Hobbs, Jerry R.


Which States Can Be Changed by Which Events?

AAAI Conferences

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


Applications and Discovery of Granularity Structures in Natural Language Discourse

AAAI Conferences

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.



Formalizations of Commonsense Psychology

AI Magazine

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.


Formalizations of Commonsense Psychology

AI Magazine

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. We show how a close examination of a very general task (strategic planning) leads to a catalog of the concepts and facts that must be encoded for general commonsense reasoning. These concepts are sorted into a manageable number of coherent domains, one of which is the representational area of commonsense human memory. We then elaborate on these concepts using textual corpus-analysis techniques, where the conceptual distinctions made in natural language are used to improve the definitions of the concepts that should be expressible in our formal theories. These representational areas are then analyzed using more traditional knowledge representation techniques, as demonstrated in this article by our treatment of commonsense human memory.


Donald E. Walker: A Remembrance

AI Magazine

A tribute to Donald E. Walker, one of the founders of the Association for the Advancement of Artificial Intelligence, and long-time secretary-treasurer of IJCAI Inc.


Letters to the Editor

AI Magazine

Comments on "IJCAI Policy on Multiple Publication of Papers" by Alan Bundy in Spring 1989.


Ontological Promiscuity

Classics

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


Granularity

Classics

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