Search Results for author: Jungyeul Park

Found 18 papers, 3 papers with code

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

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

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 +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.


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.

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.

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.

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.

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