= Anaphora resolution =

[[https://is.muni.cz/auth/predmet/fi/ia161|IA161]] [[en/NlpInPracticeCourse|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. [[BR]]
Main approaches: 
1. Knowledge-rich approaches:
     1. Syntax-based approaches
     2. Discourse-Based Approaches 
     3. Hybrid Approaches
     4. Corpus based Approaches
1. Knowledge-poor Approaches:
     1. Machine learning techniques
=== References ===

 1. Anaphora Resolution, Studies in Language and Linguistics by Mitkov, R., 2014, Taylor & Francis
 1. Caw-coref: Conjunction-aware word-level coreference resolution. D’Oosterlinc at all. (2023)
 1. F-coref: Fast, accurate and easy to use coreference resolution. Otmazgin, S., Cattan, A., and Goldberg, Y. (2022)
 1. Efficient and interpretable neural models for entity tracking. Toshniwal, S. (2022)
 1. Coreference resolution through a seq2seq transition-based system. Bohnet, B., Alberti, C., and Collins, M. (2022)

== Practical Session ==

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.

The task:
 1. download the script with data from [[raw-attachment:hobbs.zip|here]] ->> correct solution  [[raw-attachment:hobbs_correct.py|here]]
 1. NLTK package is required for `hobbs.py`. When running at your computer, paste 
    {{{
pip3 install nltk --user
}}} 
    in the terminal to install NLTK package. Faculty machines should have `nltk` already installed.
 1. understand Hobbs' definition of anaphora resolution and replace `XXX` function calls with correct ones
 1. find 20 nontrivial sentences with anaphora: 10 that Hobbs algorithm can recognize and 10 sentences it dos not. You 
 can use [https://nlp.fi.muni.cz/projekty/qa/parser/ the Stanford parser] 
 to test new sentences - copy the tree into file formated into one line.
 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''). 

Commands:
 1. execute Hobbs script: 
    {{{
python3 ./hobbs.py demosents.txt He
}}}