The Computable News project

Radford, Will; Tse, Daniel; Nothman, Joel; Hachey, Ben; Wright, George; Curran, James; Cannings, Will; O'Keefe, Tim; Honnibal, Matthew; Vadas, David; Loxley, Candice

doi:10.1145/2740908.2742009

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…In 2011, the TAC-KBP challenge [22] introduced a task that includes NIL clustering; in the NEEL challenge [36] that is based on microposts, NIL clustering was part of the task as well. Approaches that tackled these tasks typically applied clustering based on similarity measures over the entity mentions in the text [6,12,15,29,32]. More recently, Angell et al [3] train two separate bi-encoders and cross-encoders to compute inter-mention and mention-entity affinities.…”

Section: Related Workmentioning

confidence: 99%

NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker

Heist¹,

Paulheim²

2023

Preprint

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Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the reference knowledge base is complete. In practice, however, it is necessary to deal with the case of linking to an entity that is not contained in the knowledge base (NIL entity). Recent works have shown that, instead of focusing only on affinities between mentions and entities, considering inter-mention affinities can be used to represent NIL entities by producing clusters of mentions. At the same time, inter-mention affinities can help to substantially improve linking performance for known entities. With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities. The approach clusters mentions and entities based on dense representations from Transformers and resolves conflicts (if more than one entity is assigned to a cluster) by computing transitive mention-entity affinities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL entities. Further, we apply the presented approach to an actual EL task, namely to knowledge graph population by linking entities in Wikipedia listings, and provide an analysis of the outcome.

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Section: Related Workmentioning

confidence: 99%

NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker

Heist¹,

Paulheim²

2023

Preprint

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“…Opinion Mining Another strain focuses on characterizing voices in a text by opinion . Such work has been applied in computational platforms for journalists (Radford et al, 2015) and in fake news detection (Conforti et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Identifying Informational Sources in News Articles

Spangher,

Peng,

Ferrara

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We first show that our dataset can be used to train high-performing models for information detection and source attribution. Then, we introduce a novel task, source prediction, to study the compositionality of sources in news articles -i.e. how they are chosen to complement each other. We show good modeling performance on this task, indicating that there is a pattern to the way different sources are used together in news storytelling. This insight opens the door for a focus on sources in narrative science (i.e. planningbased language generation) and computational journalism (i.e. a source-recommendation system to aid journalists writing stories). 1

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“…Journalists, particularly in the current age of rampant media manipulators and fake news outlets, must try to preserve their integrity to foster transparency in journalism (Jurado, 2020). Thus, accurate extraction and attribution are important to the news platform (Radford et al , 2015). In some news, readers can find politicians’ statements in the form of direct quotations (Syaifudin and Nurwidyantoro, 2017).…”

Section: Introductionmentioning

confidence: 99%

Understanding quotation extraction and attribution: towards automatic extraction of public figure’s statements for journalism in Indonesia

Purnomo

Kumar

Zulkarnain

2020

GKMC

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Purpose Extracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is one type of information that can be processed into structured data. Therefore, having the knowledge base about this data will be very beneficial for further use, such as for opinion mining, claim detection and fact-checking. This study aims to understand statement extraction tasks and the models that have already been applied to formulate a framework for further study. Design/methodology/approach This paper presents a literature review from selected previous research that specifically addresses the topics of quotation extraction and quotation attribution. Research works that discuss corpus development related to quotation extraction and quotation attribution are also considered. The findings of the review will be used as a basis for proposing a framework to direct further research. Findings There are three findings in this study. Firstly, the extraction process still consists of two main tasks, namely, the extraction of quotations and the attribution of quotations. Secondly, most extraction algorithms rely on a rule-based algorithm or traditional machine learning. And last, the availability of corpus, which is limited in quantity and depth. Based on these findings, a statement extraction framework for Indonesian language corpus and model development is proposed. Originality/value The paper serves as a guideline to formulate a framework for statement extraction based on the findings from the literature study. The proposed framework includes a corpus development in the Indonesian language and a model for public figure statement extraction. Furthermore, this study could be used as a reference to produce a similar framework for other languages.

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The Computable News project

Cited by 6 publications

References 4 publications

NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker

NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker

Identifying Informational Sources in News Articles

Understanding quotation extraction and attribution: towards automatic extraction of public figure’s statements for journalism in Indonesia

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