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                                      Preface


Recently, a new field has emerged taking benefit of both domains: Data Mining (DM)
and Natural Language Processing (NLP). Indeed, statistical and machine learning
methods hold a predominant position in NLP research1 , advanced methods such as
recurrent neural networks, Bayesian networks and kernel based methods are exten-
sively 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, sym-
bolic 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 2014 workshop:
DMNLP’14, held on September 15, 2014 in Nancy. DMNLP’14 (Workshop on Inter-
actions between Data Mining and Natural Language Processing) is the first work-
shop 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 23 submitted papers, 9 were accepted as regular papers amounting to an
acceptance rate of 39%. In addition to regular contributions, two less mature works,
which were still considered valuable for discussion, were accepted as posters and appear
as extended abstract in this volume.
    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 2014, and more specifi-
cally Amedeo Napoli and Chedy Raı̈ssy, and the ECML/PKDD 2014 workshop chairs
Bettina Berendt and Patrick Gallinari.



September 2014                                        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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