@inproceedings{coll-ardanuy-etal-2020-living,
title = "Living Machines: A study of atypical animacy",
author = "Coll Ardanuy, Mariona and
Nanni, Federico and
Beelen, Kaspar and
Hosseini, Kasra and
Ahnert, Ruth and
Lawrence, Jon and
McDonough, Katherine and
Tolfo, Giorgia and
Wilson, Daniel CS and
McGillivray, Barbara",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.400",
doi = "10.18653/v1/2020.coling-main.400",
pages = "4534--4545",
abstract = "This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.",
}
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<abstract>This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.</abstract>
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%0 Conference Proceedings
%T Living Machines: A study of atypical animacy
%A Coll Ardanuy, Mariona
%A Nanni, Federico
%A Beelen, Kaspar
%A Hosseini, Kasra
%A Ahnert, Ruth
%A Lawrence, Jon
%A McDonough, Katherine
%A Tolfo, Giorgia
%A Wilson, Daniel CS
%A McGillivray, Barbara
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F coll-ardanuy-etal-2020-living
%X This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.
%R 10.18653/v1/2020.coling-main.400
%U https://aclanthology.org/2020.coling-main.400
%U https://doi.org/10.18653/v1/2020.coling-main.400
%P 4534-4545
Markdown (Informal)
[Living Machines: A study of atypical animacy](https://aclanthology.org/2020.coling-main.400) (Coll Ardanuy et al., COLING 2020)
ACL
- Mariona Coll Ardanuy, Federico Nanni, Kaspar Beelen, Kasra Hosseini, Ruth Ahnert, Jon Lawrence, Katherine McDonough, Giorgia Tolfo, Daniel CS Wilson, and Barbara McGillivray. 2020. Living Machines: A study of atypical animacy. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4534–4545, Barcelona, Spain (Online). International Committee on Computational Linguistics.