Predicting word sense annotation agreement

Hector Martinez Alonso; Anders Trærup Johannsen; Oier Lopez de Lacalle; Eneko Agirre

Predicting word sense annotation agreement

Hector Martinez Alonso, Anders Trærup Johannsen, Oier Lopez de Lacalle, Eneko Agirre

Centre for Language Technology

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 sense inventories, the difficulty of the examples or the interpretation preferences of the annotators. 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.

Original language	English
Title of host publication	LSDSem 2015 : Linking Models of Lexical, Sentential and Discourse-level Semantics
Number of pages	6
Publisher	Association for Computational Linguistics
Publication date	2015
Pages	89-94
ISBN (Print)	978-1-941643-32-7
Publication status	Published - 2015

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

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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 sense inventories, the difficulty of the examples or the interpretation preferences of the annotators. 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

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PB - Association for Computational Linguistics

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Predicting word sense annotation agreement

Abstract

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