Mechanisms of sentence processing: assigning roles to constituents
Parallel distributed processing: explorations in the microstructure of cognition, vol. 2
Learning and applying contextual constraints in sentence comprehension
Artificial Intelligence - On connectionist symbol processing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Combining Symbolic and Neural Learning
Machine Learning
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Rule Revision With Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Natural Language Grammatical Inference with Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Parsing complex sentences with structured connectionist networks
Neural Computation
Symbolic connectionism in natural language disambiguation
IEEE Transactions on Neural Networks
Linguistic Relations Encoding in a Symbolic-Connectionist Hybrid Natural Language Processor
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
| Hi-index | 0.00 |
In linguistics, the semantic relations between words in a sentence are accounted for, inter alia, as the assignment of thematic roles, e.g. AGENT, INSTRUMENT, etc. As in predicate logic, simple linguistic expressions are decomposed into one predicate (often the verb) and its arguments. The predicate assigns thematic roles to the arguments, so that each sentence has a thematic grid, a structure with all thematic roles assigned by the predicate. In order to reveal the thematic grid of a sentence, a system called HTRP (Hybrid Thematic Role Processor) is proposed, in which the connectionist architecture has, as input, a featural representation of the words of a sentence, and, as output, its thematic grid. Both a random initial weight version (RIW) and a biased initial weight version (BIW) are proposed to account for systems without and with initial knowledge, respectively. In BIW, initial connection weights reflect symbolic rules for thematic roles. For both versions, after supervised training, a set of final symbolic rules is extracted, which is consistently correlated to linguistic - symbolic - knowledge. In the case of BIW, this amounts to a revision of the initial rules. In RIW, symbolic rules seem to be induced from the connectionist architecture and training.