Conference Paper

Spelling Correction for Search Engine Queries.

DOI: 10.1007/978-3-540-30228-5_33 Conference: Advances in Natural Language Processing, 4th International Conference, EsTAL 2004, Alicante, Spain, October 20-22, 2004, Proceedings
Source: DBLP

ABSTRACT Search engines have become the primary means of accessing informa- tion on the Web. However, recent studies show misspelled words are very com- mon in queries to these systems. When users misspell query, the results are incor- rect or provide inconclusive information. In this work, we discuss the integration of a spelling correction component into tumba!, our community Web search en- gine. We present an algorithm that attempts to select the best choice among all possible corrections for a misspelled term, and discuss its implementation based on a ternary search tree data structure.

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    ABSTRACT: Typically, web search users submit short and ambiguous queries to search engines. As a result, users spent much time in formulating query in order to retrieve relevant information in the top ranked results. In this paper, term association graph is employed in order to provide query suggestion by assessing the linkage structure of the text graph constructed over a collection of documents. In addition to that, a biologically inspired model based on Ant Colony Optimisation (ACO) has been explored and applied over term association graph as learning process that addresses the problem of deriving optimal query suggestions. The user interactions with the search engine is treated as an individual ant’s navigation and the collective navigations of all ants over the time result in strengthening more significant paths in a term association graph which in turn used to provide query modification suggestions. We present an algorithm that attempts to select the best related keyword among all possible suggestions for an input search query and discuss its implementation based on a ternary search tree and graph data structure. We experimentally study the performance of the proposed method in comparing with different techniques.
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    ABSTRACT: Text description of engineering diagnoses recorded during and after vehicle repair process plays an important role in root cause analyzing and vehicle maintenance. The fact that such text is unstructured, lack of grammar, has a lot of spelling errors and a large amount of self-invented domain specific terminologies introduces challenges and difficulties for automatic information retrieving and categorization. This paper presents our research in text mining in vehicle diagnostic applications. Specifically, an automatic typo correction system is proposed and implemented. We build multiple knowledge bases to detect and correct typos, and a neural network classifier to select good candidates for correcting typos. Experiment results show that our system outperforms state-of-art spell checking systems.
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