Young-Min Kim

Also published as: Young Min Kim


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Transformed Protoform Reconstruction
Young Min Kim | Kalvin Chang | Chenxuan Cui | David R. Mortensen
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Protoform reconstruction is the task of inferring what morphemes or words appeared like in the ancestral languages of a set of daughter languages. Meloni et al (2021) achieved the state-of-the-art on Latin protoform reconstruction with an RNN-based encoder-decoder with attention model. We update their model with the state-of-the-art seq2seq model: the Transformer. Our model outperforms their model on a suite of different metrics on two different datasets: their Romance data of 8,000 cognates spanning 5 languages and a Chinese dataset (Hou 2004) of 800+ cognates spanning 39 varieties. We also probe our model for potential phylogenetic signal contained in the model. Our code is publicly available at


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Insurance Question Answering via Single-turn Dialogue Modeling
Seon-Ok Na | Young-Min Kim | Seung-Hwan Cho
Proceedings of the Second Workshop on When Creative AI Meets Conversational AI

With great success in single-turn question answering (QA), conversational QA is currently receiving considerable attention. Several studies have been conducted on this topic from different perspectives. However, building a real-world conversational system remains a challenge. This study introduces our ongoing project, which uses Korean QA data to develop a dialogue system in the insurance domain. The goal is to construct a system that provides informative responses to general insurance questions. We present the current results of single-turn QA. A unique aspect of our approach is that we borrow the concepts of intent detection and slot filling from task-oriented dialogue systems. We present details of the data construction process and the experimental results on both learning tasks.


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Investigating the Image of Entities in Social Media: Dataset Design and First Results
Julien Velcin | Young-Min Kim | Caroline Brun | Jean-Yves Dormagen | Eric SanJuan | Leila Khouas | Anne Peradotto | Stephane Bonnevay | Claude Roux | Julien Boyadjian | Alejandro Molina | Marie Neihouser
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The objective of this paper is to describe the design of a dataset that deals with the image (i.e., representation, web reputation) of various entities populating the Internet: politicians, celebrities, companies, brands etc. Our main contribution is to build and provide an original annotated French dataset. This dataset consists of 11527 manually annotated tweets expressing the opinion on specific facets (e.g., ethic, communication, economic project) describing two French policitians over time. We believe that other researchers might benefit from this experience, since designing and implementing such a dataset has proven quite an interesting challenge. This design comprises different processes such as data selection, formal definition and instantiation of an image. We have set up a full open-source annotation platform. In addition to the dataset design, we present the first results that we obtained by applying clustering methods to the annotated dataset in order to extract the entity images.


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Annotated Bibliographical Reference Corpora in Digital Humanities
Young-Min Kim | Patrice Bellot | Elodie Faath | Marin Dacos
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper, we present new bibliographical reference corpora in digital humanities (DH) that have been developed under a research project, Robust and Language Independent Machine Learning Approaches for Automatic Annotation of Bibliographical References in DH Books supported by Google Digital Humanities Research Awards. The main target is the bibliographical references in the articles of site, an oldest French online journal platform in DH field. Since the final object is to provide automatic links between related references and articles, the automatic recognition of reference fields like author and title is essential. These fields are therefore manually annotated using a set of carefully defined tags. After providing a full description of three corpora, which are separately constructed according to the difficulty level of annotation, we briefly introduce our experimental results on the first two corpora. A popular machine learning technique, Conditional Random Field (CRF) is used to build a model, which automatically annotates the fields of new references. In the experiments, we first establish a standard for defining features and labels adapted to our DH reference data. Then we show our new methodology against less structured references gives a meaningful result.