@inproceedings{48108a1c29294be2bdc2d09d13d59a09,
title = "Forecasting emerging trends from scientific literature",
abstract = "Text analysis methods for the automatic identification of emerging technologies by analyzing the scientific publications, are gaining attention because of their socio-economic impact. The approaches so far have been mainly focused on retrospective analysis by mapping scientific topic evolution over time. We propose regression based approaches to predict future keyword distribution. The prediction is based on historical data of the keywords, which in our case, are LREC conference proceedings. Considering the insufficient number of data points available from LREC proceedings, we do not employ standard time series forecasting methods. We form a dataset by extracting the keywords from previous year proceedings and quantify their yearly relevance using tf-idf scores. This dataset additionally contains ranked lists of related keywords and experts for each keyword.",
keywords = "Emerging trends, Extraction, Regression, Trend analysis",
author = "Kartik Asooja and Georgeta Bordea and Gabriela Vulcu and Paul Buitelaar",
year = "2016",
language = "English",
series = "Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016",
publisher = "European Language Resources Association (ELRA)",
pages = "417--420",
editor = "Nicoletta Calzolari and Khalid Choukri and Helene Mazo and Asuncion Moreno and Thierry Declerck and Sara Goggi and Marko Grobelnik and Jan Odijk and Stelios Piperidis and Bente Maegaard and Joseph Mariani",
booktitle = "Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016",
note = "10th International Conference on Language Resources and Evaluation, LREC 2016 ; Conference date: 23-05-2016 Through 28-05-2016",
}