stöhr, 2024. "Advancing language models through domain knowledge integration: a comprehensive approach to training, evaluation, and optimization of social scientific neural word embeddings," Journal of Computational Social Science, Springer, vol. 7(2), pages 1753-1793, October.
    16. Yi Jiang & Rui Meng & Yong Huang & Wei Lu & Jiawei Liu, 2023. "Generating keyphrases for readers: A controllable keyphrase generation framework," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(7), pages 759-774, July.
    17. Yi Zhang & Fen Zhao & Jianguo Lu, 2019. "P2V: large-scale academic paper embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 399-432, October.

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