Searcharxiv⌕ Search

arXiv subjects

Rebecca J. Passonneau

Publications and source records attributed to Rebecca J. Passonneau.

22 records · Page 2Linked to original sources

Applying Reliability Metrics to Co-Reference Annotation

Studies of the contextual and linguistic factors that constrain discourse phenomena such as reference are coming to depend increasingly on annotated language corpora. In preparing the corpora, it is important to evaluate the reliability of the annotation, but methods for doing so have not been readily available. In this report, I present a method for computing reliability of coreference annotation. First I review a method for applying the information retrieval metrics of recall and precision to coreference annotation proposed by Marc Vilain and his collaborators. I show how this method makes it possible to construct contingency tables for computing Cohen's Kappa, a familiar reliability metric. By comparing recall and precision to reliability on the same data sets, I also show that recall and precision can be misleadingly high. Because Kappa factors out chance agreement among coders, it is a preferable measure for developing annotated corpora where no pre-existing target annotation exists.

cmp-lg↗

Integrating Gricean and Attentional Constraints

This paper concerns how to generate and understand discourse anaphoric noun phrases. I present the results of an analysis of all discourse anaphoric noun phrases (N=1,233) in a corpus of ten narrative monologues, where the choice between a definite pronoun or phrasal NP conforms largely to Gricean constraints on informativeness. I discuss Dale and Reiter's [To appear] recent model and show how it can be augmented for understanding as well as generating the range of data presented here. I argue that integrating centering [Grosz et al., 1983] [Kameyama, 1985] with this model can be applied uniformly to discourse anaphoric pronouns and phrasal NPs. I conclude with a hypothesis for addressing the interaction between local and global discourse processing.

cmp-lg↗

Combining Multiple Knowledge Sources for Discourse Segmentation

We predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. We present two methods for developing segmentation algorithms from training data: hand tuning and machine learning. When multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).

cmp-lg↗

Intention-based Segmentation: Human Reliability and Correlation with Linguistic Cues

Certain spans of utterances in a discourse, referred to here as segments, are widely assumed to form coherent units. Further, the segmental structure of discourse has been claimed to constrain and be constrained by many phenomena. However, there is weak consensus on the nature of segments and the criteria for recognizing or generating them. We present quantitative results of a two part study using a corpus of spontaneous, narrative monologues. The first part evaluates the statistical reliability of human segmentation of our corpus, where speaker intention is the segmentation criterion. We then use the subjects' segmentations to evaluate the correlation of discourse segmentation with three linguistic cues (referential noun phrases, cue words, and pauses), using information retrieval metrics.

cmp-lg↗