Titlebook: Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing; Stefan Wermter,Ellen Riloff,Gabriele Schel

整洁

Training stochastic grammars on semantical categories,l class. This method does not depend on any specific grammar or set of semantical categories, so it can be used on (almost) any existing system. We present experimental results that show our method gives a considerable improvement over regular stochastic grammars.

暖昧关系

休战

CLEAR

HEPA-filter

Boundary Layer Turbulence Behavior,learn about the relations within the concept and document space of an existing domain. The results are quite encouraging because with existing input data a usable representation of the knowledge space can be obtained.

即席

https://doi.org/10.1007/b137383s of word hypothesis sequences. In this paper we focus on techniques for improving the quality of pruned hypotheses from a speech recognizer using acoustic, syntactic, and semantic knowledge. We show that the developed architecture is able to cope with real-world spontaneously spoken language in an incremental and parallel manner.

窒息

facetious

Knowledge acquisition in concept and document spaces by using self-organizing neural networks,learn about the relations within the concept and document space of an existing domain. The results are quite encouraging because with existing input data a usable representation of the knowledge space can be obtained.

牌带来

Using hybrid connectionist learning for speech/language analysis,s of word hypothesis sequences. In this paper we focus on techniques for improving the quality of pruned hypotheses from a speech recognizer using acoustic, syntactic, and semantic knowledge. We show that the developed architecture is able to cope with real-world spontaneously spoken language in an incremental and parallel manner.

Enliven

Implications of an automatic lexical acquisition system,lle was thoroughly tested within a complex, real-world domain. The implementation of this system produced many lessons which are applicable to language learning in general. This paper describes Camille‘s implications for evaluation, for knowledge representation, and for cognitive modeling.