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Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level(very positive, low negative, etc.) and cannot exploit the intensity information. Moreover, automatically identifying emotions like anger, fear, joy, sadness, disgust etc., from text introduces challenging scenarios where single tweet may contain multiple emotions with different intensities and some emotions may even co-occur in some of the tweets. In this paper, we propose an architecture, Experts Model, inspired from the standard Mixture of Experts (MoE) model. The key idea here is each expert learns different sets of features from the feature vector which helps in better emotion detection from the tweet. We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT). The experimental results show that our proposed approach deals with the emotion detection problem and stands at top-5 results., 10 pages, 6 figures, The 32nd Pacific Asia Conference on Language, Information and Computation (PACLIC 32)

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein\ud requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of\ud investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene\ud prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus\ud performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as\ud complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level.\ud Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length\ud cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also\ud manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene\ud database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following:\ud integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms,\ud non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein\ud three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic\ud microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB\ud analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information\ud build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates\ud (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for nonprotein-coding\ud RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within\ud human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together\ud with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing\ud phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources\ud needed for the exploration of human biology and pathology