Adaptive Enterprise Search Spellcheck Module
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Solution Overview
Problem
Enterprise search engines lack adaptive and customizable query spell correction capabilities, as off-the-shelf spellcheckers are inadequate for domain-specific environments and require query logs, which are often unavailable.
Innovation Solution
A computer-implemented spellcheck module that generates a candidate set of corrections for misspelled words by analyzing text using a corpus-based approach, ranking suggestions with context-sensitive scoring, and implementing a self-adaptable misspell detection strategy to improve search results and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If off-the-shelf spellcheckers are used, then general grammatical correctness is achieved, but domain-specific adaptability is lost
Solution Approach 1:
The system changes the parameters of spellchecking by using domain-specific corpora to build customized spellcheckers. Instead of using fixed general-purpose spellchecking parameters, the system adapts parameters such as vocabulary, spelling patterns, and language models to match the specific domain (e.g., medical, legal, technical), thereby achieving both domain-specific adaptability and reliable spelling correction accuracy.
Solution Approach 2:
The system performs preliminary action by pre-processing and analyzing domain-specific corpora to build customized spellchecking models before actual spellchecking occurs. This includes collecting domain-specific text data, extracting vocabulary and spelling patterns, and training domain-adapted language models in advance, so that when spellchecking is needed, the system already has tailored correction capabilities ready.
2Adaptability or versatility
If comprehensive spellchecking is implemented without query logs, then language independence is achieved, but implementation complexity increases
Solution Approach 1:
The system implements self-service by automatically adapting to different languages without requiring manual configuration or query logs. The spellchecker analyzes the input text's language characteristics, automatically selects or trains appropriate language models, and adjusts its spellchecking strategy accordingly. This enables language independence while keeping implementation complexity manageable through automated language detection and adaptation.
Solution Approach 2:
The system achieves universality by designing a spellchecking framework that can handle multiple languages and domains through a unified architecture. The same core spellchecking engine can be adapted to different languages and domains by loading appropriate corpora and models, eliminating the need for separate implementations for each language or domain and reducing overall implementation complexity.
3Productivity
If fast spell correction is implemented, then user experience is improved, but correction accuracy may be reduced
Solution Approach 1:
The system applies partial action by providing spell correction suggestions in a prioritized order rather than evaluating all possible corrections equally. The most likely corrections are presented first based on probability calculations from the domain-specific corpus, allowing the system to achieve high accuracy for the most probable corrections quickly, while less probable corrections can be evaluated if needed.
Solution Approach 2:
The system replaces traditional mechanical spellchecking approaches with probabilistic and statistical methods based on domain-specific corpora. Instead of using rigid rule-based systems that are fast but less accurate, the system uses language models and probability calculations that can quickly estimate correction likelihoods, achieving both speed and accuracy through computational statistics rather than mechanical rule application.
Data Source
AI summary
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.