Driving style (DS) assessment plays an important role in intelligent transportation system (ITS) application, such as driving feedback provision and usage-based insurance. This paper aims to study the impact of the freight tasks (i.e., vehicle Axle, cargo type, task mileage, and freight costs) on the DS. After cleaning and processing truck GPS, Waybill, and Map, the concept of a minimal unit of DS recognition (MUDSR) is proposed and considered as a micro-DS expression unit to quantify the impact of the freight tasks on the DS. In addition, a unified expression framework, termed driving style structure (DSS), is proposed to compare and apply results of DS related studies in different conditions. The impacts of different characteristics of freight tasks on the DS are then analyzed using the DSS. The results obtained demonstrate that the freight tasks lead to an average increase of 1.58% in the proportion of safe DS in the trucks’ DDS. The safety awareness and the concern for monetary losses of truck drivers significantly affect the DSS. In addition, the freight tasks exhibiting higher costs of goods have a greater impact on the improvement of the DSS safety. Based on the results obtained in this study, the relationship between the safe driving attitude and driving performance is discussed. Relevant recommendations are also provided for freight task allocation and long-term and short-term trucker training.

Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolutional neural networks or recurrent networks). They may fail to explore the potential representation space in different abstraction levels. In this paper, we propose deep recurrent neural networks (DRNNs) for relation classification to tackle this challenge. Further, we propose a data augmentation method by leveraging the directionality of relations. We evaluated our DRNNs on the SemEval-2010 Task 8, and achieve an F1-score of 86.1%, outperforming previous state-of-the-art recorded results.

This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP.We chose two widely studied neural models and tasks as our testbed.We tried several frequently applied or newly proposed regularization strategies, including penalizing weights (embeddings excluded), penalizing embeddings, reembedding words, and dropout.We also emphasized on incremental hyperparameter tuning, and combining different regularizations.The results provide a picture on tuning hyperparameters for neural NLP models.

Question-oriented text retrieval, aka natural language-based text retrieval, has been widely used in software engineering. Earlier work has concluded that questions with the same keywords but different interrogatives (such as how, what) should result in different answers. But what is the difference? How to identify the right answers to a question? In this paper, we propose to investigate the "answer style" of software questions with different interrogatives. Towards this end, we build classifiers in a software text repository and propose a re-ranking approach to refine search results. The classifiers are trained by over 16,000 answers from the StackOverflow forum. Each answer is labeled accurately by its question's explicit or implicit interrogatives. We have evaluated the performance of our classifiers and the refinement of our re-ranking approach in software text retrieval. Our approach results in 13.1% and 12.6% respectively improvement with respect to text retrieval criteria nDCG@1 and nDCG@10 compared to the baseline. We also apply our approach to FAQs of 7 open source projects and show 13.2% improvement with respect to nDCG@1. The results of our experiments suggest that our approach could find answers to FAQs more precisely.

Learning non-taxonomic relations becomes an important research topic in ontology extension. Most of the existing learning approaches are mainly based on expert crafted corpora. These approaches are normally domain-specific and the corpora acquisition is laborious and costly. On the other hand, based on the static corpora, it is not able to meet personalized needs of semantic relations discovery for various taxonomies. In this paper, we propose a novel approach for learning non-taxonomic relations on demand. For any supplied taxonomy, it can focus on the segment of the taxonomy and collect information dynamically about the taxonomic concepts by using Wikipedia as a learning source. Based on the newly generated corpus, non-taxonomic relations are acquired through three steps: a) semantic relatedness detection; b) relations extraction between concepts; and c) relations generalization within a hierarchy. The proposed approach is evaluated on three different predefined taxonomies and the experimental results show that it is effective in capturing non-taxonomic relations as needed and has good potential for the ontology extension on demand.

Domain concepts and taxonomic relationships are an essential part of a domain ontology. They are used in a number of applications, including natural language processing, information retrieval, knowledge management and so on. Nowadays, with the continuous permeation of various kinds of Internet knowledge applications, numerous new concepts are emerged and released on to the Internet. So, the Internet has become an invaluable source of new concepts for almost every possible domain of knowledge. In order to ensure the domain ontologies keep pace with fast changing knowledge, we proposed an web searching based concepts and taxonomic relationships discovering approach. By our approach, the potential concepts on the Internet, which are taxonomically related with the give seeds concepts, can be discovered autonomously and iteratively. In this paper, the approach and a corresponding application in Chinese web pages are reported in detail. The experiments show that, our approach can catch the related domain concepts precisely, meanwhile, can reject irrelevant concepts and figure out the domain knowledge border definitely.