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<pdf width="1500px">https://ceur-ws.org/Vol-1880/preface.pdf</pdf>
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                                      Preface


Recently, a field has emerged taking benefit of both domains: Data Mining (DM) and
Natural Language Processing (NLP). Indeed, statistical and machine learning meth-
ods hold a predominant position in NLP research1 , advanced methods such as recur-
rent neural networks, Bayesian networks and kernel based methods are extensively
researched, and “may have been too successful (. . . ) as there is no longer much room
for anything else”2 . They have proved their e↵ectiveness for some tasks but one major
drawback is that they do not provide human readable models. By contrast, symbolic
machine learning methods are known to provide more human-readable model that
could be an end in itself (e.g., for stylistics) or improve, by combination, further meth-
ods including numerical ones. Research in Data Mining has progressed significantly in
the last decades, through the development of advanced algorithms and techniques to
extract knowledge from data in di↵erent forms. In particular, for two decades Pattern
Mining has been one of the most active field in Knowledge Discovery.
    This volume contains the papers presented at the ECML/PKDD 2017 workshop:
DMNLP’17, held on September 22, 2017 in Skopje. DMNLP’17 (Workshop on Interac-
tions between Data Mining and Natural Language Processing) is the fourth edition
of a workshop dedicated to Data Mining and Natural Language Processing cross-
fertilization, i.e a workshop where NLP brings new challenges to DM, and where DM
gives future prospects to NLP. It is well-known that texts provide a very challenging
context to both NLP and DM with a huge volume of low-structured, complex, domain-
dependent and task-dependent data. The objective of DMNLP is thus to provide a
forum to discuss how Data Mining can be interesting for NLP tasks, providing symbolic
knowledge, but also how NLP can enhance data mining approaches by providing richer
and/or more complex information to mine and by integrating linguistic knowledge
directly in the mining process. Out of 10 submitted papers, 6 were accepted.
    The high quality of the program of the workshop was ensured by the much-
appreciate work of the authors and the Program Committee members. Finally, we wish
to thank the local organization team of ECML/PKDD 2017. and the ECML/PKDD
2017 workshop chairs Nathalie Japkowicz and Panc̆e Panov.



September 2017                                        Peggy Cellier, Thierry Charnois
                                                         Andreas Hotho, Stan Matwin
                                             Marie-Francine Moens, Yannick Toussaint




1
  D. Hall, D. Jurafsky, and C. M. Manning. Studying the History of Ideas Using Topic
  Models. In Proceedings of the 2008 Conference on Empirical Methods in Natural
  Language Processing, pp. 363–371, 2008
2
  K. Church. A Pendulum Swung Too Far. Linguistic Issues in Language Technology,
  Vol. 6, CSLI publications, 2011.

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