757 research outputs found
Prosodic Event Recognition using Convolutional Neural Networks with Context Information
This paper demonstrates the potential of convolutional neural networks (CNN)
for detecting and classifying prosodic events on words, specifically pitch
accents and phrase boundary tones, from frame-based acoustic features. Typical
approaches use not only feature representations of the word in question but
also its surrounding context. We show that adding position features indicating
the current word benefits the CNN. In addition, this paper discusses the
generalization from a speaker-dependent modelling approach to a
speaker-independent setup. The proposed method is simple and efficient and
yields strong results not only in speaker-dependent but also
speaker-independent cases.Comment: Interspeech 2017 4 pages, 1 figur
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages
We present a transition-based dependency parser that uses a convolutional
neural network to compose word representations from characters. The character
composition model shows great improvement over the word-lookup model,
especially for parsing agglutinative languages. These improvements are even
better than using pre-trained word embeddings from extra data. On the SPMRL
data sets, our system outperforms the previous best greedy parser (Ballesteros
et al., 2015) by a margin of 3% on average.Comment: Accepted in ACL 2017 (Short
A General-Purpose Tagger with Convolutional Neural Networks
We present a general-purpose tagger based on convolutional neural networks
(CNN), used for both composing word vectors and encoding context information.
The CNN tagger is robust across different tagging tasks: without task-specific
tuning of hyper-parameters, it achieves state-of-the-art results in
part-of-speech tagging, morphological tagging and supertagging. The CNN tagger
is also robust against the out-of-vocabulary problem, it performs well on
artificially unnormalized texts
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