Answering Questions about Complex Events

Abstract : Reasoning about event structure is a fundamental research problem in Artificial Intelligence. Event scenarios and procedures are inherently about change of state. To understand them and answer questions about them requires a means of describing, simulating and analyzing the underlying processes, taking into account preconditions and effects, the resources they produce and consume, and their interactions with each other. We propose a novel, comprehensive event schema that covers many of the parameters required and has explicit links to language through FrameNet. Based on the event schema, we have implemented a dynamic model of events capable of simulation and causal inference. We describe the results of applying this event reasoning platform to question answering and system diagnosis, providing responses to questions on justification, temporal projection, ability and 'what-if' hypotheticals, as well as complex problems in diagnosis of systems with incomplete knowledge.

[1]  David Chapman,et al.  Pengi: An Implementation of a Theory of Activity , 1987, AAAI.

[2]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[3]  Falko Bause,et al.  Stochastic Petri Nets: An Introduction to the Theory , 2012, PERV.

[4]  John McCarthy,et al.  SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE , 1987 .

[5]  Benjamin K. Bergen,et al.  Embodied Construction Grammar in Simulation-Based Language Understanding , 2003 .

[6]  Christopher D. Manning,et al.  An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition , 2006, ACL.

[7]  James Pustejovsky,et al.  Evita: A Robust Event Recognizer For QA Systems , 2005, HLT.

[8]  Casimir A. Kulikowski,et al.  Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[9]  Marco Ajmone Marsan,et al.  Modelling with Generalized Stochastic Petri Nets , 1995, PERV.

[10]  Andrés Montoyo,et al.  Natural Language Processing and Information Systems, 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15-17, 2005, Proceedings , 2005, NLDB.

[11]  C. Fillmore FRAME SEMANTICS AND THE NATURE OF LANGUAGE * , 1976 .

[12]  Johan van Benthem,et al.  Handbook of Logic and Language , 1996 .

[13]  Josef Ruppenhofer,et al.  FrameNet II: Extended theory and practice , 2006 .

[14]  Christopher R. Johnson,et al.  Background to Framenet , 2003 .

[15]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[16]  Lance A. Miller,et al.  Review of The process of question answering: a computer simulation of cognition by Wendy G. Lehnert. Lawrence Erlbaum Associates 1978. , 1980 .

[17]  Lynne Bowker Third International conference on Language Resources and Evaluation(LREC 2002) , 2002 .

[18]  Daniel Jurafsky,et al.  Automatic Labeling of Semantic Roles , 2002, CL.

[19]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[20]  Peter A. Flach,et al.  Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves , 2003, ICML.

[21]  Steven Bethard,et al.  Finding event, temporal and causal structure in text: a machine learning approach , 2007 .

[22]  Pushpak Bhattacharyya,et al.  Is question answering an acquired skill? , 2004, WWW '04.

[23]  Gerhard Fliedner,et al.  Towards Natural Interactive Question Answering , 2006, LREC.

[24]  Miriam R. L. Petruck FRAME SEMANTICS , 1996 .

[25]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[26]  Ramanathan V. Guha,et al.  Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project , 1990 .

[27]  Bruce W. Porter,et al.  Automated Modeling for Answering Prediction Questions: Selecting the Time Scale and System Boundary , 1994, AAAI.

[28]  Dan Klein,et al.  Named Entity Recognition with Character-Level Models , 2003, CoNLL.

[29]  Jerry R. Hobbs,et al.  DAML-S: Semantic Markup for Web Services , 2001, SWWS.

[30]  Ronald C. Arkin,et al.  Integrating behavioral, perceptual, and world knowledge in reactive navigation , 1990, Robotics Auton. Syst..

[31]  Deborah L. McGuinness,et al.  OWL Web ontology language overview , 2004 .

[32]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[33]  Mark Steedman,et al.  Temporal Ontology and Temporal Reference , 1988, CL.

[34]  D. Mossman Three-way ROCs , 1999, Medical decision making : an international journal of the Society for Medical Decision Making.

[35]  Stanley J. Rosenschein,et al.  Formal theories of knowledge in AI and robotics , 1986, New Generation Computing.

[36]  Sheila A. McIlraith,et al.  Simulation, verification and automated composition of web services , 2002, WWW.

[37]  Wendy Grace Lehnert,et al.  The Process of Question Answering , 2022 .

[38]  Nancy A. Chinchor,et al.  Overview of MUC-7 , 1998, MUC.

[39]  Martha Palmer,et al.  From TreeBank to PropBank , 2002, LREC.

[40]  John McCarthy,et al.  Programs with common sense , 1960 .

[41]  Peter Clark,et al.  A library of generic concepts for composing knowledge bases , 2001, K-CAP '01.

[42]  Shan Wang,et al.  Classifying Temporal Relations Between Events , 2007, ACL.

[43]  Nathanael Chambers,et al.  Unsupervised Learning of Narrative Event Chains , 2008, ACL.

[44]  Jerome A. Feldman,et al.  Best-fit constructional analysis , 2008 .

[45]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[46]  Srini Narayanan,et al.  Putting Frames in Perspective , 2002, COLING.

[47]  S. Narayanan Talking the Talk is Like Walking the Walk : A Computational Model of Verbal Aspect , 1997 .

[48]  Nadia Busi Analysis issues in Petri nets with inhibitor arcs , 2002, Theor. Comput. Sci..

[49]  Adam Pease,et al.  Towards a standard upper ontology , 2001, FOIS.

[50]  Srinivas Narayanan,et al.  Reasoning About Actions in Narrative Understanding , 1999, IJCAI.

[51]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[52]  Alessandro Moschitti,et al.  Shallow Semantic Parsing Based on FrameNet, VerbNet and PropBank , 2006, ECAI.

[53]  Daniel Gildea,et al.  The Proposition Bank: An Annotated Corpus of Semantic Roles , 2005, CL.

[54]  Vladimir Lifschitz,et al.  Between Circumscription and Autoepistemic Logic , 1989, KR.

[55]  Marc Moens,et al.  Seventh Message Understanding Conference (MUC-7) , 1998 .

[56]  Mark A. Przybocki,et al.  The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation , 2004, LREC.

[57]  Hitoshi Isahara,et al.  IREX: IR & IE Evaluation Project in Japanese , 2000, LREC.

[58]  Sanda M. Harabagiu,et al.  High performance question/answering , 2001, SIGIR '01.

[59]  Michael Gelfond,et al.  Representing Action and Change by Logic Programs , 1993, J. Log. Program..

[60]  Sanda M. Harabagiu,et al.  Shallow Semantics for Relation Extraction , 2005, IJCAI.

[61]  Jerome A. Feldman,et al.  Contextual bootstrapping for grammar learning , 2008 .

[62]  Frank van Harmelen,et al.  Web Ontology Language , 2004 .

[63]  Gerhard Fliedner A Generalised Similarity Measure for Question Answering , 2005, NLDB.

[64]  David J. Israel,et al.  A Question-Answering System for AP Chemistry: Assessing KR&R Technologies , 2004, KR.

[65]  Nils J. Nilsson,et al.  Shakey the Robot , 1984 .

[66]  Michael Colclough The Process of Question Answering — A Computer Simulation of Cognition , 1979 .