Name
Abstract | ||
|---|---|---|
Recommending citations for scientific texts and other texts such as news articles has recently attracted considerable amount of attention. However, typically, the existing approaches for citation recommendation do not explicitly incorporate the question of whether a given context (e.g., a sentence), for which citations are to be recommended, actually "deserves" citations. Determining the "cite-worthiness" for each potential citation context as a step before the actual citation recommendation is beneficial, as (1) it can reduce the number of costly recommendation computations to a minimum, and (2) it can more closely approximate human-citing behavior, since neither too many nor too few recommendations are provided to the user. In this paper, we present a method based on a convolutional recurrent neural network for classifying potential citation contexts. Our experiments show that we can significantly outperform the baseline solution [1] and reduce the number of citation recommendations to about 1/10. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-76941-7_50 | ADVANCES IN INFORMATION RETRIEVAL (ECIR 2018) |
Keywords | Field | DocType |
Citation context,Citation recommendation,Recommender systems,Deep learning | Recommender system,Information retrieval,Computer science,Citation,Recurrent neural network,Citation context,Artificial intelligence,Deep learning,Sentence | Conference |
Volume | ISSN | Citations |
10772 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Färber | 1 | 56 | 22.11 |
| Alexander Thiemann | 2 | 2 | 1.04 |
| Adam Jatowt | 3 | 903 | 106.73 |