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Ambiguity Resolution from Interpretation University of Pennsylvania ScholarlyCommons IRCS Technical Reports Series Institute for Research in Cognitive Science October 1993 A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation Michael Niv University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/ircs_reports Niv, Michael, "A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation" (1993). IRCS Technical Reports Series. 184. https://repository.upenn.edu/ircs_reports/184 University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-93-27. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/ircs_reports/184 For more information, please contact [email protected]. A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation Abstract Syntactic ambiguity abounds in natural language, yet humans have no diffculty coping with it. In fact, the process of ambiguity resolution is almost always unconscious. But it is not infallible, however, as example 1 demonstrates. 1. The horse raced past the barn fell. This sentence is perfectly grammatical, as is evident when it appears in the following context: 2. Two horses were being shown to to a prospective buyer. One was raced past a meadow and the other was raced past a barn. Grammatical yet unprocessable sentences such as 1. are called 'garden-path sentences.' Their existence provides an opportunity to investigate the human sentence processing mechanism by studying how and when it fails. The aim of this thesis is to construct a computational model of language understanding which can predict processing difficulty. The data to be modeled are known examples of garden path and non-garden path sentences, and other results from psycholinguistics. It is widely believed that there are two distinct loci of computation in sentence processing: syntactic parsing and semantic interpretation. One longstanding controversy is which of these two modules bears responsibility for the immediate resolution of ambiguity. My claim is that it is the latter, and that the syntactic processing module is a very simple device which blindly and faithfully constructs all possible analyses for the sentence up to the current point of processing. The interpretive module serves as a filter, occasionally discarding certain of these analyses which it deems less appropriate for the ongoing discourse than their competitors. This document is divided into three parts. The first is introductory, and reviews a selection of proposals from the sentence processing literature. The second part explores a body of data which has been adduced in support of a theory of structural preferences - one that is inconsistent with the present claim. I show how the current proposal can be specified ot account for the available data, and moreover to predict where structural preference theories will go wrong. The third part is a theoretical investigation of how well the proposed architecture can be realized using current conceptions of linguistic competence. In it, I present a parsing algorithm and a meaning-based ambiguity resolution method. Comments University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-93-27. This thesis or dissertation is available at ScholarlyCommons: https://repository.upenn.edu/ircs_reports/184 The Institute For Research In Cognitive Science A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation P (Ph.D. Dissertation) by E Michael Niv University of Pennsylvania Philadelphia, PA 19104-6228 N October 1993 Site of the NSF Science and Technology Center for Research in Cognitive Science N University of Pennsylvania IRCS Report 93-27 Founded by Benjamin Franklin in 1740 A Computational Model of Syntactic Processing Ambiguity Resolution from Interpretation Michael Niv A Dissertation in Computer and Information Science Presented to the Faculties of the UniversityofPennsylvania in Partial Fulllmentofthe Requirements for the Degree of Do ctor of Philosophy Mark J Steedman Sup ervisor of Dissertation Mark J Steedman Graduate Group Chairp erson c Copyright Michael Niv Acknowledgements My deep est feelings of gratitude and indebtedness are to my advisor and mentor Mark Steedman Mark has taught me not just linguistics and cognition but also ab out thinking and b ehaving like a scientist He tirelessly and carefully read through each of the manymany drafts of pap ers I have given him including this thesis providing meticulous and insightful comments and sp ent countless hours explaining and debating these comments with me Iwould guess that every single p oint I discuss in this thesis reects his contributions This thesis like me is a pro duct of a community The Cognitive Science communityatPenn brings forth