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It is necessary to understand the semantic information in various scenarios. Relation classification (RC) is an import method to implement the description of semantic formalization. It aims at classifying a relation between two specified entities in a sentence. Existing RC models typically rely on supervised learning and distant supervision. Supervised learning requires large-scale supervised training datasets, which are not readily available. Distant supervision introduces noise, and many long-tail relations still suffer from data sparsity. Few-shot learning, which is widely used in image classification, is an effective method for overcoming data sparsity. In this paper, we apply few-shot learning to a relation classification task. However, not all instances contribute equally to the relation prototype in a text-based few-shot learning scenario, which can cause the prototype deviation problem. To address this problem, we propose context attention-based prototypical networks. We design context attention to highlight the crucial instances in the support set to generate a satisfactory prototype. Besides, we also explore the application of a recently popular pre-trained language model to few-shot relation classification tasks. The experimental results demonstrate that our model outperforms the state-of-the-art models and converges faster.<\/jats:p>","DOI":"10.1186\/s13638-020-01720-6","type":"journal-article","created":{"date-parts":[[2020,6,8]],"date-time":"2020-06-08T09:05:57Z","timestamp":1591607157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Few-shot relation classification by context attention-based prototypical networks with BERT"],"prefix":"10.1186","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5759-3562","authenticated-orcid":false,"given":"Bei","family":"Hui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhui","family":"Nian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,8]]},"reference":[{"issue":"1","key":"1720_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.26599\/BDMA.2018.9020028","volume":"2","author":"C Kong","year":"2019","unstructured":"C. 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