Attention, intentions, and the structure of discourse
Computational Linguistics
Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Never look back: an alternative to centering
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A centering approach to pronouns
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Hierarchy of salience and discourse analysis and production
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Resolving pronominal reference to abstract entities
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A machine learning approach to pronoun resolution in spoken dialogue
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Automatic Recognition of the Function of Singular Neuter Pronouns in Texts and Spoken Data
DAARC '09 Proceedings of the 7th Discourse Anaphora and Anaphor Resolution Colloquium on Anaphora Processing and Applications
Anaphora resolution for biomedical literature by exploiting multiple resources
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
| Hi-index | 0.00 |
This paper describes an extension of the DAR-algorithm (Navarretta, 2004) for resolving intersentential pronominal anaphors referring to individual and abstract entities in texts and dialogues. In DAR individual entities are resolved combining models which identify high degree of salience with high degree of givenness (topicality) of entities in the hearer's cognitive model, e.g. (Grosz et al., 1995), with Hajičová et al.'s (1990) salience account which assigns the highest degree of salience to entities in the focal part of an utterance in Information Structure terms, which often introduce new information in discourse. Anaphors referring to abstract entities are resolved with an extension of the algorithm presented by Eckert and Strube (2000). The extended DAR-algorithm accounts for differences in the resolution mechanisms of different types of Danish pronouns. Manual tests of the algorithm show that DAR performs better than other resolution algorithms on the same data.