Abstract
Keyphrase extraction can provide effective ways of organizing scientific documents. For this task, neural-based methods usually suffer from performance unstability due to data scarcity. In this paper, we adopt the pipeline two-step method including candidate extraction and keyphrase ranking, where candidate extraction is a key to influence the whole performance. In the candidate extraction step, to overcome the low-recall problem of traditional rule-based method, we propose a novel semi-supervised data augmentation method, where a neural-based tagging model and a discriminative classifier boost each other and get more confident phrases as candidates. With more reasonable candidates, keyphrase are identified with recall promoted. Experiments on SemEval 2017 Task 10 show that our model can achieve competitive results.
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Acknowledgement
We thank the anonymous reviewers for their insightful comments on this paper. This work was partially supported by National Natural Science Foundation of China (61572049 and 61273278).
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Liu, Q., Kawahara, D., Li, S. (2018). Scientific Keyphrase Extraction: Extracting Candidates with Semi-supervised Data Augmentation. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_16
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