Connectionism and cognitive architecture: a critical analysis
Connections and symbols
Implications of recursive distributed representations
Advances in neural information processing systems 1
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Artificial Intelligence - On connectionist symbol processing
Learning and applying contextual constraints in sentence comprehension
Artificial Intelligence - On connectionist symbol processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Mechanisms of sentence processing: assigning roles to constituents
Parallel distributed processing
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks
Machine Learning - Connectionist approaches to language learning
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
Subsymbolic natural language processing: an integrated model of scripts, lexicon, and memory
Subsymbolic natural language processing: an integrated model of scripts, lexicon, and memory
The effect of anaphor and ellipsis resolution on proximity searching in a text database
Information Processing and Management: an International Journal
Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
Language acquisition from sparse input without error feedback
Neural Networks
Trajectories through Knowledge Space: A Dynamic Framework for Machine Comprehension
Trajectories through Knowledge Space: A Dynamic Framework for Machine Comprehension
Information Retrieval
Representing Natural Language with Prolog
IEEE Software
Structuring Knowledge In Vague Domains
IEEE Transactions on Knowledge and Data Engineering
An architecture for anaphora resolution
ANLC '88 Proceedings of the second conference on Applied natural language processing
A connectionist model of some aspects of anaphor resolution
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Anaphora resolution: a multi-strategy approach
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
Journal of Artificial Intelligence Research
Anaphors, PPs and disambiguation process for conceptual analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Symbolic connectionism in natural language disambiguation
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Holographic reduced representations
IEEE Transactions on Neural Networks
Stability properties of labeling recursive auto-associative memory
IEEE Transactions on Neural Networks
Automatic discourse structure detection using shallow textual continuity
International Journal of Human-Computer Studies
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This paper presents an explicit connectionist-inspired, language learning model in which the process of settling on a particular interpretation for a sentence emerges from the interaction of a set of 驴soft驴 lexical, semantic, and syntactic primitives. We address how these distinct linguistic primitives can be encoded from different modular knowledge sources but strongly involved in an interactive processing in such a way as to make implicit linguistic information explicit. The learning of a quasi-logical form, called context-dependent representation, is inherently incremental and dynamical in such a way that every semantic interpretation will be related to what has already been presented in the context created by prior utterances. With the aid of the context-dependent representation, the capability of the language learning model in text understanding is strengthened. This approach also shows how the recursive and compositional role of a sentence as conveyed in the syntactic structure can be modeled in a neurobiologically motivated linguistics based on dynamical systems rather on combinatorial symbolic architecture. Experiments with more than 2,000 sentences in different languages illustrating the influences of the context-dependent representation on semantic interpretation, among other issues, are included.