Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results.Comment: 12 pages for EMNLP 201

Autonomous Ground Vehicle

WildCat is an autonomous ground vehicle (AGV). AGVs were first developed for military purposes: Intelligent Transportation Systems (ITS), Manufacturing, Search and Rescue operations, Mining, etc. WildCat will be entered in the Intelligent Ground Vehicle competition (IGVC) held in June 2016 at Oakland University in Rochester, Michigan. Teams from major universities not only in the U.S., but also India, France, the UK, China, and around the world will be competing. The IGVC offers a design experience that is at the very cutting edge of engineering education. It is multidisciplinary, theory-based, hands-on, team implemented, and outcome assessed competition. It encompasses the very latest technologies impacting industrial development and taps subjects of high interest to students. The objective of the competition is to challenge students to think creatively as a team about the evolving technologies of vehicle electronic controls, sensors, computer science, robotics, and system integration throughout the design, fabrication, and field testing of autonomous intelligent mobile robots. The vehicle will compete to: 1) autonomously navigate an outdoor obstacle course as quickly as possible, keeping within the speed limit and reaching all GPS waypoints, 2) complete a course with remote (user) control, and 3) have ingenuity and uniqueness in design

Seasonality and frequency of snake and goanna incursions on an Australian agricultural institute and the attitudes of staff regarding coexistence

A number of snakes and goannas persist in areas frequented by humans, such as the Elizabeth Macarthur Agricultural Institute in New South Wales, Australia. This institute is used as both an office location and an agricultural demonstration site, which inevitably results in snakes and goannas coming close to institute buildings, prompting concerns for human safety. Reports of snakes and goannas within 100m of institute buildings were collected over a three-year period; an analysis of those data identified a seasonal pattern of incursions by three species between September and March. A short verbal survey revealed that more staff had encountered goannas than snakes. Most were cautious to curious when snakes and goannas were reported, whereas only a minority admitted to being fearful when snakes were reported