Abstract: Embodiments are directed to a spellcheck module for an enterprise search engine. The spellcheck module includes a candidate suggestion generation module that generates a number of candidate words that may be the correction of the misspelled word. The candidate suggestion generation module implements an algorithm for indexing, searching, and storing terms from an index with a constrained edit distance, using words in a collection of documents. The spellcheck module further includes a candidate suggestion ranking module. In one embodiment, a non-contextual approach using a linear combination of distance and probability scores is utilized; while in another embodiment, a context sensitive approach accounting for real-word misspells and adopting deep learning models is utilized. In use, a query is provided to the spellcheck module to generate results in the form of a ranked list of generated candidate entries that may be an entry a user accidentally misspelled.

Abstract: Embodiments are directed to a spellcheck module for an enterprise search engine. The spellcheck module includes a candidate suggestion generation module that generates a number of candidate words that may be the correction of the misspelled word. The candidate suggestion generation module implements an algorithm for indexing, searching, and storing terms from an index with a constrained edit distance, using words in a collection of documents. The spellcheck module further includes a candidate suggestion ranking module. In one embodiment, a non-contextual approach using a linear combination of distance and probability scores is utilized; while in another embodiment, a context sensitive approach accounting for real-word misspells and adopting deep learning models is utilized. In use, a query is provided to the spellcheck module to generate results in the form of a ranked list of generated candidate entries that may be an entry a user accidentally misspelled.

Abstract: Embodiments are directed to a spellcheck module for an enterprise search engine. The spellcheck module includes a candidate suggestion generation module that generates a number of candidate words that may be the correction of the misspelled word. The candidate suggestion generation module implements an algorithm for indexing, searching, and storing terms from an index with a constrained edit distance, using words in a collection of documents. The spellcheck module further includes a candidate suggestion ranking module. In one embodiment, a non-contextual approach using a linear combination of distance and probability scores is utilized; while in another embodiment, a context sensitive approach accounting for real-word misspells and adopting deep learning models is utilized. In use, a query is provided to the spellcheck module to generate results in the form of a ranked list of generated candidate entries that may be an entry a user accidentally misspelled.

Abstract: Embodiments are directed to a spellcheck module for an enterprise search engine. The spellcheck module includes a candidate suggestion generation module that generates a number of candidate words that may be the correction of the misspelled word. The candidate suggestion generation module implements an algorithm for indexing, searching, and storing terms from an index with a constrained edit distance, using words in a collection of documents. The spellcheck module further includes a candidate suggestion ranking module. In one embodiment, a non-contextual approach using a linear combination of distance and probability scores is utilized; while in another embodiment, a context sensitive approach accounting for real-word misspells and adopting deep learning models is utilized. In use, a query is provided to the spellcheck module to generate results in the form of a ranked list of generated candidate entries that may be an entry a user accidentally misspelled.