In the OmSieve project we applied methods of Answer Set Programming to Coreference Resolution.
Principal Investigator (Project Manager): Peter Schüller
Duration: January 2015 to December 2016
Funding: Scientific and Technological Research Council of Turkey (TÜBİTAK) Program 3001
English Title: Open-Minded Coreference Resolution Sieve Based on Answer Set Programming
Turkish Title: Çözüm Kümesi Programlama Tabanlı Muhakeme Edilen Eşgönderge Sieve Çözümlenmesi
Answer Set Programming (ASP) is a general purpose logic programming formalism that supports comfortable representation of knowledge, non-monotonic reasoning processes, and reasoning with hybrid knowledge bases. In an ASP logic program we describe (i) a set of potential solutions, (ii) relationships between concepts in the solution, and (iii) constraints on solutions. Given such a representation an ASP solver (a software tool) computes those solutions that adhere to the specified relationships and constraints. ASP solvers can find all solutions to such problems and they are engineered to find these solutions efficiently. Moreover ASP supports hybrid reasoning which means that some relationships between concepts can be described outside the ASP logic, for example in a Python program.
Coreference Resolution is the Computer Linguistics task of finding out which phrases of a natural language discourse refer to the same entity in the world. For example in the sentence “He said to the people: ‘I need your help’” the task is to find out that “he” and “I” refers to the same entity (the speaker), furthermore “the people” and “your” refers to the same entity (the listeners). Coreference Resolution is challenging: noun phrases can refer to the same entity for various reasons, they can be synonyms, hypernyms, or hyponyms, or they can be coreferent because of background knowledge and discourse information (e.g., “my brother”, “John”, and “the king” can be coreferent due to contextual information).
The OmSieve project was successfully concluded in early 2017.
- Kenda Alakraa (MSc student) obtained a scholarship from the project and graduated within the scope of the project.
- Kübra Cıngıllı (MSc student) obtained a partial scholarship from the project and assisted the creation of the Marmara Turkish Coreference Corpus.
- The CaspR – Semi-Automatic Coreference Resolution Adjudication Tool based on Answer Set Programming was created.
- The Marmara Turkish Coreference Corpus and Coreference Resolution Baseline was created and published online and as a technical report.
J Journal Article C Conference Paper E Editorship O Other
O Peter Schüller, Kübra Cingilli, Ferit Tunçer, Baris Gün Sürmeli, Aysegül Pekel, Ayse Hande Karatay, and Hacer Ezgi Karakas. Marmara Turkish Coreference Corpus and Coreference Resolution Baseline. Technical Report, Marmara University & TU Wien, , Version 2. [ arXiv ]
J Peter Schüller. Adjudication of Coreference Annotations via Answer Set Optimization. Journal of Experimental & Theoretical Artificial Intelligence 30 (4), pages 525-546, , DOI: 10.1080/0952813X.2018.1456793. [ arXiv ]
C Peter Schüller. Adjudication of Coreference Annotations via Answer Set Optimization. In: Logic Programming and Nonmonotonic Reasoning (LPNMR), volume 10377 of Lecture Notes in Computer Science, pages 343-357, , Best Application Paper, DOI: 10.1007/978-3-319-61660-5_31.
O Kenda Alakraa. Coreference Resolution Sieve based on Answer Set Programming. Masters Thesis, Marmara University, . [ PDF ]
O Barış Gün Sürmeli, Kübra Cıngıllı, Ferit Tunçer, and Peter Schüller. Turkish Coreference Annotation Manual (V2). . [ PDF ]
C Peter Schüller. Adjudication of Coreference Annotations via Finding Optimal Repairs of Equivalence Relations. In: International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA), volume 1745 of CEUR Workshop Proceedings, pages 57-71, . [ PDF ]
J Thomas Eiter, Michael Fink, Giovambattista Ianni, Thomas Krennwallner, Christoph Redl, and Peter Schüller. A Model Building Framework for Answer Set Programming with External Computations. Theory and Practice of Logic Programming 16 (04), pages 418-464, , arXiv:1507.01451 [cs.AI], DOI: 10.1017/S1471068415000113. [ link ] [ arXiv ]
J Esra Erdem, Volkan Patoglu, and Peter Schüller. A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks. AI Communications 29 (2), pages 319-349, , DOI: 10.3233/AIC-150697. [ link ] [ PDF ]
C Thomas Eiter, Christoph Redl, and Peter Schüller. Problem Solving Using the HEX Family. In: Computational Models of Rationality - Essays dedicated to Gabriele Kern-Isberner on the occasion of her 60th birthday, pages 150-174, . [ link ] [ PDF ]
O Thomas Eiter, Christoph Redl, and Peter Schüller. Problem Solving Using the HEX Family. Technical Report INFSYS RR-1843-15-07, Institut für Informationssysteme, TU Wien, , Favoritenstraße 9-11, A-1040 Vienna. [ PDF ]
C Peter Schüller and Antonius Weinzierl. Answer Set Application Programming: a Case Study on Tetris. In: International Conference on Logic Programming (ICLP), Technical Communications, volume 1433 of CEUR Workshop Proceedings, . [ PDF ] [ supporting material ]
O Barış Gün Sürmeli and Peter Schüller. Turkish Coreference Annotation Manual (V1). . [ PDF ]