Byoung-Kee Yi

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Two classifiers -Support Vector Machine (SVM) and Conditional Random Fields (CRFs) are applied here for the recognition of biomedical named entities. According to their different characteristics, the results of two classifiers are merged to achieve better performance. We propose an automatic corpus expansion method for SVM and CRF to overcome the shortage(More)
In many applications, the data of interest comprises multiple sequences that evolve over time. Examples include currency exchange rates, network traffic data. We develop a fast method to analyze such co-evolving time sequences jointly to allow (a) estimation/forecasting of missing/delayed/future values, (b) quantitative data mining,and (c) outlier(More)
As multimedia applications spread widely, it is crucial for programming and design support systems to handle “time” in multimedia documents effectively and flexibly. This paper presents a set of interactive system support tools for designing and maintaining the temporal behavior of multimedia documents. The tool set provides mechanisms for anomaly(More)
With the advent of ubiquitous computing, we can easily collect large-scale trajectory data, say, from moving vehicles. This paper studies pattern-matching problems for trajectory data over road networks, which complements existing efforts focusing on (1) a spatiotemporal window query for location-based service or (2) euclidean space with no restriction. In(More)
Nowadays, the need for effective integrated health information management is very high, with which each individual can monitor his/her health status and the ever increasing healthcare cost can be significantly reduced therefore. Moreover, advancements in information, communication and sensor technology have made readily available many types of highly(More)
SUMMARY POSBIOTM-NER is a trainable biomedical named-entity recognition system. POSBIOTM-NER can be automatically trained and adapted to new datasets without performance degradation, using CRF (conditional random field) machine learning techniques and automatic linguistic feature analysis. Currently, we have trained our system on three different datasets.(More)