Semantics in storytelling in Swedish fiction

Kokkinakis, Dimitrios; Malm, Mats; Bergenmar, Jenny; Ighe, Ann

doi:10.1145/2595188.2595208

Cited by 2 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our way is different from the work of Kokkinakis et al in a sense that we treat the texts in the speech balloons near the characters as dialogs, as we described in the previous paragraph. In their later work, Kokkinakis et al [13] created a simplified cooccurrence network for the characters in the Swedish novel "Vi Bookar, Krokar och Rothar." They did not add any semantic relations in the network.…”

Section: Creating Character Network From Literary Textsmentioning

confidence: 99%

Creating Character Networks with Kinship Relations from Comics

Murakami

Nagaoka

Kyogoku

2020

International Journal of Service and Knowledge Management

View full text Add to dashboard Cite

To find and understand comic contents, we present an approach of creating character networks with semantic relations from comics using the panels and speech balloons. First, we build a dataset that contains a comic's characters and its words in speech balloons separated by the panels. Second, we extract the semantic relations among characters using the words in the speech balloons and panels. Third, we develop character networks by identifying the important characters using the frequencies of characters and their relatedness, and then we add semantic relations to these networks. As an initial step in finding semantic relations, we focus on kinship relations. We implement our approach and successfully build character networks from four popular Japanese comics. Finally, we evaluate our kinship relation extraction algorithms and the created character networks.

show abstract

Section: Creating Character Network From Literary Textsmentioning

confidence: 99%

Creating Character Networks with Kinship Relations from Comics

Murakami

Nagaoka

Kyogoku

2020

International Journal of Service and Knowledge Management

View full text Add to dashboard Cite

show abstract

“…Named Entity Recognition (NER), the identification and extraction of words representing named people, places, and groups, is a common and relatively accurate (Dekker et al, 2019) methodology in text and data mining. Across the humanities, NER‐derived data has been used to make novel arguments about interpersonal relationships in Swedish literature (Kokkinakis et al, 2014), to trace how feminist ideas travel through novels (Moravec & Chang, 2021), and to study networks of day‐to‐day life contained in historical personal diaries (Fields et al, 2023). Outside of the humanities, NER is commonly used to mine research literature for new medical insights (Wang et al, 2020; Ramachandran & Arutchelvan, 2020), and to study manufacturing processes (Kumar & Starly, 2022) and marketing strategies in high‐end fashion (Chilet et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Tuning Out the Noise: Benchmarking Entity Extraction for Digitized Native American Literature

Parulian,

Dubnicek,

Evans

et al. 2023

Proceedings of the Association for Information Science and Tech

View full text Add to dashboard Cite

Named Entity Recognition (NER), the automated identification and tagging of entities in text, is a popular natural language processing task, and has the power to transform restricted data into open datasets of entities for further research. This project benchmarks four NER models–Stanford NER, BookNLP, spaCy‐trf and RoBERTa–to identify the most accurate approach and generate an open‐access, gold‐standard dataset of human annotated entities. To meet a real‐world use case, we benchmark these models on a sample dataset of sentences from Native American authored literature, identifying edge cases and areas of improvement for future NER work.

show abstract

Semantics in storytelling in Swedish fiction

Cited by 2 publications

References 9 publications

Creating Character Networks with Kinship Relations from Comics

Creating Character Networks with Kinship Relations from Comics

Tuning Out the Noise: Benchmarking Entity Extraction for Digitized Native American Literature

Contact Info

Product

Resources

About