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)

Joint RNN Model for Argument Component Boundary Detection

Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline. Existing ACBD methods heavily depend on task-specific knowledge, and require considerable human efforts on feature-engineering. To tackle these problems, in this work, we formulate ACBD as a sequence labeling problem and propose a variety of Recurrent Neural Network (RNN) based methods, which do not use domain specific or handcrafted features beyond the relative position of the sentence in the document. In particular, we propose a novel joint RNN model that can predict whether sentences are argumentative or not, and use the predicted results to more precisely detect the argument component boundaries. We evaluate our techniques on two corpora from two different genres; results suggest that our joint RNN model obtain the state-of-the-art performance on both datasets.Comment: 6 pages, 3 figures, submitted to IEEE SMC 201

Comprehensive evaluation of RNA-seq quantification methods for linearity

Figure S3. Concordant analysis between rank of estimated quantifications and rank of measured abundance value at gene level (a) and isoform level (b). The fitted value in the y-axis is estimated from model D∼m×A+n×B+ε. Ranks were normalized by the number of quantifications in each plot. (PDF 5950 kb

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

Wet adhesion properties of oilseed proteins stimulated by chemical and physical interactions and bonding

Doctor of PhilosophyDepartment of Grain Science and IndustryX. Susan SunThe ecological and public health liabilities related with consuming petroleum resources have inspired the development of sustainable and environmental friendly materials. Plant protein, as a byproduct of oil extraction, has been identified as an economical biomaterial source and has previously demonstrated excellent potential for commercial use. Due to the intrinsic structure, protein-based materials are vulnerable to water and present relatively low wet mechanical properties. The purpose of this study focuses on increasing protein surface hydrophobicity through chemical modifications in order to improve wet mechanical strength. However, most of the water sensitive groups (WSG), such as amine, carboxyl, and hydroxyl groups, are also attributed to adhesion. Therefore, the goal of this research is to reduce water sensitive groups to an optimum level that the modified soy protein presents good wet adhesion and wet mechanical strength. In this research, we proposed two major approaches to reduce WSG: 1). By grafting hydrophobic chemicals onto the WSGs on protein surface; 2). By interacting hydrophobic chemicals with the WSGs. For grafting, undecylenic acid (UA), a castor oil derivative with 11-carbon chain with a carboxyl group at one end and naturally hydrophobic, was used. Carboxyl groups from UA reacted with amine groups from protein and converted amines into ester with hydrophobic chains grafting on protein surface. The successful grafting of UA onto soy protein isolate (SPI) was proved by both Infrared spectroscopy (IR) and ninhydrin test. Wood adhesive made from UA modified soy protein had reached the highest wet strength of 3.30 ± 0.24 MPa with fiber pulled out, which was 65% improvement than control soy protein. Grafting fatty acid chain was verified to improve soy protein water resistance. For interaction approach, soy oil with three fatty acid chains was used to modify soy protein. Soy oil was first modified into waterborne polyurethanes (WPU) to improve its compatibility and reactivity with aqueous protein. The main forces between WPU and protein were hydrogen bonding, hydrophobic interactions, and physical entanglement. Our results showed that WPU not only increased protein surface hydrophobicity with its fatty acid chains but also enhanced the three-dimensional network structure in WPU-SPI adhesives. WPU modification had increased wet adhesion strength up to 3.81 ± 0.34 MPa with fiber pulled out compared with 2.01 ± 0.46 MPa of SPI. Based on IR and thermal behavior changes observed by DSC, it was inferred that a new crosslinking network formed between WPU and SPI. To exam if the UA and WPU technologies developed using soy protein are suitable for other plant proteins, we selected camelina protein because camelina oil has superior functional properties for jet fuels and polymers. Like soy protein, camelina protein is also highly water sensitive. However, simply applied UA and WPU to camelina protein following the same methods used for soy proteins, we did not obtain the same good adhesion results compared to what we achieved with soy protein. After protein structure analysis, we realized that camelina protein is more compact in structure compared to soy protein that made it weak in both dry and wet adhesion strength. Therefore, for camelina protein, we unfolded its compact structure with Polymericamine epichlorohydrine (PAE) first to improve flexible chains with more adhesion groups for future reaction with UA or WPU. PAE with charged groups interacted camelina protein through electrostatic interaction and promoted protein unfolding to increase reactivity within protein subunits and between protein and wood cells. Therefore, the wet adhesion strength of camelina protein was improved from zero to 1.30 ± 0.23 MPa, which met the industrial standard for plywood adhesives in terms of adhesion strength. Then the wet adhesion strength of camelina protein was further improved after applying UA and WPU into the PAE modified camelina protein. In addition, we also found PAE unfolding significantly improved the dry adhesion strength of camelina protein from 2.39 ± 0.52 to 5.39 ± 0.50 MPa with 100% wood failure on two-layer wood test. Camelina meal which is even more economical than camelina protein was studied as wood adhesive. Through a combination of PAE and laccase modification method, the wet adhesion strength of camelina meal was improved as high as 1.04 ± 0.19MPa, which also met industrial standards for plywood adhesives. The results of this study had proven successful modification of oilseed protein to increase water resistance and wet mechanical strength. We have gained in-depth understanding of the relationship between protein structure and wet adhesion strength. The successful modification of plant proteins meeting the industrial needs for bio-adhesives will promote the development of eco-friendly and sustainable materials

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

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity

A study on compressive anisotropy and nonassociated flow plasticity of the AZ31 Magnesium Alloy in hot rolling

Effect of anisotropy in compression is studied on hot rolling of AZ31 magnesium alloy with a three-dimensional constitutive model based on the quadratic Hill48 yield criterion and nonassociated flow rule (non-AFR). The constitutive model is characterized by compressive tests of AZ31 billets since plastic deformations of materials are mostly caused by compression during rolling processes. The characterized plasticity model is implemented into ABAQUS/Explicit as a user-defined material subroutine (VUMAT) based on semi-implicit backward Euler\u27s method. The subroutine is employed to simulate square-bar rolling processes. The simulation results are compared with rolled specimens and those predicted by the von Mises and the Hill48 yield function under AFR. Moreover, strip rolling is also simulated for AZ31 with the Hill48 yield function under non-AFR. The strip rolling simulation demonstrates that the lateral spread generated by the non-AFR model is in good agreement with experimental data. These comparisons between simulation and experiments validate that the proposed Hill48 yield function under non-AFR provides satisfactory description of plastic deformation behavior in hot rolling for AZ31 alloys in case that the anisotropic parameters in the Hill48 yield function and the non-associated flow rule are calibrated by the compressive experimental results