@inproceedings{roark-etal-2006-sparseval,
title = "{SP}arseval: Evaluation Metrics for Parsing Speech",
author = "Roark, Brian and
Harper, Mary and
Charniak, Eugene and
Dorr, Bonnie and
Johnson, Mark and
Kahn, Jeremy and
Liu, Yang and
Ostendorf, Mari and
Hale, John and
Krasnyanskaya, Anna and
Lease, Matthew and
Shafran, Izhak and
Snover, Matthew and
Stewart, Robin and
Yung, Lisa",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L06-1060/",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T SParseval: Evaluation Metrics for Parsing Speech
%A Roark, Brian
%A Harper, Mary
%A Charniak, Eugene
%A Dorr, Bonnie
%A Johnson, Mark
%A Kahn, Jeremy
%A Liu, Yang
%A Ostendorf, Mari
%A Hale, John
%A Krasnyanskaya, Anna
%A Lease, Matthew
%A Shafran, Izhak
%A Snover, Matthew
%A Stewart, Robin
%A Yung, Lisa
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F roark-etal-2006-sparseval
%X 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.
%U https://aclanthology.org/L06-1060/
Markdown (Informal)
[SParseval: Evaluation Metrics for Parsing Speech](https://aclanthology.org/L06-1060/) (Roark et al., LREC 2006)
ACL
- Brian Roark, Mary Harper, Eugene Charniak, Bonnie Dorr, Mark Johnson, Jeremy Kahn, Yang Liu, Mari Ostendorf, John Hale, Anna Krasnyanskaya, Matthew Lease, Izhak Shafran, Matthew Snover, Robin Stewart, and Lisa Yung. 2006. SParseval: Evaluation Metrics for Parsing Speech. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).