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Approximate Inference. FINNISH KEYWORDS (2)
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Anna Korhonen (2)
Roi Reichar T (2)
The Pennsylvania State University CiteSeerX Archives (2)
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Hits 1 – 2 of 2
1
Document and corpus level inference for unsupervised and transductive learning of information structure of scientic documents
Roi Reichar T
;
Anna Korhonen
In: http://aclweb.org/anthology/C/C12/C12-2097.pdf (2012)
Abstract:
Inferring the information structure of scientific documents has proved useful for supporting information access across scientific disciplines. Current approaches are largely supervised and expensive to port to new disciplines. We investigate primarily unsupervised discovery of information structure. We introduce a novel graphical model that can consider different types of prior knowledge about the task: within-document discourse patterns, cross-document sentence similarity information based on linguistic features, and prior knowledge about the correct classification of some of the input sentences when this information is available. We apply the model to Argumentative Zoning (AZ) scheme and evaluate it on a fully unsupervised learning scenario and two transduction scenarios where the categories of some test sentences are known. The model substantially outperforms similarity and topic model based clustering approaches as well as traditional transduction algorithms. TITLE AND ABSTRACT IN FINNISH Dokumentti- ja korpustason inferenssiin perustuva ohjaamattomankoneoppimisen tekniikka tieteellisen
Keyword:
A
;
Approximate Inference. FINNISH KEYWORDS
;
Argumentative Zoning
;
Information structure
;
Rakenteen analyysi
URL:
http://aclweb.org/anthology/C/C12/C12-2097.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.423.1715
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2
Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents
Roi Reichar T
;
Anna Korhonen
In: http://www.cl.cam.ac.uk/%7Err439/papers/283_Paper.pdf
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