Contextual Search Indexing via Stop Word Token Coalescence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Search engines face performance bottlenecks in handling phrase queries and contextual searches due to frequent stop words, which increase disk usage, processor load, and memory consumption, and existing methods like skip lists and next-word indexing either slow down indexing or increase index complexity.
Innovation Solution
The method involves preprocessing documents to mark stop words and coalesce them with prefixes, creating specialized internal tokens that are stored in the index, allowing for efficient contextual matching by modifying queries to use these tokens, thereby reducing the data size and improving search speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stop words are included in phrase queries, then contextual search accuracy is improved, but search performance deteriorates due to frequent word processing overhead
Solution Approach 1:
The patent applies preliminary action by pre-processing the corpus to identify and mark stop words before indexing. During query processing, these pre-marked stop words are quickly recognized and handled through optimized pathways, eliminating the need for real-time analysis of frequent words and thus maintaining both accuracy and performance
2Speed
If specialized indexes like skip lists are used for phrase queries, then query speed is improved, but index complexity and size increase
Solution Approach 1:
The patent extracts stop words from the general indexing process and handles them through a separate, optimized mechanism. By isolating the processing of frequent words into a distinct pathway with pre-computed positions and specialized data structures, the main index remains simple while query speed is improved through the dedicated stop word handling system
3Productivity
If next-word indexing is applied to stop words, then phrase query performance is improved, but the number of unique index entries increases significantly
Solution Approach 1:
The patent merges the stop word index with the main document index into a unified structure. Instead of creating separate index entries for each stop word occurrence, the system integrates stop word positions directly into the document posting lists, allowing phrase queries to be processed efficiently without multiplying the total number of index entries
4Measurement precision
If comprehensive index structures are maintained for contextual matching, then search accuracy is improved, but system memory usage increases
Solution Approach 1:
The patent applies local quality by maintaining detailed contextual information only where needed - specifically at stop word positions and phrase boundaries - while using simplified representations for the rest of the corpus. This selective detail approach ensures accurate contextual matching occurs precisely at critical points without requiring comprehensive detailed indexing throughout the entire system
Data Source
AI summary
Words having selected characteristics in a corpus of documents are found using a data processor arranged to execute queries. Memory stores an index structure in which entries in the index structure map words and marks for words having the selected characteristics to locations within documents in the corpus. Entries in the index structure represent words and other entries represent marks with the location information of a marked word. The entries for the marks can be tokens coalesced with prefixes of respective marked words or adjacent. A query processor forms a modified query by adding a mark for a word to the query. The processor executes the modified query.