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Part 1: Document Description
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Citation |
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Title: |
Abstract Anaphora Resolution [Source Code] |
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Identification Number: |
doi:10.11588/data/UDMPY5 |
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Distributor: |
heiDATA |
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Date of Distribution: |
2019-02-04 |
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Version: |
1 |
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Bibliographic Citation: |
Marasovic, Ana, 2019, "Abstract Anaphora Resolution [Source Code]", https://doi.org/10.11588/data/UDMPY5, heiDATA, V1 |
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Citation |
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Title: |
Abstract Anaphora Resolution [Source Code] |
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Identification Number: |
doi:10.11588/data/UDMPY5 |
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Authoring Entity: |
Marasovic, Ana (Department of Computational Linguistics, Heidelberg University, Germany) |
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Other identifications and acknowledgements: |
Anette Frank |
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Other identifications and acknowledgements: |
Leo Born |
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Other identifications and acknowledgements: |
Juri Opitz |
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Distributor: |
heiDATA |
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Access Authority: |
Marasovic, Ana |
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Holdings Information: |
https://doi.org/10.11588/data/UDMPY5 |
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Study Scope |
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Keywords: |
Computer and Information Science |
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Abstract: |
Abstract Anaphora Resolution (AAR) aims to find the interpretation of nominal expressions (e.g., this result, those two actions) and pronominal expressions (e.g., this, that, it) that refer to abstract-object-antecedents such as facts, events, plans, actions, or situations. <p> <p> The folder Silver Data contains the code for processing the silver training data described in Marasović et al. (2017). For more information read Silver Data/README.<p> <p> The folder Gold Data contains the code for processing the gold training and evaluation data. Use Gold Data/process_aar_data.py to prepare the ASN corpus (Kolhatkar et al, 2013) and the CoNLL-12 shared task data (Jauhar et al, 2015). Read arrau_csn/instructions_arrau_construction.txt for processing of the ARRAU corpus (Poesio et al, 2018).<p> <p> The implementation for training and evaluating models presented in Marasović et al. (2017) maybe be found in the folder EMNLP 2017. The readme contains the information on how to run the training and evaluation scripts. <p> <p> The implementation for training and evaluating models presented in the thesis may be found in the Thesis folder. <p> <p> |
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Methodology and Processing |
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Sources Statement |
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Data Sources: |
Jauhar, S. K., Guerra, R., Gonzàlez Pellicer, E., and Recasens, M. (2015). Resolving Discourse-Deictic Pronouns: A Two-Stage Approach to Do It. In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, pages 299–308, Denver, Colorado. |
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Kolhatkar, V., Zinsmeister, H., and Hirst, G. (2013). Interpreting Anaphoric Shell Nouns using Antecedents of Cataphoric Shell Nouns as Training Data. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 300–310, Seattle, Washington, USA. |
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Poesio, M., Grishina, Y., Kolhatkar, V., Moosavi, N., Roesiger, I., Roussel, A., Simonjetz, F., Uma, A., Uryupina, O., Yu, J., and Zinsmeister, H. (2018). Anaphora Resolution with the ARRAU Corpus. In Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference, pages 11–22. Association for Computational Linguistics. |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Marasović A., Born L., Opitz J., and Frank A. (2017). A Mention-Ranking Model for Abstract Anaphora Resolution. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP). Copenhagen, Denmark. |
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Identification Number: |
http://aclweb.org/anthology/D/D17/D17-1021.pdf |
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Bibliographic Citation: |
Marasović A., Born L., Opitz J., and Frank A. (2017). A Mention-Ranking Model for Abstract Anaphora Resolution. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP). Copenhagen, Denmark. |
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Label: |
AbstractAnaphoraResolution.zip |
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Notes: |
application/zip |