128,701 research outputs found
Unsupervised Extraction of Representative Concepts from Scientific Literature
This paper studies the automated categorization and extraction of scientific
concepts from titles of scientific articles, in order to gain a deeper
understanding of their key contributions and facilitate the construction of a
generic academic knowledgebase. Towards this goal, we propose an unsupervised,
domain-independent, and scalable two-phase algorithm to type and extract key
concept mentions into aspects of interest (e.g., Techniques, Applications,
etc.). In the first phase of our algorithm we propose PhraseType, a
probabilistic generative model which exploits textual features and limited POS
tags to broadly segment text snippets into aspect-typed phrases. We extend this
model to simultaneously learn aspect-specific features and identify academic
domains in multi-domain corpora, since the two tasks mutually enhance each
other. In the second phase, we propose an approach based on adaptor grammars to
extract fine grained concept mentions from the aspect-typed phrases without the
need for any external resources or human effort, in a purely data-driven
manner. We apply our technique to study literature from diverse scientific
domains and show significant gains over state-of-the-art concept extraction
techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201
Temporal fuzzy association rule mining with 2-tuple linguistic representation
This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules
Data-Mining a Large Digital Sky Survey: From the Challenges to the Scientific Results
The analysis and an efficient scientific exploration of the Digital Palomar
Observatory Sky Survey (DPOSS) represents a major technical challenge. The
input data set consists of 3 Terabytes of pixel information, and contains a few
billion sources. We describe some of the specific scientific problems posed by
the data, including searches for distant quasars and clusters of galaxies, and
the data-mining techniques we are exploring in addressing them.
Machine-assisted discovery methods may become essential for the analysis of
such multi-Terabyte data sets. New and future approaches involve unsupervised
classification and clustering analysis in the Giga-object data space, including
various Bayesian techniques. In addition to the searches for known types of
objects in this data base, these techniques may also offer the possibility of
discovering previously unknown, rare types of astronomical objects.Comment: Invited paper, to appear in Applications of Digital Image Processing
XX, ed. A. Tescher, Proc. S.P.I.E. vol. 3164, in press; 10 pages, a
self-contained TeX file, and 3 separate postscript figure
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