3,239 research outputs found
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Predicting protein properties such as solvent accessibility and secondary
structure from its primary amino acid sequence is an important task in
bioinformatics. Recently, a few deep learning models have surpassed the
traditional window based multilayer perceptron. Taking inspiration from the
image classification domain we propose a deep convolutional neural network
architecture, MUST-CNN, to predict protein properties. This architecture uses a
novel multilayer shift-and-stitch (MUST) technique to generate fully dense
per-position predictions on protein sequences. Our model is significantly
simpler than the state-of-the-art, yet achieves better results. By combining
MUST and the efficient convolution operation, we can consider far more
parameters while retaining very fast prediction speeds. We beat the
state-of-the-art performance on two large protein property prediction datasets.Comment: 8 pages ; 3 figures ; deep learning based sequence-sequence
prediction. in AAAI 201
Are Word Embedding-based Features Useful for Sarcasm Detection?
This paper makes a simple increment to state-of-the-art in sarcasm detection
research. Existing approaches are unable to capture subtle forms of context
incongruity which lies at the heart of sarcasm. We explore if prior work can be
enhanced using semantic similarity/discordance between word embeddings. We
augment word embedding-based features to four feature sets reported in the
past. We also experiment with four types of word embeddings. We observe an
improvement in sarcasm detection, irrespective of the word embedding used or
the original feature set to which our features are augmented. For example, this
augmentation results in an improvement in F-score of around 4\% for three out
of these four feature sets, and a minor degradation in case of the fourth, when
Word2Vec embeddings are used. Finally, a comparison of the four embeddings
shows that Word2Vec and dependency weight-based features outperform LSA and
GloVe, in terms of their benefit to sarcasm detection.Comment: The paper will be presented at Conference on Empirical Methods in
Natural Language Processing (EMNLP) 2016 in November 2016.
http://www.emnlp2016.net
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