Automatic Detection of Nonreferential It in Spoken Multi-Party Dialog

Automatic Detection of Nonreferential It in Spoken Multi-Party Dialog Christoph M¨uller EML Research gGmbH Villa Bosch Schloß-Wolfsbrunnenweg 33 69118 Heidelberg, Germany christoph.mueller@eml-research.de Abstract We present an implemented machine learning system for the automatic detec- tion of nonreferential it in spoken dialog. The system builds on shallow features ex- tracted from dialog transcripts. Our exper- iments indicate a level of performance that makes the system usable as a preprocess- ing filter for a coreference resolution sys- tem. We also report results of an annota- tion study dealing with the classification of it by naive subjects. 1 Introduction This paper describes an implemented system for the detection of nonreferential it in spoken multi- party dialog. The system has been developed on the basis of meeting transcriptions from the ICSI Meeting Corpus (Janin et al., 2003), and it is in- tended as a preprocessing component for a coref- erence resolution system in the DIANA-Summ di- alog summarization project. Consider the follow- ing utterance: MN059: Yeah. Yeah. Yeah. I’m sure I could learn a lot about um, yeah, just how to - how to come up with these structures, cuz it’s - it’s very easy to whip up something quickly, but it maybe then makes sense to - to me, but not to anybody else, and - and if we want to share and integrate things, they must - well, they must be well designed really. (Bed017) In this example, only one of the three instances of it is a referential pronoun: The first it appears in the reparandum part of a speech repair (Heeman & Allen, 1999). It is replaced by a subsequent al- teration and is thus not part of the final utterance. The second it is the subject of an extraposition construction and serves as the placeholder for the postposed infinitive phrase to whip up something quickly. Only the third it is a referential pronoun which anaphorically refers to something. The task of the system described in the follow- ing is to identify and filter out nonreferential in- stances of it, like the first and second one in the example. By preventing these instances from trig- gering the search for an antecedent, the precision of a coreference resolution system is improved. Up to the present, coreference resolution has mostly been done on written text. In this domain, the detection of nonreferential it has by now be- come a standard preprocessing step (e.g. Ng & Cardie (2002)). In the few works that exist on coreference resolution in spoken language, on the other hand, the problem could be ignored, because almost none of these aimed at developing a sys- tem that could handle unrestricted input. Eck- ert & Strube (2000) focus on an unimplemented algorithm for determining the type of antecedent (mostly NP vs. non-NP), given an anaphorical pronoun or demonstrative. The system of Byron (2002) is implemented, but deals mainly with how referents for already identified discourse-deictic anaphors can be created. Finally, Strube & M¨uller (2003) describe an implemented system for re- …

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