Abstract
As more papers get included in Digital collections satisfying information needs is becoming harder. In particular, when the user searches for information beyond bibliographic metadata. The situation is even worse when the information need requires a key aspect of a paper that first needs to be annotated for indexing purposes and thus, allow searching. For instance, in the biomedical field this might apply to structured abstracts, e.g. ‘background’, ‘objectives’, ‘results’, ‘methods’ and ‘conclusion’. Current state-of-the-art deep learning approaches can only succeed if a sufficiently large amount of annotated data is available for training purposes. However, annotating several thousands of documents is not only expensive, but due to the limited availability of experts often even infeasible. To alleviate this problem, we explore the use of Language Inference as a universal feature that once applied to a limited number of annotated documents can help to achieve high accuracy to generate the desired metadata. We show through our experiments the degree of success on the difficult task of generating the structured metadata of biomedical papers and its performance stability as we increase the number of examples. We compare our suggested approach with deep learning approaches such as Doc2Vec and show that language inference is up to two orders of magnitude better achieving up to 0.82 F1 scores.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bojanowski, P., et al.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2016)
Bowman, S.R., et al.: A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics (2015)
Cer, D., et al.: Universal sentence encoder. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 169–174. Association for Computational Linguistics, Brussels, Belgium (2018)
Conneau, A., et al.: Supervised learning of universal sentence representations from natural language inference data. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 670–680. Association for Computational Linguistics, Copenhagen, Denmark (2017)
Faruqui, M., et al.: Retrofitting word vectors to semantic lexicons. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1606–1615. Association for Computational Linguistics (2015)
Gan, Z., et al.: Learning generic sentence representations using convolutional neural networks. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2390–2400. Association for Computational Linguistics, Copenhagen, Denmark (2017)
González Pinto, J.M., Balke, W.-T.: Can plausibility help to support high quality content in digital libraries? In: TPDL 2017 – 21st International Conference on Theory and Practice of Digital Libraries, pp. 169–180. Springer International Publishing, Thessaloniki, Greece (2017)
González Pinto, J.M., Balke, W.-T.: Assessing plausibility of scientific claims to support high-quality content in digital collections. Int. J. Digit. Libr. 19(59), 1–14 (2018)
Haynes, R.B., et al.: More informative abstracts revisited. Ann. Intern. Med. 113(1), 69–76 (1990)
Hayward, R.S.A., et al.: More informative abstracts of articles describing clinical practice guidelines. Ann. Intern. Med. 118(9), 731–737 (1993)
Kalchbrenner, N., Blunsom, P.: Recurrent continuous translation models. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1700–1709. Association for Computational Linguistics, Seattle, Washington, USA (2013)
Kilicoglu, H., et al.: SemMedDB: a PubMed-scale repository of biomedical semantic predications. J. Bioinform. 28(23), 3158–3160 (2012)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha, Qatar (2014)
Kiros, R., et al.: Skip-thought vectors. In: Cortes, C., et al. (eds.) Advances in Neural Information Processing Systems, 28, pp. 3294–3302. Curran Associates Inc, Red Hook (2015)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Jebara, E.P.X. (ed.) International Conference on Machine Learning - ICML 2014, pp. 1188–1196. PMLR, Bejing, China (2014)
Lev, G., et al.: In defense of word embedding for generic text representation. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9103, 35–50 (2015)
Mikolov, T., et al.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, pp. 3111–3119. Curran Associates Inc., Lake Tahoe, Nevada (2013)
Mikolov, T., et al.: Efficient estimation of word representations in vector space. In: Proceedings of the International Conference on Learning Representations (ICLR 2013), pp. 1–12. arXiv, Scottsdale, Arizona USA (2013)
Pennington, J., et al.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (2014)
Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 2227–2237. Association for Computational Linguistics, New Orleans, Louisiana (2018)
Arora, S., Liang, Y., Tengyu, M.: A simple but tough-to-beat baseline for sentence embeddings. In: 5th International Conference on Learning Representations (ICLR 2017), Toulon, France (2017)
Sutskever, I., et al.: Sequence to Sequence Learning with Neural Networks. NIPS, 9 (2014)
Toepfer, M., Seifert, C.: Content-based quality estimation for automatic subject indexing of short texts under precision and recall constraints. In: Méndez, E., et al. (eds.) Digital Libraries for Open Knowledge (TPDL 2018), pp. 3–15. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-030-00066-0_1
De Vine, L., et al.: Medical semantic similarity with a neural language model. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 1819–1822. ACM, New York, NY, USA (2014)
Wawrzinek, J., Balke, W.-T.: Semantic facettation in pharmaceutical collections using deep learning for active substance contextualization. In: Choemprayong, S., Crestani, F., Cunningham, S.J. (eds.) ICADL 2017. LNCS, vol. 10647, pp. 41–53. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70232-2_4
Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. In: Proceedings of the 8th International Joint Conference on Natural Language Processing, pp. 253–263. Asian Federation of Natural Language Processing, Taipei, Taiwan (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pinto, J.M.G., Wawrzinek, J., Kori, S., Balke, WT. (2019). Can Language Inference Support Metadata Generation?. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science(), vol 11799. Springer, Cham. https://doi.org/10.1007/978-3-030-30760-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-30760-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30759-2
Online ISBN: 978-3-030-30760-8
eBook Packages: Computer ScienceComputer Science (R0)