Query Processing Lexicon for Proper Noun Disambiguation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Natural Language Processing (NLP) tools are inadequate for processing queries due to their inability to handle the unique linguistic properties of short text strings, such as misspelling and freeform structure, which leads to ineffective query chunking and part-of-speech tagging.
Innovation Solution
A system and method that utilize a lexicon to provide recapitalization, part-of-speech tagging, and named entity recognition, adapting these processes to the specific context of queries through preprocessing, disambiguation, and chunking, allowing for improved query processing without requiring access to a document collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard NLP tools are used for query processing, then general language understanding is achieved, but query-specific accuracy deteriorates due to inability to handle misspelling and freeform structure
Solution Approach 1:
The patent applies different processing rules to different parts of the query based on their linguistic properties. Specifically, it implements case-insensitive matching for proper nouns that may be misspelled or lowercase in queries, while maintaining standard processing for other elements. This localized adaptation allows the system to handle query-specific issues without compromising overall processing accuracy.
Solution Approach 2:
The system dynamically changes processing parameters based on the detected linguistic properties of query elements. When a proper noun is identified in lowercase or with potential misspelling, the system adjusts its matching parameters to be more flexible and case-insensitive. This parameter adaptation resolves the contradiction by making the system both reliable and adaptable to query variations.
2Measurement precision
If full sentence analysis methods are applied to queries, then grammatical accuracy is improved, but performance deteriorates due to freeform structure and misspelling
Solution Approach 1:
The patent performs preliminary processing of the query before applying full sentence analysis methods. It first identifies and flags proper nouns that may be misspelled or in lowercase, then uses this preliminary information to guide the subsequent POS tagging process. This preliminary action prevents the full analysis method from failing due to freeform structure while maintaining grammatical accuracy.
Solution Approach 2:
The system segments the query into different elements and applies appropriate processing to each. Proper nouns are identified and processed separately with case-insensitive rules, while other elements undergo standard POS tagging. This segmentation allows the system to maintain high precision for named entities while preserving overall processing efficiency despite freeform structure.
3Manufacturing precision
If independent processing of capitalization, named entity detection, and query segmentation is performed, then each component can be optimized, but overall query understanding deteriorates due to lack of integration
Solution Approach 1:
The patent merges multiple independent processing components into an integrated query processing system. The named entity detection, capitalization handling, and query segmentation are combined and executed in a coordinated manner, sharing information across components. This integration prevents loss of context information while maintaining the processing accuracy of each individual component.
Solution Approach 2:
The system implements feedback loops where the output of one processing component informs the operation of subsequent components. For example, named entity detection results feed into the capitalization handling process, which in turn informs query segmentation. This feedback mechanism ensures that context information is preserved and utilized across all processing stages, resolving the contradiction between component optimization and overall understanding.
Data Source
AI summary
A system and method for natural language processing of queries are provided. A lexicon includes text elements that are recognized as being a proper noun when capitalized. A natural language query includes a sequence of text elements including words. The query is processed. The processing includes a preprocessing step, in which part of speech features are assigned to the text elements in the query. This includes identifying, from a lexicon, a text element in the query which starts with a lowercase letter and assigning recapitalization information to the text element in the query, based on the lexicon. This information includes a part of speech feature of the capitalized form of the text element. Then parts of speech for the text elements in the query are disambiguated, which includes applying rules for recapitalizing text elements based on the recapitalization information.