We present a method for finding (STATE, EVENT) pairs where EVENT can change STATE. For example, the event “realize” can put an end to the states “be unaware”, “be confused”, and “be happy”; while it can rarely affect “being hungry”. We extract these pairs from a large corpus using a fixed set of syntactic dependency patterns. We then apply a supervised Machine Learning algorithm to clean the results using syntactic and collocational features, achieving a precision of 78% and a recall of 90%. We observe 3 different relations between states and events that change them and present a method for using Mechanical Turk to differentiate between these relations

We are developing an ontology of microsocial concepts for use in an instructional system for teaching cross-cultural communication. We report here on that part of the ontology relating to interpersonal relationships. We first explicate the key concepts of commitment, shared plans, and good will. Then in terms of these we present a formal account of the host-guest relationship.

The classic dualism offered in Descartes' The English language is rich with words and phrases with Meditations on First Philosophy (1641) views a person as meaning that is grounded in our commonsense theories of having both a physical body and a nonphysical mind.

Granularity is the concept of breaking down an event into smaller parts or granules such that each individual granule plays a part in the higher level event. Humans can seamlessly shift their granularity perspectives while reading or understanding a text. To emulate such a mechanism, we describe a theory for inferring this information automatically from raw input text descriptions and some background knowledge to learn the global behavior of event descriptions from local behavior of components. We also elaborate on the importance of discovering granularity structures for solving NLP problems such as — automated question answering and text summarization.

The central challenge in commonsense knowledge representation research is to develop content theories that achieve a high degree of both competency and coverage. We describe a new methodology for constructing formal theories in commonsense knowledge domains that complements traditional knowledge representation approaches by first addressing issues of coverage. These concepts are sorted into a manageable number of coherent domains, one of which is the representational area of commonsense human memory. These representational areas are then analyzed using more traditional knowledge representation techniques, as demonstrated in this article by our treatment of commonsense human memory.

(Niles and Pease 2001). Considering that tremendous scheduling that are robust in the face of realworld progress has been made in commonsense reasoning concerns like time zones, daylight savings in specialized topics such as thermodynamics time, and international calendar variations. in physical systems (Collins and Forbus 1989), it is surprising that our best content theories Given the importance of an ontology of of people are still struggling to get past time across so many different commonsense simple notions of belief and intentionality (van der Hoek and Wooldridge 2003). However, search is the generation of competency theories systems that can successfully reason about that have a degree of depth necessary to solve people are likely to be substantially more valuable inferential problems that people are easily able than those that reason about thermodynamics to handle. in most future applications. Yet competency in content theories is only Content theories for reasoning about people half of the challenge. Commonsense reasoning are best characterized collectively as a theory of in AI theories will require that computers not commonsense psychology, in contrast to those only make deep humanlike inferences but also that are associated with commonsense (naïve) ensure that the scope of these inferences is as physics. The scope of commonsense physics, broad as humans can handle, as well. That is, best outlined in Patrick Hayes's first and second in addition to competency, content theories will "Naïve Physics Manifestos" (Hayes 1979, need adequate coverage over the full breadth of 1984), includes content theories of time, space, concepts that are manipulated in human-level physical entities, and their dynamics. It is only by achieving psychology, in contrast, concerns all some adequate level of coverage that we of the aspects of the way that people think they can begin to construct reasoning systems that think. It should include notions of plans and integrate fully into real-world AI applications, goals, opportunities and threats, decisions and where pragmatic considerations and expressive preferences, emotions and memories, along user interfaces raise the bar significantly.

He knew the challenges opinion, as one of the premier natural language were great and would require the research groups in the world. He gave efforts of many people. He had a genius for one of us (Barbara Grosz) her first AI job, even bringing these people together. In doing so, he took a of people who had known Don over the risk of a magnitude that she fully appreciated years to send us reminiscences. Although only years later when she herself was hiring each person's story differed, a striking commonality research associates.

Having been involved in there is a page limit on the papers.

Proceedings, Ninth Intl. Joint Conference on Artificial Intelligence, pp. 432-435. Los Angeles, California. August 1985.

Proceedings, 23rd Annual Meeting of the Association for Computational Linguistics, pp. 61-69. Chicago, Illinois, July 1985.