Subdeletion in Verb Phrase Ellipsis

Subdeletion in Verb Phrase Ellipsis Paul G. Donecker Villanova University 800 Lancaster Avenue Villanova, PA 19085 donecker@monet.vill.edu Abstract This paper stems from an ongoing research project ~ on verb phrase ellipsis. The project's goals are to implement a verb phrase ellipsis resolution algorithm, automatically test the algorithm on corpus data, then automatically evaluate the algorithm against human-generated answers. The paper will establish the current status of the algorithm based on this automatic evaluation, categorizing current problem situations. An algorithm to handle one of these problems, the ease of subdeletion, will be described and evaluated. The algorithm attempts to detect and solve subdeletion by locating adjuncts of similar types in a verb phrase ellipsis and corresponding antecedent. 1. Introduction A verb phrase ellipsis (VPE) exists when a sentence has an auxiliary verb but no verb phrase (VP). For example, in the sentence "Gather ye rosebuds while ye may," "may" is the beginning of a VPE. Its antecedent is "gather ye rosebuds." The research described in this paper is part of a project to automate the resolution of VPE occurrences, and also to automate the evaluation of the success of the VPE resolution (Hardt 1995). Based on these evaluations of the algorithm, several distinct categories of error situations have been determined. We have focused on errors in which the program selects the correct head verb as antecedent. These cases can be divided into the following categories: 1) too much material included from the antecedent, 2) not enough much material included from the antecedent, 3) discontinuous antecedents, and 4) miscellaneous. For a subset of case 1, subdeletion, an algorithm derived from (Lappin and McCord, 1990) is evaluated 1 This research was supported in part by NSF Career Grant, no. IRI-9502257. in regard to the Brown Corpus. 2. Background Previous studies on evaluating discourse processing (e.g., Walker, 1989; Hobbs, 1978) have involved subjectively examining test cases to determine correctness. With the development of resources such as the Penn Treebank (Marcus, Santorini, and Marcinkiewicz, 1993), it has become possible to automate empirical tests of discourse processing systems to obtain a more objective measure of their success. Towards this end, an algorithm was implemented in a Common Lisp program called VPEAL (Verb Phrase Ellipsis Antecedent Locator) (Hardt, 1995), drawing on the Penn Treebank as input. The portion of the Penn Treebank examined--the Brown Corpus, about a million words--contains about 400 VPEs. Furthermore, to automatically evaluate the algorithm, utilities were developed to automatically test the output of VPEAL for correctness. The most recent version of VPEAL contained 18 sub-parts for ranking and choosing antecedents. Testing the program's performance involved finding the percentage of correct antecedents found by any or all of these algorithms. This was achieved by having human coders read plain text versions of the parsed passages, marking what they felt to be the antecedent. Antecedents selected by VPEAL were considered correct if they matched the antecedents selected by the coders. The remainder of this paper will describe the categories of errors observed, then describe an …

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