KyungTae Lim

Also published as: Kyungtae Lim


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Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian
KyungTae Lim | Niko Partanen | Thierry Poibeau
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations
KyungTae Lim | Cheoneum Park | Changki Lee | Thierry Poibeau
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We describe the SEx BiST parser (Semantically EXtended Bi-LSTM parser) developed at Lattice for the CoNLL 2018 Shared Task (Multilingual Parsing from Raw Text to Universal Dependencies). The main characteristic of our work is the encoding of three different modes of contextual information for parsing: (i) Treebank feature representations, (ii) Multilingual word representations, (iii) ELMo representations obtained via unsupervised learning from external resources. Our parser performed well in the official end-to-end evaluation (73.02 LAS – 4th/26 teams, and 78.72 UAS – 2nd/26); remarkably, we achieved the best UAS scores on all the English corpora by applying the three suggested feature representations. Finally, we were also ranked 1st at the optional event extraction task, part of the 2018 Extrinsic Parser Evaluation campaign.

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Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations
Niko Partanen | Kyungtae Lim | Michael Rießler | Thierry Poibeau
Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages

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Affordances in Grounded Language Learning
Stephen McGregor | KyungTae Lim
Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing

We present a novel methodology involving mappings between different modes of semantic representation. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, motivated by insight from ecological psychology, we describe a model approximating affordances, by which we mean a language learner’s direct perception of opportunities for action in an environment. We present a preliminary experiment involving mapping between these two representational modalities, and propose that our methodology can become the basis for a cognitively inspired model of grounded language learning.

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The First Komi-Zyrian Universal Dependencies Treebanks
Niko Partanen | Rogier Blokland | KyungTae Lim | Thierry Poibeau | Michael Rießler
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

Two Komi-Zyrian treebanks were included in the Universal Dependencies 2.2 release. This article contextualizes the treebanks, discusses the process through which they were created, and outlines the future plans and timeline for the next improvements. Special attention is paid to the possibilities of using UD in the documentation and description of endangered languages.


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A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations
KyungTae Lim | Thierry Poibeau
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with “Multilingual Parsing from Raw Text to Universal Dependencies”. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings for languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5 th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score).


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Named Entity Corpus Construction using Wikipedia and DBpedia Ontology
Younggyun Hahm | Jungyeul Park | Kyungtae Lim | Youngsik Kim | Dosam Hwang | Key-Sun Choi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we propose a novel method to automatically build a named entity corpus based on the DBpedia ontology. Since most of named entity recognition systems require time and effort consuming annotation tasks as training data. Work on NER has thus for been limited on certain languages like English that are resource-abundant in general. As an alternative, we suggest that the NE corpus generated by our proposed method, can be used as training data. Our approach introduces Wikipedia as a raw text and uses the DBpedia data set for named entity disambiguation. Our method is language-independent and easy to be applied to many different languages where Wikipedia and DBpedia are provided. Throughout the paper, we demonstrate that our NE corpus is of comparable quality even to the manually annotated NE corpus.


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Korean NLP2RDF Resources
YoungGyun Hahm | KyungTae Lim | Jungyeul Park | Yongun Yoon | Key-Sun Choi
Proceedings of the 10th Workshop on Asian Language Resources