=Paper=
{{Paper
|id=None
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-757/preface.pdf
|volume=Vol-757
}}
==None==
Dmitry Ignatov, Sergei Kuznetsov, Jonas Poelmans (Eds.)
CDUD’11 – Concept Discovery in Unstructured Data
Workshop co-located with the 13th International Conference on Rough Sets,
Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC-2011)
June 2011, Moscow, Russia
The proceedings are published online in the CEUR-Workshop series (ISSN 1613-
0073) and the volume Vol-757 has a unique URN: urn:nbn:de:0074-757-4.
i
Volume Editors
Dmitry Ignatov
School of Applied Mathematics and Informatics
National Research University Higher School of Economics, Moscow, Russia
Sergei Kuznetsov
School of Applied Mathematics and Informatics
National Research University Higher School of Economics, Moscow, Russia
Jonas Poelmans
Faculty of Business and Economics
Katholieke Universiteit Leuven, Belgium
Copyright c 2011 for the individual papers by papers’ authors, for the Volume
by the editors. All rights reserved. No part of this publication may be reproduced,
stored in a retrieval system, or transmitted, in any form or by any means without
the prior permission of the copyright owners.
ii
Preface
Concept discovery is a Knowledge Discovery in Databases (KDD) research field
that uses human-centered techniques such as Formal Concept Analysis (FCA),
Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the
underlying conceptual structure of the data. Traditional machine learning tech-
niques are mainly focusing on structured data whereas most data available re-
sides in unstructured, often textual, form. Compared to traditional data mining
techniques, human-centered instruments actively engage the domain expert in
the discovery process.
This volume contains the contributions to CDUD 2011, the International
Workshop on Concept Discovery in Unstructured Data (CDUD) held in Moscow.
The main goal of this workshop was to provide a forum for researchers and devel-
opers of data mining instruments working on issues with analyzing unstructured
data.
We are proud that we could welcome 13 valuable contributions to this vol-
ume. The majority of the accepted papers described innovative research on data
discovery in unstructured texts. Authors worked on issues such as transforming
unstructured into structured information by amongst others extracting keywords
and opinion words from texts with Natural Language Processing methods. Multi-
ple authors who participated in the workshop used methods from the conceptual
structures field including Formal Concept Analysis and Conceptual Graphs. Ap-
plications include but are not limited to text mining police reports, sociological
definitions, movie reviews, etc.
Last but not least, we would like to thank the administration of the Higher
School of Economics who took care of all arrangements to make this conference
pleasant and enjoyable.
June 2011, Moscow Dmitry Ignatov
Sergei Kuznetsov
Jonas Poelmans
iii
Organization
This CDUD’11 workshop was held in June 2011 in Moscow, Russia co-located
with the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining,
and Granular Computing (RSFDGrC-2011) at the National Research University
Higher School of Economics.
Program Chairs
Dmitry Ignatov State University Higher School of Economics, Russia
Sergei Kuznetsov State University Higher School of Economics, Russia
Jonas Poelmans Katholieke Universiteit Leuven, Belgium
Program Committee
Guido Dedene Katholieke Universiteit Leuven, Belgium
Amsterdam Business School, The Netherlands
Paul Elzinga Amsterdam-Amstelland Police, The Netherlands
Bernhard Ganter Dresden University of Technology, Germany
Richard Hill University of Derby, UK
Alex Neznanov State University Higher School of Economics, Russia
Simon Polovina University of Sheffield, UK
Henrik Scharfe Aalborg University, Denmark
Vladimir Selegey ABBYY, Russia
Stijn Viaene Katholieke Universiteit Leuven, Belgium
Laszlo Szathmary University of Quebec in Montreal, Canada
Sponsoring Institutions
ABBYY, Moscow
Russian Foundation for Basic Research, Moscow
Poncelet Laboratory (UMI 2615 du CNRS), Moscow
State University Higher School of Economics, Moscow
Yandex, Moscow
Witology, Moscow
Dynasty Foundation, Moscow
Table of Contents
Automatic Entity Detection Based on News Cluster Structure . . . . . . . . . 1
Aleksey Alekseev and Natalia Loukachevitch
Application of Conceptual Structures in Requirements Modeling . . . . . . . 11
Michael Bogatyrev and Vadim Nuriahmetov
Abstracting Concepts from Text Documents by Using an Ontology . . . . . . 21
Ekaterina Cherniak, Olga Chugunova, Julia Askarova, Susana Nasci-
mento and Boris Mirkin
Extraction and Use of Opinion Words for Three-Way Review
Classification Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Ilia Chetviorkin and Natalia Loukachevitch
Constructing Galois Lattice in Good Classification Tests Mining . . . . . . . . 43
Xenia Naidenova
Concept Relation Discovery and Innovation Enabling Technology
(CORDIET) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Jonas Poelmans, Paul Elzinga, Alexey Neznanov, Stijn Viaene, Sergei
Kuznetsov, Dmitry Ignatov and Guido Dedene
Concept Lattice Implementation in Semantic Structuring of Adjectives . . 63
Serge Potemkin
Exploring Semantic Orientation of Adverbs . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Serge Potemkin and Galina Kedrova
The Third Personal Pronoun Anaphora Resolution in Texts from
Narrow Subject Domains with Grammatical Errors and Mistypings . . . . . . 79
Daniel Skatov and Sergey Liverko
An FCA-Based Approach to the Study of Socialization Definitions . . . . . . 93
Sergei Vinkov
Temporal Concept Analysis Explained by Examples . . . . . . . . . . . . . . . . . . . 104
Karl Erich Wolff
Research Challenges of Dynamic Socio-Semantic Networks . . . . . . . . . . . . . 119
Rostislav Yavorsky
Recommender System Based on Algorithm of Bicluster Analysis RecBi . . 122
Dmitry Ignatov, Jonas Poelmans and Vasily Zaharchuk
v