Abstract
Social media platforms are used to discuss current events with very complex narratives that become difficult to understand. In this work, we introduce Tweet2Story, a web app to automatically extract narratives from small texts such as tweets and describe them through annotations. By doing this, we aim to mitigate the difficulties existing on creating narratives and give a step towards deeply understanding the actors and their corresponding relations found in a text. We build the web app to be modular and easy-to-use, which allows it to easily incorporate new techniques as they keep getting developed.
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- 1.
in the sentence “Steve Jobs was the CEO of Apple”, the entity “Steve Jobs” fits the category of “person”.
- 2.
the expression “last year” would be parsed as “2020” as of this writing.
- 3.
in the sentence “Sally lives in Paris. She lives in France”, both “Sally” and “She” refer to the same entity and, therefore, belong to the same cluster.
- 4.
in the sentence “Sally lives in Paris”, the event is expressed through the verb “lives”.
- 5.
in the sentence “Sally lives in Paris” the triple “Sally - lives - in Paris” is categorized as a location triple.
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- 8.
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Acknowledgements
Vasco Campos and Pedro Mota were financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020 and LA/P/0063/2020. Ricardo Campos and Alípio Jorge were financed by the ERDF - European Regional Development Fund through the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145-FEDER-03185). This funding fits under the research line of the Text2Story project.
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Campos, V., Campos, R., Mota, P., Jorge, A. (2022). Tweet2Story: A Web App to Extract Narratives from Twitter. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_32
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