a b eautifully dissonant buzz of intellectual activity The diversity of outlo oks paradigms metho ds results and opinions ab out the study of the mind provides an ideal en vironment in which to go shopping for ones own direction It is imp ossible to identify the individual memb ers and visitors to IRCS Institute for Research in Cognitive Science that have shap ed my p ersp ective so Ill just thank the two p eople who are resp onsible for enabling the tremendous growth of IRCS during mystayatPenn Aravind Joshi and Lila Gleitman Iamvery grateful to my do ctoral committee Janet Fodor Aravind Joshi Mitch Marcus and Ellen Prince for their helpful comments on this do cument in particular to Ellen and Janet for long and fruitful discussions ab out the rst parts of the thesis and to Mitch for making the Penn Treebank available to me My colleagues Breck Baldwin Barbara Di Eugenio Bob Frank Dan Hardt Jamie Henderson oungSuk Lee Owen Rambow Phil Resnik Rob ert Rubino Lyn Walker Beth Ann Ho ckeyY and many other memb ers of the Computational Linguistics group at Penn provided me with much help supp ort and friendship throughout my graduate studies IRCS p ostdo cs have b een particularly helpful I have learned a great deal from Sandeep Prasada Je Siskind and Mark Hepple This work has b enetted greatly from suggestions and advice by Ellen Bard Julie Boland Lyn Frazier Susan Garnsey Mark Johnson Rob ert Ladd Don Mitchell Dick Oehrle Stu Shieb er Val Tannen Henry Thompson AmyWeinb erg Steve Whittaker and Bill Wo o ds Rich Pito was very helpful in extending his treebank searching program tgrep to accommo date my needs I am grateful to Bonnie Webb er for oerring me my rst opp ortunity to do research the TraumAID pro ject and for her nancial supp ort during my rst few years at Penn The research rep orted in this do cumentwas supp orted by the following grants DARPA N J ARODAALC NSF IRI Ben Franklin SC I thank the Computational Linguistics facultymemb ers for their sustained nancial supp ort My thanks also go to Jow and Oyi Ali Baba Yue Kee and the Kims for sustenance iii Iamvery grateful to Barbara Ramesh Bob Sandeep and to the ocial memb ers of the late nightWawa crew Patrick Anuj Tilman esp ecially to YoungSuk for the b eginnings of lifelong friendships Ive had a few really wonderful teachers Ruta in kindergarten Mme OConnor for Frenchin high scho ol Jo e ORourke in college and Val Tannen in graduate scho ol They each p ossess the rare and precious talent of sparking curiosity and intellectual excitement in their students My most imp ortant teachers havebeenmy parents Yaa and Avigdor I thank them and my two sisters Adi and Tamar for encouraging my curiosity and coming to terms with just how curious I have b ecome iv Abstract A Computational Mo del of Syntactic Pro cessing Ambiguity Resolution from Interpretation Michael Niv Mark J Steedman Sup ervisor Syntactic ambiguity ab ounds in natural language yet humans have no diculty coping with it In fact the pro cess of ambiguity resolution is almost always unconscious But it is not infallible however as example demonstrates The horse raced past the barn fell This sentence is p erfectly grammatical as is evident when it app ears in the following context Two horses were b eing shown o to a prosp ective buyer One was raced past a meadow and the other was raced past a barn Grammatical yet unpro cessable sentences such as are called gardenpath sentences Their existence provides an opp ortunitytoinvestigate the human sentence pro cessing mechanism by studying how and when it fails The aim of this thesis is to construct a computational mo del of language understanding which can predict pro cessing diculty The data to b e mo deled are known examples of garden path and nongarden path sentences and other results from psycholinguistics It is widely b elieved that there are two distinct lo ci of computation in sentence pro cessing syntactic parsing and semantic interpretation One longstanding controversy is which of these two mo dules b ears resp onsibility for the immediate resolution of ambiguity My claim is that it is the latter and that the syntactic pro cessing mo dule is a very simple device which blindly and faithfully constructs all p ossible analyses for the sentence up to the current p oint of pro cessing h The interpretive mo dule serves as a lter o ccasionally discarding certain of these analyses whic it deems less appropriate for the ongoing discourse than their comp etitors This do cument is divided into three parts The rst is intro ductory and reviews a selection of prop osals from the sentence pro cessing literature The second part explores a b o dy of data which has b een adduced
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