Abstract
Measuring semantic similarity of words is of crucial importance in Natural Language Processing. Although there are many different approaches for this task, there is still room for improvement. In contrast to many other methods that use web search engines or large lexical databases, we developed such methods that solely rely on large static corpora. They create a binary or numerical feature vector for each word making use of statistical information obtained from the corpora. These vectors contain features based on context words or grammatical relations extracted from the corpora and they employ diverse weighting schemes. After creating the feature vectors, word similarity is calculated using various vector similarity measures. Beside the individual methods, their combinations were also tested. Evaluated on both the Miller-Charles dataset and the TOEFL synonym questions, they achieve competitive results to recent methods.
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Dobó, A., Csirik, J. (2013). Computing Semantic Similarity Using Large Static Corpora. In: van Emde Boas, P., Groen, F.C.A., Italiano, G.F., Nawrocki, J., Sack, H. (eds) SOFSEM 2013: Theory and Practice of Computer Science. SOFSEM 2013. Lecture Notes in Computer Science, vol 7741. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35843-2_42
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DOI: https://doi.org/10.1007/978-3-642-35843-2_42
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