no code implementations • 22 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.
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.
no code implementations • WS 2017 • Jungyeul Park, Lo{\"\i}c Dugast, Jeen-Pyo Hong, Chang-Uk Shin, Jeong-Won Cha
We propose a novel method to bootstrap the construction of parallel corpora for new pairs of structurally different languages.
no code implementations • WS 2012 • YoungGyun Hahm, Kyungtae Lim, Jungyeul Park, Yongun Yoon, Key-Sun Choi
no code implementations • LREC 2014 • Younggyun Hahm, Jungyeul Park, Kyungtae Lim, Youngsik Kim, Dosam Hwang, Key-Sun Choi
In this paper, we propose a novel method to automatically build a named entity corpus based on the DBpedia ontology.
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.
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).
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.
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.
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.
no code implementations • JEPTALNRECITAL 2018 • Jungyeul Park
Nous proposons trois nouvelles m{\'e}thodes pour construire et optimiser des plongements de mots pour le fran{\c{c}}ais.
no code implementations • 10 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.
no code implementations • 29 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.
no code implementations • 7 Sep 2023 • Jungyeul Park, Mija Kim
This paper describes word {segmentation} granularity in Korean language processing.
no code implementations • 24 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.
no code implementations • 24 Feb 2024 • Min Zeng, Jiexin Kuang, Mengyang Qiu, Jayoung Song, Jungyeul Park
The writing examples of English language learners may be different from those of native speakers.
no code implementations • 27 Feb 2024 • Izia Xiaoxiao Wang, Xihan Wu, Edith Coates, Min Zeng, Jiexin Kuang, Siliang Liu, Mengyang Qiu, Jungyeul Park
The utilization of technology in second language learning and teaching has become ubiquitous.
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.
1 code implementation • 10 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.
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.