Crowdsourcing Argumentation Structures in Chinese Hotel Reviews

Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.Comment: 6 pages,3 figures,This article has been submitted to "The 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC2017)

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Epidemiological and transcriptome data identify potential key genes involved in iron overload for type 2 diabetes

Abstract Background Many previous studies have reported the association between iron overload (IO) and type 2 diabetes mellitus (T2DM). However, the underlying molecular mechanism is not clear. Methods Epidemiological data from the National Health and Nutrition Examination Survey 2017–2018 (NHANES) was used to systematically explore the association between IO and diabetes. Furthermore, transcriptome data from Gene Expression Omnibus (GEO) were analyzed using bioinformatics methods to explore the underlying functional mechanisms at the molecular level. Results Data from NHANES showed a “W” shape relationship between serum iron (frozen) and the risk of diabetes (P < 0.001) as well as a “∧” shape correlation between serum unsaturated iron binding capacity (UIBC) and the risk of diabetes (P = 0.007). Furthermore, the serum iron (frozen) was positively associated with fasting plasma glucose and HOMAB (P < 0.05), and UIBC was positively associated with fasting insulin (P < 0.05). Transcriptome data showed that two IO-related genes [Transferrin receptor (TFRC) and Solute carrier family-11 member-2 (SLC11A2)] were down-regulated in T2DM. The correlation analysis showed that expression levels of TFRC and SLC11A2 were significantly and positively correlated with genes involved in insulin secretion (P < 0.05). Protein–protein interaction network analysis showed that TFRC and SLC11A2 interacted with four key genes, including VAMP2, HIF1A, SLC2A1, and RAB11FIP2. Conclusion We found that IO status was associated with increased FPG and aggravated HOMAB, and two IO-related genes (TFRC and SLC11A2) might induce the occurrence of T2DM by influencing insulin secretion, which provides potential therapeutic targets for T2DM patients