16,765 research outputs found
Extracting finite structure from infinite language
This paper presents a novel connectionist memory-rule based model capable of learning the finite-state properties of an input language from a set of positive examples. The model is based upon an unsupervised recurrent self-organizing map [T. McQueen, A. Hopgood, J. Tepper, T. Allen, A recurrent self-organizing map for temporal sequence processing, in: Proceedings of Fourth International Conference in Recent Advances in Soft Computing (RASC2002), Nottingham, 2002] with laterally interconnected neurons. A derivation of functionalequivalence theory [J. Hopcroft, J. Ullman, Introduction to Automata Theory, Languages and Computation, vol. 1, Addison-Wesley, Reading, MA, 1979] is used that allows the model to exploit similarities between the future context of previously memorized sequences and the future context of the current input sequence. This bottom-up learning algorithm binds functionally related neurons together to form states. Results show that the model is able to learn the Reber grammar [A. Cleeremans, D. Schreiber, J. McClelland, Finite state automata and simple recurrent networks, Neural Computation, 1 (1989) 372–381] perfectly from a randomly generated training set and to generalize to sequences beyond the length of those found in the training set
Self-Adaptive Hierarchical Sentence Model
The ability to accurately model a sentence at varying stages (e.g.,
word-phrase-sentence) plays a central role in natural language processing. As
an effort towards this goal we propose a self-adaptive hierarchical sentence
model (AdaSent). AdaSent effectively forms a hierarchy of representations from
words to phrases and then to sentences through recursive gated local
composition of adjacent segments. We design a competitive mechanism (through
gating networks) to allow the representations of the same sentence to be
engaged in a particular learning task (e.g., classification), therefore
effectively mitigating the gradient vanishing problem persistent in other
recursive models. Both qualitative and quantitative analysis shows that AdaSent
can automatically form and select the representations suitable for the task at
hand during training, yielding superior classification performance over
competitor models on 5 benchmark data sets.Comment: 8 pages, 7 figures, accepted as a full paper at IJCAI 201
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