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        <title>One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts</title>
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        <namePart type="given">Andrey</namePart>
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    <name type="personal">
        <namePart type="given">Lilja</namePart>
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            <title>Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change</title>
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        <name type="personal">
            <namePart type="given">Nina</namePart>
            <namePart type="family">Tahmasebi</namePart>
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            <namePart type="given">Lars</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type ‘location:armed-group’ based on data about past events. As the source of semantic information, we use diachronic word embedding models trained on English news texts. A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the number of false positives; this approach is shown to be beneficial on two different corpora. Finally, we publish a ready-to-use test set for one-to-X analogy evaluation on historical armed conflicts data.</abstract>
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    <identifier type="doi">10.18653/v1/W19-4724</identifier>
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%0 Conference Proceedings
%T One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts
%A Kutuzov, Andrey
%A Velldal, Erik
%A Øvrelid, Lilja
%Y Tahmasebi, Nina
%Y Borin, Lars
%Y Jatowt, Adam
%Y Xu, Yang
%S Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F kutuzov-etal-2019-one
%X We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type ‘location:armed-group’ based on data about past events. As the source of semantic information, we use diachronic word embedding models trained on English news texts. A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the number of false positives; this approach is shown to be beneficial on two different corpora. Finally, we publish a ready-to-use test set for one-to-X analogy evaluation on historical armed conflicts data.
%R 10.18653/v1/W19-4724
%U https://aclanthology.org/W19-4724
%U https://doi.org/10.18653/v1/W19-4724
%P 196-201

Markdown (Informal)

[One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts](https://aclanthology.org/W19-4724) (Kutuzov et al., LChange 2019)

• One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts (Kutuzov et al., LChange 2019)

ACL

• Andrey Kutuzov, Erik Velldal, and Lilja Øvrelid. 2019. One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts. In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, pages 196–201, Florence, Italy. Association for Computational Linguistics.