In this work we develop a new system for automated activity recognition in an Intensive Care Unit. Our system is capable of recognizing a set of 7 high-level activities using only 3D perception. We take a three step approach to classifying actions from depth images. First, temporal action sequences are created by segmenting and tracking people throughout several hours of video footage. Second, ...

This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors. Specifically, we detect a new semantic relation by projecting the new relation’s training instances onto a lower dimension topic space constructed from existing relati...

We present an approach for semantic relation extraction between nominals that combines semantic information with shallow syntactic processing. We propose to use the ResearchCyc knowledge base as a source of semantic information about nominals. Each source of information is represented by a specific kernel function. The experiments were carried out using support vector machines as a classifier. ...

Developing methods that can identify important patterns in complex large-scale temporal datasets is one of the key challenges in machine learning and data mining research. Our work focuses on the development of methods that can, based on past data, identify unusual patient-management actions in the Electronic Medical Record (EMR) of the current patient and raise alerts if such actions are encou...

The objective of the SALA (SpeechDat across Latin America) project is to record large SpeechDat-like databases to train telephone speech recognisers for any country in Latin America. The SALA consortium is composed by several European The Netherlands). This paper gives an overview of the project, introduces the definition of the databases, shows the dialectal distribution in the countries where...

In order to facilitate content based information retrieval we need methods for analyzing the semantic structures in the text. In order to facilitate this, we propose a straightforward method for identifying semantic relations between two concepts in an ontology based on supervised machine learning. More specifically we have achieved good results in trying to identify the relation a preposition ...

In this paper, we propose the application of Machine Learning (ML) methods to the Semantic Web (SW) as a mechanism to predict the correctness of semantic relations. For this purpose, we have acquired a learning dataset from the SW and we have performed an extensive experimental evaluation covering more than 1,800 relations of various types. We have obtained encouraging results, reaching a maxim...

In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. The QBK is used here for data transformation prior to classification in a medical problem concerning the prediction of mortality in patients suffering severe sepsis. This is a...

This paper presents a classification-based approach to noun-noun compound interpretation within the statistical learning framework of kernel methods. In this framework, the primary modelling task is to define measures of similarity between data items, formalised as kernel functions. We consider the different sources of information that are useful for understanding compounds and proceed to defin...