Natural language processing, moving from rules to data

Adrian Horia Dediu, Joana M. Matos, Carlos Martéın-Vide

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


During the last decade, we assist to a major change in the direction that theoretical models used in natural language processing follow. We are moving from rule-based systems to corpus-oriented para-digms. In this paper, we analyze several generative formalisms together with newer statistical and data-oriented linguistic methodologies. We review existing methods belonging to deep or shallow learning applied in various subfields of computational linguistics. The continuous, fast improvements obtained by practical, applied machine learning techniques may lead us to new theoretical developments in the classic models as well. We discuss several scenarios for future approaches.

Original languageEnglish
Title of host publicationTheory and Applications of Models of Computation - 14th Annual Conference, TAMC 2017, Proceedings
EditorsGerhard Jager, Silvia Steila, T.V. Gopal
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319559100
Publication statusPublished - 2017
Event14th Annual Conference on Theory and Applications of Models of Computation, TAMC 2017 - Bern, Switzerland
Duration: 20 Apr 201722 Apr 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10185 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Annual Conference on Theory and Applications of Models of Computation, TAMC 2017


  • Computational linguistics
  • Computational models
  • Machine translation
  • Speech processing methods


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