Abstract
Analyzing long text articles in the pharmaceutical domain, for the purpose of knowledge extraction and recognizing entities of interest, is a tedious task. In our previous research efforts, we were able to develop a platform which successfully extracts entities and facts from pharmaceutical texts and populates a knowledge graph with the extracted knowledge. However, one drawback of our approach was the processing time; the analysis of a single text source was not interactive enough, and the batch processing of entire article datasets took too long. In this paper, we propose a modified pipeline where the texts are summarized before the analysis begins. With this, the source articles is reduced significantly, to a compact version which contains only the most commonly encountered entities. We show that by reducing the text size, we get knowledge extraction results comparable to the full text analysis approach and, at the same time, we significantly reduce the processing time, which is essential for getting both real-time results on single text sources, and faster results when analyzing entire batches of collected articles from the domain.
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The articles were retrieved from https://www.fiercepharma.com/, https://www.pharmacist.com/ and https://www.pharmaceutical-journal.com/.
References
Bizer, C., Heath, T., Idehen, K., Berners-Lee, T.: Linked data on the web. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 1265–1266. ACM, New York (2008). https://doi.org/10.1145/1367497.1367760, http://doi.acm.org/10.1145/1367497.1367760
Burtsev, M., et al.: DeepPavlov: open-source library for dialogue systems. In: Proceedings of ACL 2018, System Demonstrations, Melbourne, Australia, pp. 122–127. Association for Computational Linguistics, July 2018. https://doi.org/10.18653/v1/P18-4021, https://www.aclweb.org/anthology/P18-4021
Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist.4, 357–370 (2016). https://doi.org/10.1162/tacl_a_00104,https://www.aclweb.org/anthology/Q16-1026
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems, pp. 121–124. Association for Computing Machinery (2013)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Gardner, M., et al.: AllenNLP: A deep semantic natural language processing platform. In: Proceedings of Workshop for NLP Open Source Software (NLP-OSS), Melbourne, Australia, pp. 1–6. Association for Computational Linguistics, July 2018. https://doi.org/10.18653/v1/W18-2501, https://www.aclweb.org/anthology/W18-2501
Honnibal, M., Montani, I.: spaCy 2: Natural Language Understanding with Bloom Embeddings. Convolutional Neural Networks and Incremental Parsing (2017, to appear)
Jofche, N.: Master’s thesis: analysis of textual data in the pharmaceutical domain using deep learning. Faculty of Computer Science and Engineering (2019)
Kuru, O., Can, O.A., Yuret, D.: CharNER: character-level named entity recognition. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, The COLING 2016 Organizing Committee, Osaka, Japan, pp. 911–921. December 2016. https://www.aclweb.org/anthology/C16-1087
Lamurias, A., Couto, F.M.: LasigeBioTM at MEDIQA 2019: biomedical question answering using bidirectional transformers and named entity recognition. In: Proceedings of the 18th BioNLP Workshop and Shared Task, Florence, Italy, pp. 523–527. Association for Computational Linguistics, August 2019. https://doi.org/10.18653/v1/W19-5057, https://www.aclweb.org/anthology/W19-5057
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Baltimore, Maryland, pp. 55–60. Association for Computational Linguistics, June 2014. https://doi.org/10.3115/v1/P14-5010, https://www.aclweb.org/anthology/P14-5010
Mendes, P.N., Jakob, M., Garcia-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems (I-Semantics). Association for Computing Machinery (2011)
Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proceedings of EMNLP-04 and the 2004 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, July 2004
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411. Association for Computational Linguistics (2004)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web 8(3), 489–508 (2017)
Řehůřek, R., Sojka, P.: Software framework for topic modelling with large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, ELRA, Valletta, Malta, pp. 45–50, May 2010. http://is.muni.cz/publication/884893/en
Srinivasa-Desikan, B.: Natural Language Processing and Computational Linguistics: A Practical Guide to Text Analysis with Python, Gensim, SpaCy, and Keras. Expert insight, Packt Publishing (2018). https://books.google.mk/books?id=_tGctQEACAAJ
Steinberger, J., Ježek, K.: Using latent semantic analysis in text summarization and summary evaluation. In: Proceedings of the ISIM 2004, pp. 93–100 (2004)
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)
Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008. Curran Associates, Inc. (2017). http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
Wang, X., et al.: Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics 35(10), 1745–1752 (2019). https://doi.org/10.1093/bioinformatics/bty869
Wolf, T., et al..: Hugging face’s transformers: state-of-the-art natural language processing. ArXiv abs/1910.03771 (2019)
Zhu, F., Shen, B.: Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing. PLoS One 7, 39230 (2012)
Acknowledgement
The work in this paper was partially financed by the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje.
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Dobreva, J., Jofche, N., Jovanovik, M., Trajanov, D. (2020). Improving NER Performance by Applying Text Summarization on Pharmaceutical Articles. In: Dimitrova, V., Dimitrovski, I. (eds) ICT Innovations 2020. Machine Learning and Applications. ICT Innovations 2020. Communications in Computer and Information Science, vol 1316. Springer, Cham. https://doi.org/10.1007/978-3-030-62098-1_8
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