Changes between Version 21 and Version 22 of private/NlpInPracticeCourse/AnaphoraResolution

Timestamp:

Dec 7, 2021, 8:39:17 PM (2 years ago)

Author:

xmedved1

Comment:

--

private/NlpInPracticeCourse/AnaphoraResolution

@@ -18 +18 @@
 === References ===
 
- 1. Anaphora Resolution, Studies in Language and Linguistics by Mitkov, R., 2014, Taylor & Francis, ISBN 9781317881810
- 1. Anaphora resolution: the state of the art, Ruslan Mitkov,1999, Citeseer
- 1. Strategies of anaphora resolution, Tanya Reinhart, 2006, North Holland, [[http://dspace.library.uu.nl/handle/1874/17181|Source]]
- 1. Discriminative Approach to Predicate-argument Structure Analysis with Zero-anaphora Resolution, Kenji Imamura and Kuniko Saito  and Tomoko Izumi, 2009, Association for Computational Linguistics, ACMID 1667611, [[http://dl.acm.org/citation.cfm?id=1667583.1667611| Source]]
- 1. The Influence of Minimum Edit Distance on Reference Resolution, Michael Strube and Stefan Rapp and Christoph Muller, EMNLP 2002, Association for Computational Linguistics, ACMID 1118733, [[http://dx.doi.org/10.3115/1118693.1118733|Source]]
- 1. Combining Sample Selection and Error-driven Pruning for Machine Learning of Coreference Rules, Vincent Ng and Claire Cardie, EMNLP 2002, Association for Computational Linguistics, ACMID 1118701, [[http://dx.doi.org/10.3115/1118693.1118701|Source]]
+ 1. Anaphora Resolution, Studies in Language and Linguistics by Mitkov, R., 2014, Taylor & Francis
+ 1. Wikicrem: A large unsupervised corpus for coreference resolution, Kocijan, V., Camburu, O.-M., Cretu, A.-M., Yordanov, Y., Blunsom, P., and Lukasiewicz, T. (2019).
+ 1. A comprehensive review on feature set used for anaphora resolution, Lata, K., Singh, P., and Dutta, K. (2020).
+ 1. The referential reader: A recurrent entity network for anaphora resolution, Liu, F., Zettlemoyer, L., and Eisenstein, J. (2019).
+ 1. Petra: A sparsely supervised memory model for people tracking, Toshniwal, S., Ettinger, A., Gimpel, K., and Livescu, K. (2020).
+
 
 == Practical Session ==
@@ -34 +34 @@
  1. understand Hobbs' definition of anaphora resolution and replace `XXX` function calls with correct ones
  1. find 20 nontrivial sentences wit anaphora: 10 that Hobbs algorithm can recognize and 10 sentences it dos not. You 
- can use [http://nlp.stanford.edu:8080/parser/index.jsp the Stanford parser] 
+ can use [https://nlp.fi.muni.cz/projekty/qa/parser/ the Stanford parser] 
  to test new sentences - copy the tree to one line and remove the ROOT tag.
  1. submit your `hobbs.py` script with 10 examples that are correctly recognized with `hobbs.py` and 10 examples that are not correctly recognized by `hobbs.py` in the homework vault. For each unrecognized example write an explanation into one separate file `unrecognized_notes.txt` (first column: ''example id'', second column: ''explanation'').