Word-Level Contextual Sentiment Analysis with Interpretability

Word-level contextual sentiment analysis (WCSA) is an important task for mining reviews or opinions. When analyzing this type of sentiment in the industry, both the interpretability and practicality are often required. However, such a WCSA method has not been established. This study aims to develop a WCSA method with interpretability and practicality. To achieve this aim, we propose a novel neural network architecture called Sentiment Interpretable Neural Network (SINN). To realize this SINN practically, we propose a novel learning strategy called Lexical Initialization Learning (LEXIL). SINN is interpretable because it can extract word-level contextual sentiment through extracting word-level original sentiment and its local and global word-level contexts. Moreover, LEXIL can develop the SINN without any specific knowledge for context; therefore, this strategy is practical. Using real textual datasets, we experimentally demonstrate that the proposed LEXIL is effective for improving the interpretability of SINN and that the SINN features both the high WCSA ability and high interpretability

Estimation of Cross-Lingual News Similarities Using Text-Mining Methods

In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM) based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method

Estimation of Cross-Lingual News Similarities Using Text-Mining Methods

In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM) based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method

Zn(OTf)(2)-mediated annulations of N-propargylated tetrahydrocarbolines: divergent synthesis of four distinct alkaloidal scaffolds

Intramolecular hydroarylations of N-propargylated tetrahydrocarbolines were efficiently mediated using a unique combination of Zn(OTf) 2 with t-BuOH under neutral conditions. Use of the artificial force induced reaction method in the global reaction route mapping strategy provided insights into the Zn(OTf) 2-mediated hydroarylations and the associated intriguing solvent effects of t-BuOH facilitating a protodezincation process without a Bronsted acid activator. We systematically implemented three distinct hydroarylations as well as an unanticipated a-alkenylation of a carbonyl group to obtain the four alkaloidal scaffolds 2-4, and 18. Zn(OTf) 2-mediated annulation of 1c proceeded through kinetic formation of the spiroindole 3c followed by an alkenyl shift and concomitant retro-Mannich-type fragmentation to furnish azepino[ 4,5-b] indole 2 framework. Substituents on substrate 1 in the vicinity of the reaction sites substantially affected the mode of the divergent annulations. Judicious choices of the substituents, solvent and reaction conditions enabled programmable divergent synthesis of the four distinct skeletons

C3H/HeNSlc mouse with low phospholipid transfer protein expression showed dyslipidemia

Abstract High serum levels of triglycerides (TG) and low levels of high-density lipoprotein cholesterol (HDL-C) increase the risk of coronary heart disease in humans. Herein, we first reported that the C3H/HeNSlc (C3H-S) mouse, a C3H/HeN-derived substrain, is a novel model for dyslipidemia. C3H-S showed hypertriglyceridemia and low total cholesterol (TC), HDL-C, and phospholipid (PL) concentrations. To identify the gene locus causing dyslipidemia in C3H-S, we performed genetic analysis. In F2 intercrosses between C3H-S mice and strains with normal serum lipids, the locus associated with serum lipids was identified as 163–168 Mb on chromosome 2. The phospholipid transfer protein (Pltp) gene was a candidate gene within this locus. Pltp expression and serum PLTP activity were markedly lower in C3H-S mice. Pltp expression was negatively correlated with serum TG and positively correlated with serum TC and HDL-C in F2 mice. Genome sequencing analysis revealed that an endogenous retrovirus (ERV) sequence called intracisternal A particle was inserted into intron 12 of Pltp in C3H-S. These results suggest that ERV insertion within Pltp causes aberrant splicing, leading to reduced Pltp expression in C3H-S. This study demonstrated the contribution of C3H-S to our understanding of the relationship between TG, TC, and PL metabolism via PLTP