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  • Authors: Vernier, Martin; Martin Vernier: https://orcid.org/0000-0003-4381-5712;

    Surface: India, Ladakh, Sumda Rikpa Bao, Zone 4, Rock 15, Surface 1, Photograph 1. Type of documentation of the rock art site: systematic. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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  • Authors: Project, The Bern Digital Pantheon;

    Digital Pantheon

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  • Authors: Bruneau, Laurianne; Laurianne Bruneau: https://orcid.org/0000-0002-4257-1173;

    Zone: India, Changthang, Kawathang, Zone 1, Photograph 130. Type of documentation of the rock art site: surveyed. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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  • Authors: Vernier, Martin; Martin Vernier: https://orcid.org/0000-0003-4381-5712;

    Surface: India, Zanskar, Zamthang, Zone 1, Rock 84, Surface 1, Photograph 4. Type of documentation of the rock art site: systematic. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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  • Authors: The 28th International Conference on Computational Linguistics 2020; Bohn, Tanner;

    To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of text selected from the article and given more salient presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from these models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. Human evaluation also supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this problem further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Code to reproduce this work is available at https://github.com/tannerbohn/AutomaticPullQuoteSelection.

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  • Authors: Vernier, Martin; Martin Vernier: https://orcid.org/0000-0003-4381-5712;

    Surface: India, Ladakh, Kaltze Zampa, Zone 3, Rock 9, Surface 2, Photograph 1. Type of documentation of the rock art site: systematic. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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  • Authors: Hamerow, Helena; Roushannafas, Tina;

    lateral view of charred cereal grains (Triticum free-threshing) from archaeological excavations at Botolph Bridge, England (2mm scalebar)

    https://doi.org/10.2...arrow_drop_down
    https://doi.org/10.25446/oxfor...
    Image . 2022
    License: CC BY NC
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  • Authors: Bruneau, Laurianne; Laurianne Bruneau: https://orcid.org/0000-0002-4257-1173;

    Zone: India, Changthang, Kawathang, Zone 1, Photograph 127. Type of documentation of the rock art site: surveyed. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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  • Authors: Hamerow, Helena; Stroud, Elizabeth;

    interior view of charred cereal grains (Secale cereale) from archaeological excavations at Pudding Lane, Barley, England

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    https://doi.org/10.25446/oxfor...
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  • Authors: The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Liu, Bing;

    Anthology paper link: https://aclanthology.org/2021.emnlp-main.270/ Abstract: Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods ��� neural EA models ��� rely on training with seed alignment, i.e., a set of pre-aligned entity pairs which are very costly to annotate. In this paper, we devise a novel Active Learning (AL) framework for neural EA, aiming to create highly informative seed alignment to obtain more effective EA models with less annotation cost. Our framework tackles two main challenges encountered when applying AL to EA: (1) How to exploit dependencies between entities within the AL strategy. Most AL strategies assume that the data instances to sample are independent and identically distributed. However, entities in KGs are related. To address this challenge, we propose a structure-aware uncertainty sampling strategy that can measure the uncertainty of each entity as well as its impact on its neighbour entities in the KG. (2) How to recognise entities that appear in one KG but not in the other KG (i.e., bachelors). Identifying bachelors would likely save annotation budget. To address this challenge, we devise a bachelor recognizer paying attention to alleviate the effect of sampling bias. Empirical results show that our proposed AL strategy can significantly improve sampling quality with good generality across different datasets, EA models and amount of bachelors.

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The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
33,089 Research products
  • Authors: Vernier, Martin; Martin Vernier: https://orcid.org/0000-0003-4381-5712;

    Surface: India, Ladakh, Sumda Rikpa Bao, Zone 4, Rock 15, Surface 1, Photograph 1. Type of documentation of the rock art site: systematic. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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  • Authors: Project, The Bern Digital Pantheon;

    Digital Pantheon

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  • Authors: Bruneau, Laurianne; Laurianne Bruneau: https://orcid.org/0000-0002-4257-1173;

    Zone: India, Changthang, Kawathang, Zone 1, Photograph 130. Type of documentation of the rock art site: surveyed. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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  • Authors: Vernier, Martin; Martin Vernier: https://orcid.org/0000-0003-4381-5712;

    Surface: India, Zanskar, Zamthang, Zone 1, Rock 84, Surface 1, Photograph 4. Type of documentation of the rock art site: systematic. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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  • Authors: The 28th International Conference on Computational Linguistics 2020; Bohn, Tanner;

    To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of text selected from the article and given more salient presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from these models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. Human evaluation also supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this problem further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Code to reproduce this work is available at https://github.com/tannerbohn/AutomaticPullQuoteSelection.

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  • Authors: Vernier, Martin; Martin Vernier: https://orcid.org/0000-0003-4381-5712;

    Surface: India, Ladakh, Kaltze Zampa, Zone 3, Rock 9, Surface 2, Photograph 1. Type of documentation of the rock art site: systematic. Thesaurus: http://opentheso.huma-num.fr/opentheso/api/ark:/19500/hrdthvzabqr25

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