Search Results for author: Jungyeul Park

Found 26 papers, 5 papers with code

A New Annotation Scheme for the Sejong Part-of-speech Tagged Corpus

1 code implementation WS 2019 Jungyeul Park, Francis Tyers

In this paper we present a new annotation scheme for the Sejong part-of-speech tagged corpus based on Universal Dependencies style annotation.

Morphological Analysis named-entity-recognition +3

Yet Another Format of Universal Dependencies for Korean

1 code implementation COLING 2022 Yige Chen, Eunkyul Leah Jo, Yundong Yao, Kyungtae Lim, Miikka Silfverberg, Francis M. Tyers, Jungyeul Park

In this study, we propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies.

Dependency Parsing

K-UniMorph: Korean Universal Morphology and its Feature Schema

1 code implementation10 May 2023 Eunkyul Leah Jo, Kyuwon Kim, Xihan Wu, Kyungtae Lim, Jungyeul Park, Chulwoo Park

This dataset adopts morphological feature schema from Sylak-Glassman et al. (2015) and Sylak-Glassman (2016) for the Korean language as we extract inflected verb forms from the Sejong morphologically analyzed corpus that is one of the largest annotated corpora for Korean.

Second-Order Belief Hidden Markov Models

no code implementations22 Jan 2015 Jungyeul Park, Mouna Chebbah, Siwar Jendoubi, Arnaud Martin

The probabilistic HMMs have been one of the most used techniques based on the Bayesian model.

Corpus Selection Approaches for Multilingual Parsing from Raw Text to Universal Dependencies

1 code implementation CONLL 2017 Ryan Hornby, Clark Taylor, Jungyeul Park

This paper describes UALing{'}s approach to the \textit{CoNLL 2017 UD Shared Task} using corpus selection techniques to reduce training data size.

Using the International Standard Language Resource Number: Practical and Technical Aspects

no code implementations LREC 2012 Khalid Choukri, Victoria Arranz, Olivier Hamon, Jungyeul Park

This paper describes the International Standard Language Resource Number (ISLRN), a new identification schema for Language Resources where a Language Resource is provided with a unique and universal name using a standardized nomenclature.

Artificial Error Generation with Fluency Filtering

no code implementations WS 2019 Mengyang Qiu, Jungyeul Park

The quantity and quality of training data plays a crucial role in grammatical error correction (GEC).

Grammatical Error Correction

Improving Precision of Grammatical Error Correction with a Cheat Sheet

no code implementations WS 2019 Mengyang Qiu, Xuejiao Chen, Maggie Liu, Krishna Parvathala, Apurva Patil, Jungyeul Park

In this paper, we explore two approaches of generating error-focused phrases and examine whether these phrases can lead to better performance in grammatical error correction for the restricted track of BEA 2019 Shared Task on GEC.

Grammatical Error Correction Machine Translation +1

Le benchmarking de la reconnaissance d'entit\'es nomm\'ees pour le fran\ccais (Benchmarking for French NER)

no code implementations JEPTALNRECITAL 2018 Jungyeul Park

Cet article pr{\'e}sente une t{\^a}che du benchmarking de la reconnaissance de l{'}entit{\'e} nomm{\'e}e (REN) pour le fran{\c{c}}ais.

Benchmarking NER

Une note sur l'analyse du constituant pour le fran\ccais (A Note on constituent parsing for French)

no code implementations JEPTALNRECITAL 2018 Jungyeul Park

Cet article traite des analyses d{'}erreurs quantitatives et qualitatives sur les r{\'e}sultats de l{'}analyse syntaxique des constituants pour le fran{\c{c}}ais.

Korean Named Entity Recognition Based on Language-Specific Features

no code implementations10 May 2023 Yige Chen, Kyungtae Lim, Jungyeul Park

In the paper, we propose a novel way of improving named entity recognition in the Korean language using its language-specific features.

named-entity-recognition Named Entity Recognition

Extrinsic Factors Affecting the Accuracy of Biomedical NER

no code implementations29 May 2023 Zhiyi Li, Shengjie Zhang, Yujie Song, Jungyeul Park

Biomedical named entity recognition (NER) is a critial task that aims to identify structured information in clinical text, which is often replete with complex, technical terms and a high degree of variability.

Data Augmentation named-entity-recognition +2

Word segmentation granularity in Korean

no code implementations7 Sep 2023 Jungyeul Park, Mija Kim

This paper describes word {segmentation} granularity in Korean language processing.

Segmentation

Frustratingly Simple Prompting-based Text Denoising

no code implementations24 Feb 2024 Jungyeul Park, Mengyang Qiu

This paper introduces a novel perspective on the automated essay scoring (AES) task, challenging the conventional view of the ASAP dataset as a static entity.

Automated Essay Scoring Denoising +1

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