Journal articles
Distributional measures as proxies for semantic relatedness.
, Submitted for publication.
Saif Mohammad and Graeme Hirst.
[doi]
[BibTeX]
Book chapters
Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research.
In:
P. Perner, editor,
Advances in Data Mining. Applications and Theoretical Aspects, pages 273-287.
Springer Berlin Heidelberg, 2012.
Jonas Poelmans, DmitryI. Ignatov, Stijn Viaene, Guido Dedene and SergeiO. Kuznetsov.
[doi]
[abstract]
[BibTeX]
Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
Master's thesis
Machine Learnability Analysis of Textclassifications in a Social Bookmarking Folksonomy.
Master's thesis (Bachelor Thesis), University of Kassel, Kassel, 2008.
Jens Illig.
[BibTeX]
Journal articles
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis.
Journal on Artificial Intelligence Research, 24:305-339, 2005.
Philipp Cimiano, Andreas Hotho and Steffen Staab.
[doi]
[BibTeX]
Conference articles
Wordnet improves text document clustering.
In:
Proc. SIGIR Semantic Web Workshop.
Toronto, 2003.
A Hotho, S. Staab and G. Stumme.
[doi]
[BibTeX]
Explaining Text Clustering Results using Semantic Structures.
In: N. Lavrač, D. Gamberger and H. B. Todorovski, editors,
Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, volume 2838, series LNAI, pages 217-228.
Springer, Heidelberg, 2003.
Andreas Hotho, Steffen Staab and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Common text clustering techniques offer rather poor capabilities
for explaining to their users why a particular result has been
achieved. They have the disadvantage that they do not relate
semantically nearby terms and that they cannot explain how
resulting clusters are related to each other.
In this paper, we discuss a way of integrating a large thesaurus
and the computation of lattices of resulting clusters into common text clustering
in order to overcome these two problems.
As its major result, our approach achieves an explanation using an
appropriate level of granularity at the concept level as well as
an appropriate size and complexity of the explaining lattice of
resulting clusters.
Ontologies improve text document clustering.
In:
Proceedings of the 2003 IEEE International Conference on Data Mining, pages 541-544 (Poster.
IEEE Computer Society, Melbourne, Florida, 2003.
Andreas Hotho, Steffen Staab and Gerd Stumme.
[doi]
[BibTeX]
Technical reports
Text Clustering Based on Background Knowledge.
Technical Report , University of Karlsruhe, Institute AIFB, 2003.
Andreas Hotho, Steffen Staab and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Text document clustering plays an important role in providing intuitive
navigation and browsing mechanisms by organizing large amounts of information
into a small number of meaningful clusters. Standard partitional or agglomerative
clustering methods efficiently compute results to this end.
However, the bag of words representation used for these clustering methods is often
unsatisfactory as it ignores relationships between important terms that do not
co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning
has been achieved, because it is only specified extensionally. In order to
deal with the two problems, we integrate background knowledge into the process of
clustering text documents.
First, we preprocess the texts, enriching their representations by background knowledge
provided in a core ontology — in our application Wordnet. Then, we cluster
the documents by a partitional algorithm. Our experimental evaluation on Reuters
newsfeeds compares clustering results with pre-categorizations of news. In the experiments,
improvements of results by background knowledge compared to the baseline
can be shown for many interesting tasks.
Second, the clustering partitions the large number of documents to a relatively small
number of clusters, which may then be analyzed by conceptual clustering. In our approach,
we applied Formal Concept Analysis. Conceptual clustering techniques are
known to be too slow for directly clustering several hundreds of documents, but they
give an intensional account of cluster results. They allow for a concise description
of commonalities and distinctions of different clusters. With background knowledge
they even find abstractions like “food” (vs. specializations like “beef” or “corn”).
Thus, in our approach, partitional clustering reduces first the size of the problem
such that it becomes tractable for conceptual clustering, which then facilitates the
understanding of the results.
Conference articles
Conceptual Clustering of Text Clusters.
In: G. Kókai and J. Zeidler, editors,
Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002), pages 37-45.
2002.
A. Hotho and G. Stumme.
[doi]
[BibTeX]