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Crowdsourcing and Aggregating Nested Markable Annotations

Madge, Chris and Yu, Juntao and Chamberlain, Jon and Kruschwitz, Udo and Paun, Silviu and Poesio, Massimo (2019) Crowdsourcing and Aggregating Nested Markable Annotations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019-07-28 - 2019-08-02.

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Abstract

One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation. Markable identification is typically carried out semi-automatically, by running a markable identifier and correcting its output by hand–which is increasingly done via annotators recruited through crowdsourcing and aggregating their responses. In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector. One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task and language-independent and are thus applicable to a variety of other annotation scenarios.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 04 Dec 2020 19:59
Last Modified: 04 Dec 2020 20:15
URI: http://repository.essex.ac.uk/id/eprint/25793

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