Predicting word sense annotation agreement

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Predicting word sense annotation agreement. / Martinez Alonso, Hector; Johannsen, Anders Trærup; Lopez de Lacalle, Oier; Agirre, Eneko.

LSDSem 2015 : Linking Models of Lexical, Sentential and Discourse-level Semantics. Association for Computational Linguistics, 2015. s. 89-94.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Martinez Alonso, H, Johannsen, AT, Lopez de Lacalle, O & Agirre, E 2015, Predicting word sense annotation agreement. i LSDSem 2015 : Linking Models of Lexical, Sentential and Discourse-level Semantics. Association for Computational Linguistics, s. 89-94.

APA

Martinez Alonso, H., Johannsen, A. T., Lopez de Lacalle, O., & Agirre, E. (2015). Predicting word sense annotation agreement. I LSDSem 2015 : Linking Models of Lexical, Sentential and Discourse-level Semantics (s. 89-94). Association for Computational Linguistics.

Vancouver

Martinez Alonso H, Johannsen AT, Lopez de Lacalle O, Agirre E. Predicting word sense annotation agreement. I LSDSem 2015 : Linking Models of Lexical, Sentential and Discourse-level Semantics. Association for Computational Linguistics. 2015. s. 89-94

Author

Martinez Alonso, Hector ; Johannsen, Anders Trærup ; Lopez de Lacalle, Oier ; Agirre, Eneko. / Predicting word sense annotation agreement. LSDSem 2015 : Linking Models of Lexical, Sentential and Discourse-level Semantics. Association for Computational Linguistics, 2015. s. 89-94

Bibtex

@inproceedings{15637f3bea2840eab89246c4abb5bfd8,
title = "Predicting word sense annotation agreement",
abstract = "High agreement is a common objective when annotating data for word senses. However, a number of factors make perfect agreement impossible, e.g. the limitations of the sense inventories, the difficulty of the examples or the interpretation preferences of the annotations.Estimating potential agreement is thus a relevant task to supplement the evaluation of sense annotations.In this article we propose two methods to predict agreement on word-annotation instances. We experiment with a continuous representation and a three-way discretization of observed agreement. In spite of the difficulty of the task, we find that different levels of agreement can be identified---in particular, low-agreement examples are easier to identify.",
author = "{Martinez Alonso}, Hector and Johannsen, {Anders Tr{\ae}rup} and {Lopez de Lacalle}, Oier and Eneko Agirre",
year = "2015",
language = "English",
isbn = "978-1-941643-32-7",
pages = "89--94",
booktitle = "LSDSem 2015",
publisher = "Association for Computational Linguistics",

}

RIS

TY - GEN

T1 - Predicting word sense annotation agreement

AU - Martinez Alonso, Hector

AU - Johannsen, Anders Trærup

AU - Lopez de Lacalle, Oier

AU - Agirre, Eneko

PY - 2015

Y1 - 2015

N2 - High agreement is a common objective when annotating data for word senses. However, a number of factors make perfect agreement impossible, e.g. the limitations of the sense inventories, the difficulty of the examples or the interpretation preferences of the annotations.Estimating potential agreement is thus a relevant task to supplement the evaluation of sense annotations.In this article we propose two methods to predict agreement on word-annotation instances. We experiment with a continuous representation and a three-way discretization of observed agreement. In spite of the difficulty of the task, we find that different levels of agreement can be identified---in particular, low-agreement examples are easier to identify.

AB - High agreement is a common objective when annotating data for word senses. However, a number of factors make perfect agreement impossible, e.g. the limitations of the sense inventories, the difficulty of the examples or the interpretation preferences of the annotations.Estimating potential agreement is thus a relevant task to supplement the evaluation of sense annotations.In this article we propose two methods to predict agreement on word-annotation instances. We experiment with a continuous representation and a three-way discretization of observed agreement. In spite of the difficulty of the task, we find that different levels of agreement can be identified---in particular, low-agreement examples are easier to identify.

M3 - Article in proceedings

SN - 978-1-941643-32-7

SP - 89

EP - 94

BT - LSDSem 2015

PB - Association for Computational Linguistics

ER -

ID: 141768705