Learning Discourse Relations with Active Data Selection

Learning Discourse Relations with Active Data Selection Tadashi Nomoto* National Institute of Japanese Literature 1-16-10 Yutaka Shinagawa Tokyo 142-8585 Japan nomoto@nij 1. ac. jp Yuji Matsumoto Nara Institute of Science and Technology 8916-5 Takayama Ikoma Nara 630-0101 Japan matsu@is, aist-nara, ac. jp Abstract The paper presents a new approach to identi- fying discourse relations, which makes use of a particular sampling method called committee- based sampling (CBS). In the committee-based sampling, multiple learning models are gener- ated to measure the utility of an input example in classification; if it is judged as not useful, then the example will be ignored. The method has the effect of reducing the amount of data required for training. In the paper, we extend CBS for decision tree classifiers. With an addi- tional extension called error feedback, it is found that the method achieves an increased accuracy as well as a substantial reduction in the amount of data for training classifiers. 1 Introduction The success of corpus-based approaches to dis- course ultimately depends on whether one is able to acquire a large volume of data annotated for discourse-level information. However, to ac- quire merely a few hundred texts annotated for discourse information is often impossible due to the enormity of the haman labor required. This paper presents a novel method for reduc- ing the amount of data for training a decision tree classifier, while not compromising the accu- racy. While there has been some work explor- ing the use of machine leaning techniques for discourse and dialogue (Marcu, 1997; Samuel et al., 1998), to our knowledge, no computational research on discourse or dialogue so far has ad- dressed the problem of reducing or minimizing the amount of data for training a learning algo- rithm. * The work reported here was conducted while the first author was with Advanced Research Lab., Hitachi Ltd, 2520 Hatoyama Saitama 350-0395 Japan. A particular method proposed here is built on the committee-based sampling, initially pro- posed for probabilistic classifiers by Dagan and Engelson (1995), where an example is selected from the corpus according to its utility in im- proving statistics. We extend the method for decision tree classifiers using a statistical tech- nique called bootstrapping (Cohen, 1995). With an additional extension, which we call error .feedback, it is found that the method achieves an increased accuracy as well as a significant reduction of training data. The method pro- posed here should be of use in domains other than discourse, where a decision tree strategy is found applicable. 2 Tagging a corpus with discourse relations In tagging a corpus, we adopted Ichikawa (1990)'s scheme for organizing discourse rela- tions (Table 1). The advantage of Ichikawa (1990)'s scheme is that it directly associates dis- course relations with explicit surface cues (eg. sentential connectives), so it is possible for the coder to determine a discourse relation by figur- ing a most natural cue that goes with a sentence he/she is working on. Another feature is that, unlike Rhetorical Structure Theory (Mann and Thompson, …

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