Quotation Detection and Classification with a Corpus-Agnostic Model

Papay, Sean; Padó, Sebastian

doi:10.26615/978-954-452-056-4_103

Cited by 10 publications

(13 citation statements)

References 11 publications

(22 reference statements)

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“…However, our interface is modular and can easily be extended to any number of traits. For example, we can enhance speech analysis by integrating indirect speeches [54], a third-person narration of discourse, for the characters. Similarly, we can integrate social ties between characters (e.g., parents, brothers) as a new indicator [17].…”

Section: Future Workmentioning

confidence: 99%

Portrayal: Leveraging NLP and Visualization for Analyzing Fictional Characters

Hoque

Ghai

Kraus

et al. 2023

Proceedings of the 2023 ACM Designing Interactive Systems Conference

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Section: Future Workmentioning

confidence: 99%

Portrayal: Leveraging NLP and Visualization for Analyzing Fictional Characters

Hoque

Ghai

Kraus

et al. 2023

Proceedings of the 2023 ACM Designing Interactive Systems Conference

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“…In addition to quote recommendation, there are some other quote-related tasks. For example, quote detection (or recognition) that is aimed at locating spans of quotes in text (Pouliquen et al, 2007;Scheible et al, 2016;Pareti et al, 2013;Papay and Padó, 2019), and quote attribution that intends to automatically attribute quotes to speakers in the text (Elson and McKeown, 2010;O'Keefe et al, 2012;Almeida et al, 2014;Muzny et al, 2017). Different from quote recommendation that focuses on famous quotes, these tasks mainly deal with the general quotes of utterance.…”

Section: Other Quote-related Tasksmentioning

confidence: 99%

QuoteR: A Benchmark of Quote Recommendation for Writing

Qi¹,

Yang²,

Yi³

et al. 2022

Preprint

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It is very common to use quotations (quotes) to make our writings more elegant or convincing. To help people find appropriate quotes more efficiently, the task of quote recommendation is presented, aiming to recommend quotes that fit the current context of writing. There have been various quote recommendation approaches, but they are evaluated on different unpublished datasets. To facilitate the research on this task, we build a large and fully open quote recommendation dataset called QuoteR, which comprises three parts including English, standard Chinese and classical Chinese. Any part of it is larger than previous unpublished counterparts. We conduct an extensive evaluation of existing quote recommendation methods on QuoteR. Furthermore, we propose a new quote recommendation model that significantly outperforms previous methods on all three parts of QuoteR. All the code and data of this paper are available at https: //github.com/thunlp/QuoteR.

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“…There is a study on quotation extraction using deep learning technology, but it focuses only on how to extract corpus agnostic quotations. The study defines a neural architecture called neural quotation detection to predict quotes directly without explicitly identifying cues (Papay and Padó, 2019).…”

Section: Quotation Extraction and Attribution Taskmentioning

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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Quotation Detection and Classification with a Corpus-Agnostic Model

Cited by 10 publications

References 11 publications

Portrayal: Leveraging NLP and Visualization for Analyzing Fictional Characters

Portrayal: Leveraging NLP and Visualization for Analyzing Fictional Characters

QuoteR: A Benchmark of Quote Recommendation for Writing

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

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