Work

Towards Full Text Shallow Discourse Relation Annotation: Experiments with Cross-Paragraph Implicit Relations in the PDTB

Year: 2017

Type: conference-paper

Abstract: Full text discourse parsing relies on texts comprehensively annotated with discourse relations.To this end, we address a significant gap in the inter-sentential discourse relations annotated in the Penn Discourse Treebank (PDTB), namely the class of cross-paragraph implicit relations, which account for 30% of inter-sentential relations in the corpus.We present our annotation study to explore the incidence rate of adjacent vs. non-adjacent implicit relations in cross-paragraph contexts, and the relative degree of difficulty in annotating them.Our experiments show a high incidence of non-adjacent relations that are difficult to annotate reliably, suggesting the practicality of backing off from their annotation to reduce noise for corpusbased studies.Our resulting guidelines follow the PDTB adjacency constraint for implicits while employing an underspecified representation of non-adjacent implicits, and yield 62% inter-annotator agreement on this task.

Source: Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL)

DOI: 10.18653/v1/w17-5502

Authors: Rashmi Prasad, Katherine Forbes-Riley, Alan Lee

Institutions: University of Wisconsin–Milwaukee, University of Pittsburgh, University of Pennsylvania

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: RI: Small: Collaborative Research: Research Leading to Comprehensive Guidelines for Discourse Relation Annotation