Toward matching the relation instantiation from DBpedia ontology to Wikipedia text

Hahm, Younggyun; Kim, Young‐Sik; Won, Yousung; Woo, Jeong-Ho; Seo, Jiwoo; Kim, Jiseong; Park, Seong-Bae; Hwang, Dosam; Choi, Key‐Sun

doi:10.1145/2660517.2660534

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…Numerous studies have been conducted on NER concerning several natural languages, including Carreras et al (2003) for Catalan, Li and McCallum (2003) for Hindi, Nouvel et al (2013) and Park (2018) for French, Benikova, Biemann, and Reznicek (2014); Benikova et al (2015) for German, and Ahmadi and Moradi (2015) for Persian. There have also been language-independent multilingual models proposed (Tjong Kim Sang and De Meulder 2003;Nothman et al 2013;Hahm et al 2014). However, NER for the Korean language has not been explored thoroughly in previous research.…”

Section: Introductionmentioning

confidence: 99%

“…Although gold standard corpora, such as CoNLL-03 (Tjong Kim Sang and De Meulder 2003), are still not available for the Korean language, it is probable that by constructing silver standard corpora with automatic approaches, both NE annotation and NER modeling can be handled decently. A silver standard NE corpus in which NE annotations are completed automatically has been constructed based on Wikipedia and DBpedia Ontology including Korean (Hahm et al 2014). This high-quality corpus outperforms the manually annotated corpus.…”

Section: Introductionmentioning

confidence: 99%

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Korean named entity recognition based on language-specific features

2023

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In this paper, we propose a novel way of improving named entity recognition (NER) in the Korean language using its language-specific features. While the field of NER has been studied extensively in recent years, the mechanism of efficiently recognizing named entities (NEs) in Korean has hardly been explored. This is because the Korean language has distinct linguistic properties that present challenges for modeling. Therefore, an annotation scheme for Korean corpora by adopting the CoNLL-U format, which decomposes Korean words into morphemes and reduces the ambiguity of NEs in the original segmentation that may contain functional morphemes such as postpositions and particles, is proposed herein. We investigate how the NE tags are best represented in this morpheme-based scheme and implement an algorithm to convert word-based and syllable-based Korean corpora with NEs into the proposed morpheme-based format. Analyses of the results of traditional and neural models reveal that the proposed morpheme-based format is feasible, and the varied performances of the models under the influence of various additional language-specific features are demonstrated. Extrinsic conditions were also considered to observe the variance of the performances of the proposed models, given different types of data, including the original segmentation and different types of tagging formats.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Korean named entity recognition based on language-specific features

2023

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“…In addition to the English and Finnish FrameNet projects, FrameNet-inspired research also exist for German (Burchardt et al 2006), Spanish (Subirats and Petruck 2003), Swedish (Borin et al 2010), Brazilian Portuguese (Duran and Aluísio 2011), Japanese (Ohara et al 2004), Chinese (You and Liu 2005), Korean (Hahm et al 2014), French (Meurs et al 2008), Danish (Bick 2011), Polish (Zawisławska et al 2008, Italian (Tonelli and Pianta 2008;Lenci et al 2010), Slovenian (Lönneker-Rodman 2007 and Hebrew (Hayoun and Elhadad 2015). For a list of FrameNets in other languages, see Appendix.…”

Section: Berkeley Framenetmentioning

confidence: 99%

FinnTransFrame: translating frames in the FinnFrameNet project

Lindén

Haltia

Laine

et al. 2018

Lang Resources & Evaluation

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The article details the formational process of the FinnTransFrame corpus, a part of the FinnFrameNet project. In addition to a large annotated frame semantic corpus of natural language examples, the project created a separate corpus of examples translated from English to Finnish. The research question when creating the FinnTransFrame corpus was to see to what extent the various frames of the original Berkeley FrameNet transfer into Finnish in translated examples, i.e. what are the main problems and how can they be categorized? A variety of Berkeley FrameNet examples were chosen from different frames and then translated by professionals. The FinnFrameNet annotation team checked all the examples and their translations to see if the frames remained intact in translation. Problematic examples were tagged according to the type of the encountered problem, with the main focus on the type of fine-grained mismatches of meaning that caused frame changes even when the translation was the best possible one. The frame-loss amounted to 4.2% of the 88,209 relevant example sentences. Filtering out sentences & Krister Lindén

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“…In addition to English and Finnish, FrameNet-related projects and FrameNet-inspired research also exists in German (Burchardt et al 2006), Spanish (Subirats & Petruck 2003), Swedish (Borin et al 2010), Brazilian Portuguese (Duran & Aluísio 2011), Japanese (Ohara et al 2004), Chinese (You & Liu 2005), Korean (Hahm et al 2014), French (Meurs et al 2008), Danish (Bick 2011), Polish (Zawisławska, Derwojedowa & Linde-Usiekniewicz 2008), Italian (Tonelli & Pianta 2008, Lenci, Johnson & Lapesa 2010), Slovenian (Lönneker-Rodman 2007a) and Hebrew (Hayoun & Elhadad 2015). For a list of FrameNets in other languages, see Appendix.…”

Section: Introductionmentioning

confidence: 99%

FinnFN 1.0: The Finnish frame semantic database

Lindén¹,

Haltia²,

Luukkonen³

et al. 2017

Nord J Linguist

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The article describes the process of creating a Finnish language FrameNet or FinnFN, based on the original English language FrameNet hosted at the International Computer Science Institute in Berkeley, California. We outline the goals and results relating to the FinnFN project and especially to the creation of the FinnFrame corpus. The main aim of the project was to test the universal applicability of frame semantics by annotating real Finnish using the same frames and annotation conventions as in the original Berkeley FrameNet project. From Finnish newspaper corpora, 40,721 sentences were automatically retrieved and manually annotated as example sentences evoking certain frames. This became the FinnFrame corpus. Applying the Berkeley FrameNet annotation conventions to the Finnish language required some modifications due to Finnish morphology, and a convention for annotating individual morphemes within words was introduced for phenomena such as compounding, comparatives and case endings. Various questions about cultural salience across the two languages arose during the project, but problematic situations occurred only in a few examples, which we also discuss in the article. The article shows that, barring a few minor instances, the universality hypothesis of frames is largely confirmed for languages as different as Finnish and English.

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Toward matching the relation instantiation from DBpedia ontology to Wikipedia text

Cited by 4 publications

References 8 publications

Korean named entity recognition based on language-specific features

Korean named entity recognition based on language-specific features

FinnTransFrame: translating frames in the FinnFrameNet project

FinnFN 1.0: The Finnish frame semantic database

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