Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling

Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subjects and objects are in a long distance. In this paper, we propose to learn more robust relation representations from the shortest dependency path through a convolution neural network. We further propose a straightforward negative sampling strategy to improve the assignment of subjects and objects. Experimental results show that our method outperforms the state-of-the-art methods on the SemEval-2010 Task 8 dataset

Learning to Predict Charges for Criminal Cases with Legal Basis

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.Comment: 10 pages, accepted by EMNLP 201

Neighborhood Matching Network for Entity Alignment

Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge. NMN estimates the similarities between entities to capture both the topological structure and the neighborhood difference. It provides two innovative components for better learning representations for entity alignment. It first uses a novel graph sampling method to distill a discriminative neighborhood for each entity. It then adopts a cross-graph neighborhood matching module to jointly encode the neighborhood difference for a given entity pair. Such strategies allow NMN to effectively construct matching-oriented entity representations while ignoring noisy neighbors that have a negative impact on the alignment task. Extensive experiments performed on three entity alignment datasets show that NMN can well estimate the neighborhood similarity in more tough cases and significantly outperforms 12 previous state-of-the-art methods.Comment: 11 pages, accepted by ACL 202

Improving first order temporal fact extraction with unreliable data

In this paper, we deal with the task of extracting first order temporal facts from free text. This task is a subtask of relation extraction and it aims at extracting relations between entity and time. Currently, the field of relation extraction mainly focuses on extracting relations between entities. However, we observe that the multi-granular nature of time expressions can help us divide the dataset constructed by distant supervision to reliable and less reliable subsets, which can help to improve the extraction results on relations between entity and time. We accordingly contribute the first dataset focusing on the first order temporal fact extraction task using distant supervision. To fully utilize both the reliable and the less reliable data, we propose to use curriculum learning to rearrange the training procedure, label dropout to make the model be more conservative about less reliable data, and instance attention to help the model distinguish important instances from unimportant ones. Experiments show that these methods help the model outperform the model trained purely on the reliable dataset as well as the model trained on the dataset where all subsets are mixed together

Extracting Human-Exoskeleton Interaction Torque for Cable-Driven Upper-Limb Exoskeleton Equipped With Torque Sensors

Powered exoskeletons have global trends in broad applications, such as rehabilitation and human strength amplification in industry, military, and activities of daily livings. The motion intention of the exoskeleton wearer can be obtained using the interaction force at the physical human-machine interface. This article implements joint torque sensors in a custom-made cable-driven exoskeleton. The model of the torque sensor signal is established to extract the human-exoskeleton interaction (HEI) torque, which can be used to predict the human upper-limb motion intention. To accurately decouple the HEI torque from other components in the torque sensor signal, a nonlinear numerical friction model composed of the cable and joint parts is investigated based on the LuGre friction model. A protocol for parameter identification of the proposed friction model is verified experimentally. Furthermore, a coefficient combining the two friction models is designed for antagonistic directions in a joint to account for the bidirectional cable drive's backlash and hysteresis characteristics. Owing to this coefficient, the error of the friction model is reduced by approximately 90% during motion direction change. Finally, the accuracy of the torque sensor model is verified experimentally, and the root-mean-square error (RMSE) is about 0.038 N·m (2.8%). The RMSE of extracted interaction torque is about 0.25 N·m (8.1%). This article validates the feasibility of extracting HEI torque via a torque sensor implemented in the upper-limb exoskeleton, which can promote the development of new generations of upper-limb exoskeleton for active rehabilitation or assistance and research on intuitive control of exoskeleton in future.</p

Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals

Surface electromyography (sEMG) signals are crucial in developing human-machine interfaces, as they contain rich information about human neuromuscular activities. &lt;italic&gt;Objective:&lt;/italic&gt; The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons. &lt;italic&gt;Methods:&lt;/italic&gt; This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters. &lt;italic&gt;Results:&lt;/italic&gt; The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity. &lt;italic&gt;Conclusion:&lt;/italic&gt; The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals. &lt;italic&gt;Significance:&lt;/italic&gt; This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.</p