CDUD 2016 – The 3rd International Workshop on Concept Discovery in Unstructured Data
Concept discovery is a subdomain of Knowledge Discovery (KDD) that uses
human-centered techniques such as Formal Concept Analysis (FCA), Topic Mod-
eling, Visual Text Representations, Conceptual Graphs etc. for gaining insight
into the underlying conceptual structure of the data. Traditional machine learn-
ing techniques are mainly focusing on structured data whereas most data avail-
able resides in unstructured, often textual, form. Compared to traditional data
mining techniques, human-centered instruments actively engage the domain ex-
pert in the discovery process.
This volume contains the papers presented at the 3rd International Workshop
on Concept Discovery in Unstructured Data (CDUD 2016) held on July 18,
2018 at the National Research University Higher School of Economics, Moscow,
Russia. This workshop welcomes papers describing innovative research on data
discovery in complex data. It particular, it provides a forum for researchers and
developers of text mining instruments, whose research is related to the analysis
of linguistic and text data.
This year 15 papers had been submitted. Each submission has been reviewed,
at least, by 2 program committee members. Seven papers have been accepted
for regular publication in the proceedings, and three more submissions for pub-
lication as project proposals or abstracts.
Papers included in this volume cover a wide range of topics related to text
mining and structures for text representation: text navigation, statistical learning
models, automatic author or field identification in texts, among others.
An invited talk given by Natalia Loukachevitch from Moscow State Univer-
sity has opened the workshop program. She has surveyed modern tasks and
approaches in sentiment analysis of Twitter messages.
Our deep gratitude goes to all the authors of submitted papers, as well as
to the Program Committee members for their commitment. We also would like
to thank our invited speaker and our sponsors: National Research University
Higher School of Economics (Moscow, Russia), Russian Foundation for Basic
Research, and ExactPro. Finally, we would like to acknowledge the EasyChair
system which helped us to manage the reviewing process.
A study on verb-noun collocation and government model extraction from large corpora