Meyer, MelinaFrey, JennyLaub, TaminoWrzalik, MarcoKrechel, DirkReussner, Ralf H.Koziolek, AnneHeinrich, Robert2021-01-272021-01-272021978-3-88579-701-2https://dl.gi.de/handle/20.500.12116/34795Citation recommendation aims to predict references based on a given text. In this paper, we focus on predicting references using small passages instead of a whole document. Besides using a search engine as baseline, we introduce two further more advanced approaches that are based on neural networks. The first one aims to learn an alignment between a passage encoder and reference embeddings while using a feature engineering approach including a simple feed forward network. The second model takes advantage of BERT, a state-of-the-art language representation model, to generate context-sensitive passage embeddings. The predictions of the second model are based on inter-passage similarities between the given text and indexed sentences, each associated with a set of references. For training and evaluation of our models, we prepare a large dataset consisting of English papers from various scientific disciplines.encitation recommendationnatural language processingrepresentation learning10.18420/inf2020_821617-5468