Summary of the paper

Title Predictive Modeling: Guessing the NLP Terms of Tomorrow
Authors Gil Francopoulo, Joseph Mariani and Patrick Paroubek
Abstract Predictive modeling, often called “predictive analytics” in a commercial context, encompasses a variety of statistical techniques that analyze historical and present facts to make predictions about unknown events. Often the unknown events are in the future, but prediction can be applied to any type of unknown whether it be in the past or future. In our case, we present some experiments applying predictive modeling to the usage of technical terms within the NLP domain.
Topics Topic Detection & Tracking, Text Mining, Information Extraction, Information Retrieval
Full paper Predictive Modeling: Guessing the NLP Terms of Tomorrow
Bibtex @InProceedings{FRANCOPOULO16.89,
  author = {Gil Francopoulo and Joseph Mariani and Patrick Paroubek},
  title = {Predictive Modeling: Guessing the NLP Terms of Tomorrow},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
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