Keeping Their Words: Direct and Indirect Chinese Quote Attribution from Newspapers

Lee, Kuan-Lin; Cheng, Yuchung; Chen, Pai-Lin; Huang, Hen-Hsen

doi:10.1145/3366424.3382716

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…The existing corpus widely used in quotation extraction and attribution is mostly available in English (Pareti, 2016;Newell et al, 2018;Zhang and Liu, 2022) and Portuguese (Fernandes et al, 2011). Other studies have built language corpora in Chinese (Lee et al, 2020), Norwegian (Salway et al, 2017), Italian (Pareti and Prodanof, 2010), German (Li et al, 2012), Dutch (Atteveldt, 2013) and Arabic (Al-Saif et al, 2018).…”

Section: Recent Workmentioning

confidence: 99%

PFSA-ID: an annotated Indonesian corpus and baseline model of public figures statements attributions

W.P.

Kumar

Zulkarnain

2022

GKMC

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Purpose By far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language. Design/methodology/approach The methodology is divided into corpus development and extraction model development. During corpus development, data were collected and annotated. The development of the extraction model entails feature extraction, the definition of the model architecture, parameter selection and configuration, model training and evaluation, as well as model selection. Findings The Indonesian corpus of public figure statements attribution achieved 90.06% agreement level between the annotator and experts and could serve as a gold standard corpus. Furthermore, the baseline model predicted most labels and achieved 82.026% F-score. Originality/value To the best of the authors’ knowledge, the resulting corpus is the first corpus for attribution of public figures’ statements in the Indonesian language, which makes it a significant step for research on attribution extraction in the language. The resulting corpus and the baseline model can be used as a benchmark for further research. Other researchers could follow the methods presented in this paper to develop a new corpus and baseline model for other languages.

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Section: Recent Workmentioning

confidence: 99%

PFSA-ID: an annotated Indonesian corpus and baseline model of public figures statements attributions

W.P.

Kumar

Zulkarnain

2022

GKMC

View full text Add to dashboard Cite

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“…Machine learning is often used to solve the task of linking text span to speakers. Sequence labelling using machine learning has been used to assign each quote to the appropriate speaker (O’Keefe et al , 2012; Pareti et al , 2013; Yeung and Lee, 2017; Lee et al , 2020). Both the studies by O’Keefe and Pareti above also attempted to apply the no sequence labelling method.…”

Section: Quotation Extraction and Attribution Taskmentioning

confidence: 99%

“…The corpus that exists today and widely used in research is mostly available in English (Pareti, 2016; Newell et al , 2018) and Portuguese (Fernandes et al , 2011). Other studies have tried to build corpus in Chinese (Lee et al , 2020), Norwegian (Salway et al , 2017), Italian (Pareti and Prodanof, 2010), German (Li et al , 2012), Dutch (Atteveldt, 2013) and Arabic (Alsaif et al , 2018). There is an opportunity to create a new corpus with different languages that uses a standard scheme for named entity recognition (NER) task and add more attributes to enrich the knowledge.…”

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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Keeping Their Words: Direct and Indirect Chinese Quote Attribution from Newspapers

Cited by 2 publications

References 2 publications

PFSA-ID: an annotated Indonesian corpus and baseline model of public figures statements attributions

PFSA-ID: an annotated Indonesian corpus and baseline model of public figures statements attributions

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

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