IA161 NLP in Practice Course, Course Guarantee: Aleš Horák
Prepared by: Marek Medveď
State of the Art
Anaphora resolution (or pronoun resolution) is the problem of resolving references to earlier or later items in the discourse.
- Knowledge-rich approaches:
- Syntax-based approaches
- Discourse-Based Approaches
- Hybrid Approaches
- Corpus based Approaches
- Knowledge-poor Approaches:
- Machine learning techniques
- Anaphora Resolution, Studies in Language and Linguistics by Mitkov, R., 2014, Taylor & Francis
- A neural entity coreference resolution review. Stylianou, N. and Vlahavas, I. (2021)
- A comprehensive review on feature set used for anaphora resolution. Lata, K., Singh, P., and Dutta, K. (2020).
- Efficient and interpretable neural models for entity tracking. Toshniwal, S. (2022)
Student has to understand Hobbs' definition of anaphora resolution and according to it implement the main function of Hobbs' algorithm in the proposed python script that contains all necessary functions. According to real data (syntactic trees) students test their adjusted program and evaluate it. At the end of the session students hand in the results to prove completing the task. An additional task is to find sentence structures that are not covered by Hobbs' algorithm.
- download the script with data from here -> correct solution here
- NLTK package is required for
hobbs.py. When running at your computer, paste
pip3 install nltk --userin the terminal to install NLTK package. Faculty machines should have
- understand Hobbs' definition of anaphora resolution and replace
XXXfunction calls with correct ones
- find 20 nontrivial sentences with anaphora: 10 that Hobbs algorithm can recognize and 10 sentences it dos not. You can use the Stanford parser to test new sentences - copy the tree to one line and remove the ROOT tag.
- submit your
hobbs.pyscript with 10 examples that are correctly recognized with
hobbs.pyand 10 examples that are not correctly recognized by
hobbs.pyin the homework vault. For each unrecognized example write an explanation into one separate file
unrecognized_notes.txt(first column: example id, second column: explanation).
- execute Hobbs script:
python3 ./hobbs.py demosents.txt He