In this work, we propose a new model for aspect-based sentiment analysis. In contrast to previous approaches, we jointly model the detection of aspects and the classification of their polarity in an end-to-end trainable neural network. We conduct experiments with different neural architectures and word representations on the recent GermEval 2017 dataset. We were able to show considerable performance gains by using the joint modeling approach in all settings compared to pipeline approaches. The combination of a convolutional neural network and fasttext embeddings outperformed the best submission of the shared task in 2017, establishing a new state of the art.
mRNA splicing is required in about 4% of protein coding genes in Saccharomyces cerevisiae. The gene structure of those genes is simple, generally comprising two exons and one intron. In order to characterize the impact of alternative splicing on the S. cerevisiae transcriptome, we perform a systematic analysis of mRNA sequencing data. We find evidence of a pervasive use of alternative splice sites and detect several novel introns both within and outside protein coding regions. We also find a predominance of alternative splicing on the 3’ side of introns, a finding which is consistent with existing knowledge on conservation of exon-intron boundaries in S. cerevisiae. Some of the alternatively spliced transcripts allow for a translation into different protein products.
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