Analogical reasoning is effective in capturing linguistic regularities. This paper proposes an analogical reasoning task on Chinese. After delving into Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28 explicit semantic relations. A big and balanced dataset CA8 is then built for this task, including 17813 questions. Furthermore, we systematically explore the influences of vector representations, context features, and corpora on analogical reasoning. With the experiments, CA8 is proved to be a reliable benchmark for evaluating Chinese word embeddings.
Some of the above list may not be traditionally thought of, or referred to, as types of reasoning. It is only when approaching the practical development of an Artificial General Intelligence is it observed that each of these are approaches or methodologies that constitute a form of reasoning independent of the others. Leveraging these reasoning types and approaches, Snasci will also be quite creative, capable of lying, story telling, humour and adapting to new situations without prior training. In business and scientific applications, Snasci's reasoning and comprehension capabilities will become invaluable. In addition, the ability to connect a wide range of sensors, novel inputs and outputs (including HPC rendering) means that any lab, research and development department, university, etc., can have a world class installation and knowledge base for a fraction of what it currently costs.
Welcome to the Workshop on Spatial and Temporal Reasoning at AAAI-07 in Vancouver, British Columbia. This workshop continues in the spirit of a series of such activities over the last fifteen years spanning related communities of researchers that study representing and reasoning about either space or time or both. In addition, the workshop has encouraged a mix of theory and applied work. Various basic representational problems in space (direction, location, proximity, geometry, intersection) and in time (coincidence, order, concurrency, overlap, granularity) attract repeated attention due to their fundamental and difficult nature. Beyond that, however, the richness of different ontologies, different applications, and different objectives assures that no small collection of solutions will serve to satisfy all needs.
Reasoning about how objects move and interact in space is pervasive in evcryday life. It is rightly considered an essential component of intelligence. Consequently, understanding spatial reasoning and developing computational models for it has been a central concern in many fields, including cognitive psychology, mathematics, robotics, vision, and artificial intelligence. Although much progress has been made over the last.