24,946 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
Automatic Structural Scene Digitalization
In this paper, we present an automatic system for the analysis and labeling
of structural scenes, floor plan drawings in Computer-aided Design (CAD)
format. The proposed system applies a fusion strategy to detect and recognize
various components of CAD floor plans, such as walls, doors, windows and other
ambiguous assets. Technically, a general rule-based filter parsing method is
fist adopted to extract effective information from the original floor plan.
Then, an image-processing based recovery method is employed to correct
information extracted in the first step. Our proposed method is fully automatic
and real-time. Such analysis system provides high accuracy and is also
evaluated on a public website that, on average, archives more than ten
thousands effective uses per day and reaches a relatively high satisfaction
rate.Comment: paper submitted to PloS On
Efficient Analysis of Complex Diagrams using Constraint-Based Parsing
This paper describes substantial advances in the analysis (parsing) of
diagrams using constraint grammars. The addition of set types to the grammar
and spatial indexing of the data make it possible to efficiently parse real
diagrams of substantial complexity. The system is probably the first to
demonstrate efficient diagram parsing using grammars that easily be retargeted
to other domains. The work assumes that the diagrams are available as a flat
collection of graphics primitives: lines, polygons, circles, Bezier curves and
text. This is appropriate for future electronic documents or for vectorized
diagrams converted from scanned images. The classes of diagrams that we have
analyzed include x,y data graphs and genetic diagrams drawn from the biological
literature, as well as finite state automata diagrams (states and arcs). As an
example, parsing a four-part data graph composed of 133 primitives required 35
sec using Macintosh Common Lisp on a Macintosh Quadra 700.Comment: 9 pages, Postscript, no fonts, compressed, uuencoded. Composed in
MSWord 5.1a for the Mac. To appear in ICDAR '95. Other versions at
ftp://ftp.ccs.neu.edu/pub/people/futrell
XML document design via GN-DTD
Designing a well-structured XML document is important for the sake of readability and maintainability. More importantly, this will avoid data redundancies and update anomalies when maintaining a large quantity of XML based documents. In this paper, we propose a method to improve XML structural design by adopting graphical notations for Document Type Definitions (GN-DTD), which is used to describe the structure of an XML document at the schema level. Multiples levels of normal forms for GN-DTD are proposed on the basis of conceptual model approaches and theories of normalization. The normalization rules are applied to transform a poorly designed XML document into a well-designed based on normalized GN-DTD, which is illustrated through examples
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