Contextual Lookup Using Statistical Language Models
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Solution Overview
Problem
Existing techniques for part-of-speech tagging and information retrieval fail to accurately determine the context-specific meaning of words or phrases, leading to irrelevant search results when used in non-annotated texts.
Innovation Solution
Implementing a process that uses contextual information, such as part-of-speech tagging and statistical language models, to dynamically determine the likelihood of a word's part of speech, named entity status, or sense, and incorporating this information to perform targeted searches and filter results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If keyword-based search is used for information retrieval, then search speed is improved, but search result relevance deteriorates
Solution Approach 1:
The system performs part-of-speech tagging and contextual analysis on the query text before executing the search. Statistical language models pre-process the input to determine word senses, named entities, and grammatical roles, so that when the search is executed, it already uses refined keywords and filters, achieving both speed and relevance
Solution Approach 2:
A statistical language model acts as an intermediary between the user's natural language query and the search engine. This intermediary layer analyzes context, disambiguates word meanings, and transforms the query into a form that maintains original intent while improving search precision without significantly increasing processing time
2Measurement precision
If contextual analysis using statistical language models is implemented, then search result relevance is improved, but system complexity increases
Solution Approach 1:
The statistical language model serves multiple functions simultaneously: part-of-speech tagging, named entity recognition, word sense disambiguation, and contextual keyword extraction. By using a single multi-functional model rather than separate specialized systems, the patent reduces overall system complexity while maintaining high search relevance
Solution Approach 2:
The system dynamically adjusts analysis parameters based on the input text characteristics. The statistical language model adapts its processing depth and focus areas according to the query type, allowing the system to maintain high relevance while avoiding unnecessary computational overhead for simple queries
3Loss of information
If part-of-speech tagging is performed on non-annotated text, then contextual understanding is improved, but processing time increases
Solution Approach 1:
The system performs part-of-speech tagging selectively on only the necessary portions of the query text that require disambiguation, rather than analyzing every word. The statistical language model identifies which words need contextual analysis and applies tagging only to those, reducing processing time while maintaining sufficient contextual understanding
Solution Approach 2:
The statistical language model performs rapid preliminary analysis to identify potential ambiguity cases before applying full part-of-speech tagging. This two-stage approach allows the system to skip detailed tagging for unambiguous words while applying comprehensive analysis only where needed, balancing processing time with contextual understanding
Data Source
AI summary
Using a statistical language model to make information related to a word or other content of interest available is disclosed. An indication that a word or phrase is of interest is received. A contextual meta-information associated with the word or phrase of interest as used in context is used to make information related to the word or phrase of interest available, for example, by forming a more precise query and/or filtering search results based on the contextual meta-information.