Time-series clustering - A decade review

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artifical intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of nlp, with an emphasis on different evaluation methods and the relationships between them.

/pdf/survey-of-the-state-of-the-art-in-natural-language-26vdvzdbx0.pdf

Survey of the state of the art in natural language generation: core tasks, applications and evaluation

http://disi.unitn.it/~p2p/RelatedWork/Matching/ontology_matching_with_semantic_verification.pdf

Ontology matching with semantic verification

This paper describes SimpleNLG, a realisation engine for English which aims to provide simple and robust interfaces to generate syntactic structures and linearise them. The library is also flexible in allowing the use of mixed (canned and non-canned) representations.

http://staff.um.edu.mt/albert.gatt/pubs/enlg2009-simplenlg.pdf

SimpleNLG: A Realisation Engine for Practical Applications

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

/pdf/data-mining-for-wearable-sensors-in-health-monitoring-13a1q3cm4g.pdf

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

https://www.um.edu.mt/library/oar/bitstream/123456789/22142/1/bt45-aij.pdf

Automatic generation of textual summaries from neonatal intensive care data

Volunteer staff from a Neonatal Intensive Care Unit (NICU) were presented with sets of anonymised physiological data recorded over approximately 45 minute periods from former patients. Staff were asked to select medical/nursing actions appropriate for each of the patients whose data were displayed. Data were shown in one of three conditions (a) as multiple line graphs similar to those commonly shown on the ward, or as textual descriptions generated by (b) expert medical/nursing staff or (c) computerised natural language generation (NLG). An overall advantage was found for the human generated text, but NLG resulted in decisions that were at least as good as those for the graphical displays with which staff were familiar. It is suggested that NLG might offer a viable automated approach to removing noise and artefacts in real, complex and dynamic data sets, thereby reducing visual complexity and mental workload, and enhancing decision-making particularly for inexperienced staff. Copyright © 2008 John Wiley & Sons, Ltd.

/pdf/when-a-graph-is-poorer-than-100-words-a-comparison-of-5675kcblh1.pdf

When a graph is poorer than 100 words: A comparison of computerised natural language generation, human generated descriptions and graphical displays in neonatal intensive care

https://www.um.edu.mt/library/oar/bitstream/123456789/22071/1/aimed2012.pdf

Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

https://www.um.edu.mt/library/oar/bitstream/123456789/22383/1/jamia2011.pdf

Cindy Sykes

Papers

Automatic generation of textual summaries from neonatal intensive care data

When a graph is poorer than 100 words: A comparison of computerised natural language generation, human generated descriptions and graphical displays in neonatal intensive care

Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

BT-Nurse: computer generation of natural language shift summaries from complex heterogeneous medical data

American Medical Informatics Association Annual Symposium Proceedings