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A Random Indexing Approach to Unsupervised Selectional Preference Induction
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics. (Datorlingvistik)
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics. (Datorlingvistik)ORCID iD: 0000-0002-4269-5619
2011 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

A selectional preference is the relation between a head-word and plausible arguments of that head-word. Estimation of the association feature between these words is important to natural language processing applications such as Word Sense Disambiguation. This study presents a novel approach to selectional preference induction within a Random Indexing word space. This is a spatial representation of meaning where distributional patterns enable estimation of the similarity between words. Using only frequency statistics about words to estimate how strongly one word selects another, the aim of this study is to develop a flexible method that is not language dependent and does not require any annotated resourceswhich is in contrast to methods from previous research. In order to optimize the performance of the selectional preference model, experiments including parameter tuning and variation of corpus size were conducted. The selectional preference model was evaluated in a pseudo-word evaluation which lets the selectional preference model decide which of two arguments have a stronger correlation to a given verb. Results show that varying parameters and corpus size does not affect the performance of the selectional preference model in a notable way. The conclusion of the study is that the language modelused does not provide the adequate tools to model selectional preferences. This might be due to a noisy representation of head-words and their arguments.

Place, publisher, year, edition, pages
2011. , 28 p.
Keyword [en]
Selectional preference induction, selectional preferences, word space model, Random Indexing, syntagma, distributional hypothesis
National Category
Language Technology (Computational Linguistics) Language Technology (Computational Linguistics)
URN: urn:nbn:se:su:diva-59493OAI: diva2:428507
2011-06-01, C307, Institutionen för lingvistik, Universitetsvägen 10 C, Stockholm, 10:00 (Swedish)
Humanities, Theology
Available from: 2011-07-04 Created: 2011-06-30 Last updated: 2014-06-02Bibliographically approved

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