LREC 2006 - Proceedings sorted by papers

Title	SParseval: Evaluation Metrics for Parsing Speech
Authors	B. Roark, M. Harper, E. Charniak, B. Dorr, M. Johnson, J. Kahn, Y. Liu, M. Ostendorf, J. Hale, A. Krasnyanskaya, M. Lease, I. Shafran, M. Snover, R. Stewart, L. Yung
Abstract	While both spoken and written language processing stand to benefit from parsing, the standard Parseval metrics (Black et al., 1991) and their canonical implementation (Sekine and Collins, 1997) are only useful for text. The Parseval metrics are undefined when the words input to the parser do not match the words in the gold standard parse tree exactly, and word errors are unavoidable with automatic speech recognition (ASR) systems. To fill this gap, we have developed a publicly available tool for scoring parses that implements a variety of metrics which can handle mismatches in words and segmentations, including: alignment-based bracket evaluation, alignment-based dependency evaluation, and a dependency evaluation that does not require alignment. We describe the different metrics, how to use the tool, and the outcome of an extensive set of experiments on the sensitivity.
Keywords	parse scoring, bracket scores, dependency scores,
Full paper	SParseval: Evaluation Metrics for Parsing Speech

Title

SParseval: Evaluation Metrics for Parsing Speech

Authors

B. Roark, M. Harper, E. Charniak, B. Dorr, M. Johnson, J. Kahn, Y. Liu, M. Ostendorf, J. Hale, A. Krasnyanskaya, M. Lease, I. Shafran, M. Snover, R. Stewart, L. Yung

Abstract

While both spoken and written language processing stand to benefit from parsing, the standard Parseval metrics (Black et al., 1991) and their canonical implementation (Sekine and Collins, 1997) are only useful for text. The Parseval metrics are undefined when the words input to the parser do not match the words in the gold standard parse tree exactly, and word errors are unavoidable with automatic speech recognition (ASR) systems. To fill this gap, we have developed a publicly available tool for scoring parses that implements a variety of metrics which can handle mismatches in words and segmentations, including: alignment-based bracket evaluation, alignment-based dependency evaluation, and a dependency evaluation that does not require alignment. We describe the different metrics, how to use the tool, and the outcome of an extensive set of experiments on the sensitivity.

Keywords

parse scoring, bracket scores, dependency scores,

Full paper

SParseval: Evaluation Metrics for Parsing Speech