Feasibility Study for Ellipsis Resultion in Dialogues by Machine-Learning Technique

A method for resolving the ellipses that appear in Japanese dialogues is proposed. This method resolves not only the subject ellipsis, but also those in object and other grammatical cases. In this approach, a machine-learning algorithm is used to select the attributes necessary for a resolution. A decision tree is built, and used as the actual ellipsis resolver. The results of blind tests have shown that the proposed method was able to provide a resolution accuracy of 91.7% for indirect objects, and 78.7% for subjects with a verb predicate. By investigating the decision tree we found that topic-dependent attributes are necessary to obtain high performance resolution, and that indispensable attributes vary according to the grammatical case. The problem of data size relative to decision-tree training is also discussed. 1 I n t r o d u c t i o n In machine translation systems, it is necessary to resolve ellipses when the source language doesn't express the subject or other grammatical cases and the target must express it. The problem of ellipsis resolution is also troublesome in information extraction and other natural language processing fields. Several approaches have been proposed to resolve ellipses, which consist of endophoric (intrasentential or anaphoric) ellipses and exophoric (or extrasentential) ellipses. One of the major approaches for endophoric ellipsis in theoretical basis utilizes the centering theory. However, its application to complex sentences has not been established because most studies have only investigated its effectiveness with successive simple sentences. Several studies of this problem have been made using the empirical approach. Among them, Murata and Nagao (1997) proposed a scoring approach where each constraint is manually scored with a n estimation of possibility, and the resolution is conducted by totaling the points each candidate receives. On the other hand, Nakaiwa and Shirai (1996) proposed a resolving algorithm for Japanese exophoric ellipses of written texts, utilizing semantic and pragmatic constraints. They claimed that 100% of the ellipses with exophoric referents could be resolved, but the experiment was a closed test with only a few samples. These approaches always require some effort to decide the scoring or the preference of provided constraints. Aone and Bennett (1995) applied a machinelearning technique to anaphora resolution in written texts. They attempted endophoric ellipsis resolution as a part of anaphora resolution, with approximately 40% recall and 74~ precision at best from 200 test samples. However, they were not concerned with exophoric ellipsis. In contrast, we applied a machine-learning approach to ellipsis resolution (Yamamoto et al., 1997). In this previous work we resolved the agent case ellipses in dialogue, with a limited topic, and performed with approximately 90% accuracy. This does not sufficiently determine the effectiveness of the decision tree, and the feasibility of this technique in resolving ellipses by each surface case is also unclear. We propose a method to resolve the ellipses that appear in Japanese dialogues. This method resolves not only the subject ellipsis, but also the object and other grammatical cases. In this approach, a machine-learning algorithm is used to build a decision tree by selecting the necessary attributes, and the decision tree is used as the actual ellipsis resoh'er. Another purpose of this paper is to discuss how effective the machine-learning approach is