Abstract-We present a language-independent spell-checker that is based on an enhancement of the n-gram model. The spell checker is proposing correction suggestions by selecting the most promising candidates from a ranked list of correction candidates that is derived based on n-gram statistics and lexical resources. Besides motivating and describing the developed techniques, we briefly discuss the use of the proposed approach in an application for keyword-and semantic-based search support. In addition, the proposed tool was compared with state-of-the-art spelling correction approaches. The evaluation showed that it outperforms the other methods.

In this paper we present a language-independent approach for conflation that does not depend on predefined rules or prior knowledge of the target language. The proposed unsupervised method is based on an enhancement of the pure n-gram model that can group related words based on various string-similarity measures, while restricting the search to specific locations of the target word by taking into account the order of n-grams. We show that the method is effective to achieve high score similarities for all word-form variations and reduces the ambiguity, i.e., obtains a higher precision and recall, compared to pure n-gram-based approaches for English, Portuguese, and Arabic. The proposed method is especially suited for conflation approaches in Arabic, since Arabic is a highly inflectional language. Therefore, we present in addition an adaptive user interface for Arabic text retrieval called "araSearch". araSearch serves as a metasearch interface to existing search engines. The system is able to extend a query using the proposed conflation approach such that additional results for relevant subwords can be found automatically.

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