Abstract Anaphora Resolution [source code]
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. The folder Silver Data...
Saved in:
| Main Author: | |
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| Format: | Database Research Data |
| Language: | English |
| Published: |
Heidelberg
Universität
2019-02-04
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| DOI: | 10.11588/data/UDMPY5 |
| Subjects: | |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.11588/data/UDMPY5 Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/UDMPY5 
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| Author Notes: | Ana Marasovic |
MARC
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| 520 | |a 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. 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. 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). 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. The implementation for training and evaluating models presented in the thesis may be found in the Thesis folder. | ||
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