An algorithm for pronominal anaphora resolution
Computational Linguistics
Cognitive Status, Information Structure, and Pronominal Reference to Clausally Introduced Entities
Journal of Logic, Language and Information
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Resolving pronominal reference to abstract entities
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Indirect anaphora resolution as semantic path search
Proceedings of the 3rd international conference on Knowledge capture
Neural Networks: Algorithms and Applications
Neural Networks: Algorithms and Applications
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Hindi paired word recognition using probabilistic neural network
International Journal of Computational Intelligence Studies
International Journal of Innovative Computing and Applications
Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools
Expert Systems with Applications: An International Journal
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In this paper, we propose the application of probabilistic neural networks (PNNs) to the classification scheme of demonstrative pronouns for indirect anaphora in Hindi corpus. The Demonstrative Pronouns in Hindi, ''yeh''(this/it), ''veh''(that/those), ''iss''(this/it), and ''uss''(that/those) can be personal or demonstrative. The differentiation can be ascertained from only the situation or the context. The case marking of pronouns further add the constraints on linguistic patterns. We propose to cast such an anaphora as a semantic inference process, which encompasses several salient linguistic characteristic features such as grammatical role, proximity, syntactic category and semantic cues. Our focus of study is demonstrative pronouns without noun phrase antecedent in Hindi written corpus. We analyzed 313 news items having 3890 sentences, 3101 pronouns, of which 608 instances covered those demonstrative pronouns, which had 183 instances with non-NP-antecedents. The effectiveness of the approach is demonstrated through set of simulations and evaluations.