Proceedings of the 11th International Workshop on Semantic Evaluation
          (SemEval-2017)

Bethard, Steven; Carpuat, Marine; Apidianaki, Marianna; Mohammad, Saif M.; Cer, Daniel; Jurgens, David

doi:10.18653/v1/s17-2

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…The systematic creation of benchmarks and the comparative performance analysis of resources, representations and algorithms is instrumental for moving the boundaries of natural language interpretation. SemEval (May et al, 2019;Apidianaki et al, 2018;Bethard et al, 2017Bethard et al, , 2016Nakov et al, 2015;Nakov and Zesch, 2014;Manandhar and Yuret, 2013;Agirre et al, 2012) is the primary venue in the NLP community for the organisation of shared NLP tasks and challenges. SemEval is organised as an annual workshop co-located with the main NLP conferences and has attracted a large and growing audience of task organisers and participants.…”

Section: Introductionmentioning

confidence: 99%

What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP

Wysocki

Florea²,

Freitas

2021

Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

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SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by Se-mEval.

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Section: Introductionmentioning

confidence: 99%

What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP

Wysocki

Florea²,

Freitas

2021

Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

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Data-driven approaches in FinTech: a survey

Tian

Han

2021

IDD

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Purpose This paper aims to explore the latest study of the emerging data-driven approach in the area of FinTech. This paper attempts to provide comprehensive comparisons, including the advantages and disadvantages of different data-driven algorithms applied to FinTech. This paper also attempts to point out the future directions of data-driven approaches in the FinTech domain. Design/methodology/approach This paper explores and summarizes the latest data-driven approaches and algorithms applied in FinTech to the following categories: risk management, data privacy protection, portfolio management, and sentiment analysis. Findings This paper details out comparison between different existed works in FinTech with traditional data analytics techniques and the latest development. The framework for the analysis process is developed, and insights regarding the implementation, regulation and workforce development are provided in this area. Originality/value To the best of the authors’ knowledge, this paper is first to consider broad aspects of data-driven approaches in the application of FinTech industry to explore the potential, challenges and limitations of this area. This study provides a valuable reference for both the current and future participants.

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Beyond facts – a survey and conceptualisation of claims in online discourse analysis

Boland

Fafalios

Tchechmedjiev³

et al. 2022

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Analyzing statements of facts and claims in online discourse is subject of a multitude of research areas. Methods from natural language processing and computational linguistics help investigate issues such as the spread of biased narratives and falsehoods on the Web. Related tasks include fact-checking, stance detection and argumentation mining. Knowledge-based approaches, in particular works in knowledge base construction and augmentation, are concerned with mining, verifying and representing factual knowledge. While all these fields are concerned with strongly related notions, such as claims, facts and evidence, terminology and conceptualisations used across and within communities vary heavily, making it hard to assess commonalities and relations of related works and how research in one field may contribute to address problems in another. We survey the state-of-the-art from a range of fields in this interdisciplinary area across a range of research tasks. We assess varying definitions and propose a conceptual model – Open Claims – for claims and related notions that takes into consideration their inherent complexity, distinguishing between their meaning, linguistic representation and context. We also introduce an implementation of this model by using established vocabularies and discuss applications across various tasks related to online discourse analysis.

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Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Cited by 3 publications

References 22 publications

What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP

What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP

Data-driven approaches in FinTech: a survey

Beyond facts – a survey and conceptualisation of claims in online discourse analysis

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