Heui-Seok Lim

Also published as: Heuiseok Lim


pdf bib
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning
Jungwoo Lim | Dongsuk Oh | Yoonna Jang | Kisu Yang | Heuiseok Lim
Proceedings of the 28th International Conference on Computational Linguistics

CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question. To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task. This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph. Moreover, ACP-based models are shown to outperform the baselines.


pdf bib
Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging
Andrew Matteson | Chanhee Lee | Youngbum Kim | Heuiseok Lim
Proceedings of the 27th International Conference on Computational Linguistics

Due to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features known as graphemes or otherwise utilizes comprehensive prior linguistic knowledge (i.e., a dictionary of known morphological transformation forms, or actions). These models have been created with the assumption that character-level, dictionary-less morphological analysis was intractable due to the number of actions required. We present, in this study, a multi-stage action-based model that can perform morphological transformation and part-of-speech tagging using arbitrary units of input and apply it to the case of character-level Korean morphological analysis. Among models that do not employ prior linguistic knowledge, we achieve state-of-the-art word and sentence-level tagging accuracy with the Sejong Korean corpus using our proposed data-driven Bi-LSTM model.

pdf bib
Character-Level Feature Extraction with Densely Connected Networks
Chanhee Lee | Young-Bum Kim | Dongyub Lee | Heuiseok Lim
Proceedings of the 27th International Conference on Computational Linguistics

Generating character-level features is an important step for achieving good results in various natural language processing tasks. To alleviate the need for human labor in generating hand-crafted features, methods that utilize neural architectures such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) to automatically extract such features have been proposed and have shown great results. However, CNN generates position-independent features, and RNN is slow since it needs to process the characters sequentially. In this paper, we propose a novel method of using a densely connected network to automatically extract character-level features. The proposed method does not require any language or task specific assumptions, and shows robustness and effectiveness while being faster than CNN- or RNN-based methods. Evaluating this method on three sequence labeling tasks - slot tagging, Part-of-Speech (POS) tagging, and Named-Entity Recognition (NER) - we obtain state-of-the-art performance with a 96.62 F1-score and 97.73% accuracy on slot tagging and POS tagging, respectively, and comparable performance to the state-of-the-art 91.13 F1-score on NER.


pdf bib
A Syllable Based Word Recognition Model for Korean Noun Extraction
Do-Gil Lee | Hae-Chang Rim | Heui-Seok Lim
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics


pdf bib
Automatic Word Spacing Using Hidden Markov Model for Refining Korean Text Corpora
Do-Gil Lee | Sang-Zoo Lee | Hae-Chang Rim | Heui-Seok Lim
COLING-02: The 3rd Workshop on Asian Language Resources and International Standardization


pdf bib
KCAT: A Korean Corpus Annotating Tool Minimizing Human Intervention
Won-He Ryu | Jin-Dong Kim | Hae-Chang Rim | Heui-Seok Lim
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics