Soo-Hwan Kim

From WikiPapers
Jump to: navigation, search

Soo-Hwan Kim is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
A composite kernel approach for dialog topic tracking with structured domain knowledge from Wikipedia 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference English 2014 Dialog topic tracking aims at analyzing and maintaining topic transitions in ongoing dialogs. This paper proposes a composite kernel approach for dialog topic tracking to utilize various types of domain knowledge obtained from Wikipedia. Two kernels are defined based on history sequences and context trees constructed based on the extracted features. The experimental results show that our composite kernel approach can significantly improve the performances of topic tracking in mixed-initiative human-human dialogs. 0 0
Wikipedia-based Kernels for dialogue topic tracking Dialogue Topic Tracking
Kernel Methods
Spoken Dialogue Systems
Wikipedia
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings English 2014 Dialogue topic tracking aims to segment on-going dialogues into topically coherent sub-dialogues and predict the topic category for each next segment. This paper proposes a kernel method for dialogue topic tracking to utilize various types of information obtained from Wikipedia. The experimental results show that our proposed approach can significantly improve the performances of the task in mixed-initiative humanhuman dialogues. 0 0
Modeling topic hierarchies with the recursive Chinese restaurant process Bayesian nonparametric models
Hierarchical topic modeling
ACM International Conference Proceeding Series English 2012 Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. While they are simple and popular, a major shortcoming of LDA and HDP is that they do not organize the topics into a hierarchical structure which is naturally found in many datasets. We introduce the recursive Chinese restaurant process (rCRP) and a nonparametric topic model with rCRP as a prior for discovering a hierarchical topic structure with unbounded depth and width. Unlike previous models for discovering topic hierarchies, rCRP allows the documents to be generated from a mixture over the entire set of topics in the hierarchy. We apply rCRP to a corpus of New York Times articles, a dataset of MovieLens ratings, and a set of Wikipedia articles and show the discovered topic hierarchies. We compare the predictive power of rCRP with LDA, HDP, and nested Chinese restaurant process (nCRP) using heldout likelihood to show that rCRP outperforms the others. We suggest two metrics that quantify the characteristics of a topic hierarchy to compare the discovered topic hierarchies of rCRP and nCRP. The results show that rCRP discovers a hierarchy in which the topics become more specialized toward the leaves, and topics in the immediate family exhibit more affinity than topics beyond the immediate family. 0 0
Multilingual named entity recognition using parallel data and metadata from wikipedia 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference English 2012 In this paper we propose a method to automatically label multi-lingual data with named entity tags. We build on prior work utilizing Wikipedia metadata and show how to effectively combine the weak annotations stemming from Wikipedia metadata with information obtained through English-foreign language parallelWikipedia sentences. The combination is achieved using a novel semi-CRF model for foreign sentence tagging in the context of a parallel English sentence. The model outperforms both standard annotation projection methods and methods based solely on Wikipedia metadata. 0 0
The study on effective programming learning using wiki community systems Knowledge management
Programming learning
Wiki system
EC-TEL English 2006 0 0