Changes between Version 1 and Version 2 of private/NlpInPracticeCourse/AnaphoraResolution

May 31, 2015, 12:49:11 PM (9 years ago)



  • private/NlpInPracticeCourse/AnaphoraResolution

    v1 v2  
    1616Approx 3 current papers (preferably from best NLP conferences/journals, eg. [[|ACL Anthology]]) that will be used as a source for the one-hour lecture:
    18  1. paper1
    19  1. paper2
    20  1. paper3
     18 1. Anaphora Resolution, Studies in Language and Linguistics by Mitkov, R., 2014, Taylor & Francis, ISBN 9781317881810
     19 1. Anaphora resolution: the state of the art, Ruslan Mitkov,1999, Citeseer
     20 1. Strategies of anaphora resolution, Tanya Reinhart, 2006, North Holland, [[|Source]]
     21 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, [[| Source]]
     22 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, [[|Source]]
     23 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, [[|Source]]
    2224== Practical Session ==
    24 Concrete description of work assignment for students for the second one-hour part of the lecture. The work will consist of tasks connected with practical implementations of algorithms connected with the current topic (probably not the state-of-the-art algorithms mentioned in the first part) and with real data. Students can test the algorithms, evaluate them and possibly try some short adaptations for various subtasks.
    26 Students can also be required to generate some results of their work and hand them in to prove completing the tasks.
     26Student has to understand Hobbs' definition of anaphora resolution and according to it implement the main function of Hobbs' algorithm in proposed python script that contains all necessary functions. According to real data (syntactic trees) student tests his program and evaluate it. At the and of the session student has to hand the results to prove completing the task. If the student finishes early the additional task is to find sentence structures that are not covered by Hobbs' algorithm.