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Identifying reference spans: topic modeling and word embeddings help IR

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Abstract

The CL-SciSumm 2016 shared task introduced an interesting problem: given a document D and a piece of text that cites D, how do we identify the text spans of D being referenced by the piece of text? The shared task provided the first annotated dataset for studying this problem. We present an analysis of our continued work in improving our system’s performance on this task. We demonstrate how topic models and word embeddings can be used to surpass the previously best performing system.

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  1. https://aclweb.org/anthology/.

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Acknowledgements

We would like to thank Avisha Das, Arthur Dunbar, and Mahsa Shafaei for providing the annotations. The authors acknowledge the use of the Maxwell Cluster and the advanced support from the Center of Advanced Computing and Data Systems at the University of Houston to carry out the research presented here.

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Authors and Affiliations

  1. Department of Computer Science, University of Houston, Houston, TX, 77204, USA

    Luis Moraes, Shahryar Baki, Rakesh Verma & Daniel Lee

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  1. Luis Moraes
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  2. Shahryar Baki
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  3. Rakesh Verma
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  4. Daniel Lee
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Corresponding author

Correspondence to Luis Moraes.

Additional information

Research supported in part by NSF Grants CNS 1319212, DGE 1433817, and DUE 1241772.

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Moraes, L., Baki, S., Verma, R. et al. Identifying reference spans: topic modeling and word embeddings help IR. Int J Digit Libr 19, 191–202 (2018). https://doi.org/10.1007/s00799-017-0220-z

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  • DOI: https://doi.org/10.1007/s00799-017-0220-z

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