PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automated word puzzle generation via topic dictionaries
Balazs Pinter, Gyula Voros, Zoltan Szabo and András Lorincz
In: International Conference on Machine Learning (ICML-2012) - Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing Workshop, 30 June 2012, Edinburgh, Scotland.


We propose a general method for automated word puzzle generation. Contrary to previous approaches in this novel field, the presented method does not rely on highly structured datasets obtained with serious human annotation effort: it only needs an unstructured and unannotated corpus (i.e., document collection) as input. The method builds upon two additional pillars: (i) a topic model, which induces a topic dictionary from the input corpus (examples include e.g., latent semantic analysis, group-structured dictionaries or latent Dirichlet allocation), and (ii) a semantic similarity measure of word pairs. Our method can (i) generate automatically a large number of proper word puzzles of different types, including the odd one out, choose the related word and separate the topics puzzle. (ii) It can easily create domain-specific puzzles by replacing the corpus component. (iii) It is also capable of automatically generating puzzles with parameterizable levels of difficulty suitable for, e.g., beginners or intermediate learners.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:Website of the workshop:
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Subjects:Natural Language Processing
Theory & Algorithms
ID Code:9550
Deposited By:Zoltan Szabo
Deposited On:02 July 2012