This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentence as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.

Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). Recently, many methods have made progress on these two tasks. However, few works aim at extracting opinion targets and opinion words as pairs. In this paper, we propose a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target. A target-fused sequence labeling neural network model is designed to perform this task. The opinion target information is well encoded into context by an Inward-Outward LSTM. Then left and right contexts of the opinion target and the global context are combined to find the corresponding opinion words. We build four datasets for TOWE based on several popular ABSA benchmarks from laptop and restaurant reviews. The experimental results show that our proposed model outperforms the other compared methods significantly. We believe that our work may not only be helpful for downstream sentiment analysis task, but can also be used for pair-wise opinion summarization.

Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in the pipeline. However, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios. To this end, we propose a novel tagging scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end fashion only with one unified grid tagging task. Additionally, we design an effective inference strategy on GTS to exploit mutual indication between different opinion factors for more accurate extractions. To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiL-STM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets. Extensive experimental results indicate that GTS models outperform strong baselines significantly and achieve state-of-the-art performance.

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Textile electrodes are becoming an attractive means in the facilitation of surface electrical stimulation. However, the stimulation comfort of textile electrodes and the mechanism behind stimulation discomfort is still unknown. In this study, a textile stimulation electrode was developed using conductive fabrics and then its impedance spectroscopy, stimulation thresholds, and stimulation comfort were quantitatively assessed and compared with those of a wet textile electrode and a hydrogel electrode on healthy subjects. The equivalent circuit models and the finite element models of different types of electrode were built based on the measured impedance data of the electrodes to reveal the possible mechanism of electrical stimulation pain. Our results showed that the wet textile electrode could achieve similar stimulation performance as the hydrogel electrode in motor threshold and stimulation comfort. However, the dry textile electrode was found to have very low pain threshold and induced obvious cutaneous painful sensations during stimulation, in comparison to the wet and hydrogel electrodes. Indeed, the finite element modeling results showed that the activation function along the z direction at the depth of dermis epidermis junction of the dry textile electrode was significantly larger than that of the wet and hydrogel electrodes, thus resulting in stronger activation of pain sensing fibers. Future work will be done to make textile electrodes have similar stimulation performance and comfort as hydrogel electrodes.

Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA, which aims to extract the corresponding opinion words for a given opinion target in a sentence. Recently, neural network methods have been applied to this task and achieve promising results. However, the difficulty of annotation causes the datasets of TOWE to be insufficient, which heavily limits the performance of neural models. By contrast, abundant review sentiment classification data are easily available at online review sites. These reviews contain substantial latent opinions information and semantic patterns. In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. To address the challenges in the transfer process, we design an effective transformation method to obtain latent opinions, then integrate them into TOWE. Extensive experimental results show that our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge. Further analysis validates the effectiveness of our model.

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Chemical oxidation is a promising pretreatment step coupled with bioremediation for removal of polycyclic aromatic hydrocarbons (PAHs). The effectiveness of Fenton, modified Fenton, potassium permanganate and activated persulfate oxidation treatments on the real contaminated soils collected from a coal gas plant (263.6 ± 73.3 mg kg −1 of the Σ16 PAHs) and a coking plant (385.2 ± 39.6 mg kg −1 of the Σ16 PAHs) were evaluated. Microbial analyses showed only a slight impact on indigenous microbial diversity by Fenton treatment, but showed the inhibition of microbial diversity and delayed population recovery by potassium permanganate reagent. After potassium permanganate treatment, the microorganism mainly existed in the soil was Pseudomonas or Pseudomonadaceae. The results showed that total organic carbon (TOC) content in soil was significantly increased by adding modified Fenton reagent (1.4%-2.3%), while decreased by adding potassium permanganate (0.2%-1%), owing to the nonspecific and different oxidative properties of chemical oxidant. The results also demonstrated that the removal efficiency of total PAHs was ordered: permanganate (90.0%-92.4%) > activated persulfate (81.5%-86.54%) > modified Fenton (81.5%-85.4%) > Fenton (54.1%-60.0%). Furthermore, the PAHs removal efficiency was slightly increased on the 7th day after Fenton and modified Fenton treatments, about 14.6%, and 14.4% respectively, and the PAHs removal efficiency only enhanced 4.1% and 1.3% respectively from 1st to 15th day after potassium permanganate and activated persulfate treatments. The oxidants greatly affect the growth of soil indigenous microbes, which cause further influence for PAHs degradation by bioremediation.