Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.
Summary Our analysis of the contribution of Cyc this year showed that the major limiting factor is still in the area of coverage. Major manual effort is required both to generate appropriate semantic forms and to map to Cyc’s predicates, and also to add instance information into Cyc. With the current state of the system, Cyc helps more to improve our answer confidences (not a part of the evaluation this year) than to get answers right. The major novelty in our system this year was the implementation of QA-by-Dossier to answer Definition questions. Here, a collection of predetermined factoid questions are asked about the subject in order to gather facts that seem to be typically mentioned in definitional articles in newspapers and reference works. An advantage of this method over others which locate definitional syntactic constructs is that our system “knows” the nature of the relationship of the retrieved item to the subject. In the evaluation, we felt that our system had performed relatively well according to our expectations of what was required, but we were very disappointed to find that the NIST assessors had different opinions regarding acceptable answers. Question answering systems can benefit from the incorporation of a broad range of technologies, including natural language processing, machine learning, information retrieval, knowledge representation, and automated reasoning. We have designed an architecture that identifies the essential roles of components in a question answering system. This architecture greatly facilitates experimentation by enabling comparisons between different choices for filling the component roles, and also provides a framework for exploring hybridization of techniques ‐ that is, combining different approaches to question answering. We present results from an initial experiment that illustrate substantial performance improvement by combining statistical and linguistic approaches to question answering. We also present preliminary and encouraging results involving the incorporation of a large knowledge base. Since 1984, a person-century of effort has gone into building CYC, a universal schema of roughly 10 5 general concepts spanning human reality. Most of the time has been spent codifying knowledge about these concepts; approximately 10 6 commonsense axioms have been handcrafted for and entered into CYC's knowledge base, and millions more have been inferred and cached by CYC. This article examines the fundamental assumptions of doing such a large-scale project, reviews the technical lessons learned by the developers, and surveys the range of applications that are or soon will be enabled by the technology. 