Target Language Engine Text Scoring via Synonym Augmentation
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Solution Overview
Problem
Conventional systems fail to effectively identify and display the most relevant content on mobile devices, as they do not adequately account for synonyms and compound words in text scoring algorithms, leading to suboptimal user experience.
Innovation Solution
The Target Language Engine augments a base dictionary with historical search queries and identifies synonyms and compound words, creating associative links to enhance text scoring and generate relevant snippets for mobile device display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text scoring algorithms are used, then the system is simple and fast, but it fails to accurately identify relevant content because it does not account for synonyms and compound words
Solution Approach 1:
The system performs preliminary actions by pre-processing text to identify compound words and generate synonyms before scoring. The text is analyzed to split compound words into constituent words, and synonyms are retrieved from a dictionary, so that when scoring occurs, the system already has the enhanced word list ready, improving accuracy without adding significant complexity to the main scoring process
Solution Approach 2:
The text scoring process is segmented into distinct stages: text preprocessing, compound word identification, synonym generation, and scoring. This segmentation allows each component to be optimized independently and makes the overall system more manageable, addressing the complexity issue while maintaining improved measurement precision
2Ease of operation
If the system processes all text content, then completeness is maintained, but mobile device display optimization is compromised due to excessive content volume
Solution Approach 1:
The system extracts and processes only the most relevant portions of text for mobile display. By using the enhanced scoring algorithm that accounts for synonyms and compound words, the system identifies and extracts key content elements that should be prominently displayed on mobile devices, separating essential information from less relevant content
Solution Approach 2:
Different quality levels are assigned to different content elements based on their relevance score. High-relevance content (those with high scores from synonym and compound word analysis) is optimized for mobile display with prominent positioning and enhanced formatting, while lower-relevance content is handled differently, creating local quality differentiation that optimizes mobile user experience
Data Source
AI summary
In various example embodiments, a system and method for a Target Language Engine are presented. The Target Language Engine augments a synonym list in a base dictionary of a target language with one or more historical search queries previously submitted to search one or more listings in listing data. The Target Language Engine identifies a compound word and a plurality of words present in the listing data that have a common meaning in the target language. Each word from the plurality of words is present in the compound word. The Target Language Engine causes a database to create an associative link between the portion of text and a word selected from at least one of the synonym list or the plurality of words.