Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.Comment: To be published in EMNLP 201

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201

Optimal Allocation of Virtual Inertia and Droop Control for Renewable Energy in Stochastic Look-Ahead Power Dispatch

To stabilize the frequency of the renewable energy sources (RESs) dominated power system, frequency supports are required by RESs through virtual inertia emulation or droop control in the newly published grid codes. Since the long-term RES prediction involves significant errors, we need online configure the frequency control parameters of RESs in a rolling manner to improve the operation economics under the premise of stabilizing system frequency. To address this concern, this paper proposes a frequency constrained stochastic look-ahead power dispatch (FCS-LAPD) model to formulate the frequency control parameters of RESs and Energy Storage Systems (ESSs) as scheduling variables, which can optimally allocate the virtual inertia and droop coefficient of RESs and ESSs. In this FCS-LAPD model, the uncertainties of RESs are characterized using Gaussian Mixture Model (GMM). The required reserves are determined by frequency control parameters, and the reserve cost coefficients are adjusted properly to allocate the reserves according to the predicted power generation. Due to the nonlinearity of the frequency nadir constraint, a convex hull approximation method is proposed to linearize it with guaranteed feasibility. The proposed FCS-LAPD is ultimately cast as an instance of quadratic programming and can be efficiently solved. Case studies on modified IEEE 24-bus system and a provincial power system in China are conducted to show the effectiveness of the proposed model

Factors on perceived waiting time and implications on passengers’ satisfaction with waiting time

In order to explore the influence factors on perceived waiting time, a multiple linear regression model is used to quantitatively describe the relationship between perceived waiting time and various factors. The model is established with 234 data, which is surveyed with questionnaire in three stops in Harbin, China. The results show that several certain factors (“trip purpose-to where”, “presence of a companion-weather have a companion or not”, “having a timing device-weather have a timing device or not”, “riding frequency-how many times to take one line per week” and “waiting behavior-what to do when wait for a bus”) have significant influence on perceived waiting time, which confirms previous findings and supports transferability of results. The significance of “waiting mood-how about the mood when wait for a bus” and “reserved waiting time-how long will wait” are confirmed for the first time in this study. In contrast to previous studies, “waiting time interval-what time of one day” is a negative variable and socioeconomic variables are non-significant. And it is found that the relationship between perceived waiting time and passengers’ satisfaction with waiting time follows a decreasing exponential distribution. With this model, the variation trend of the section, where passengers’ satisfaction value is larger than 0, is obviously steeper than the section less than 0. Such result proves that passengers’ mood with short time are more sensitive than with longer waiting time. And the borderline perceived waiting time, distinguishing satisfied from dissatisfied passengers, is proved to be 7.87 minutes when assignment interval of satisfaction is (-25,25], when satisfaction is positive (larger than 0), the accuracy is 70.30%, while the accuracy is 82.71% for negative satisfaction (less than 0)