Topically Aware Word Suggestions Using Contextual Clusters
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Solution Overview
Problem
Existing word suggestion technologies lack contextual awareness, failing to provide contextually relevant suggestions as they cannot interpret broader meanings or consider user input context.
Innovation Solution
A system that determines conditional and unconditional counts of word pairs, generates word clusters, and adjusts probabilities using a language model dictionary and freshness factor to offer topically aware word suggestions by identifying common clusters and reconstructing adjustment factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sequence-based word suggestion technology is used, then word suggestions can be generated based on common word sequences, but contextual awareness is lost and suggestions fail to interpret broader meanings
Solution Approach 1:
The patent segments the text input into sentences and further into words, processing each segment to determine topic labels. This segmentation allows the system to analyze contextual relationships between words and sentences, enabling contextual awareness while maintaining the ability to generate word suggestions based on segmented analysis.
Solution Approach 2:
The patent introduces topic labels as intermediary elements that bridge the gap between raw text sequences and contextual understanding. These topic labels act as mediators that capture the broader meaning and context of word sequences, allowing the system to generate contextually aware suggestions without losing the sequence-based approach.
2Adaptability or versatility
If user personalization data is used to generate suggestions, then user-specific patterns can be captured, but broader contextual meaning cannot be interpreted
Solution Approach 1:
The patent merges multiple data sources including user personalization data, sentence-level topic labels, and word sequence analysis into a unified suggestion generation process. This combining approach allows the system to leverage user-specific patterns while interpreting broader contextual meanings, achieving contextual relevance without proportionally increasing complexity.
Solution Approach 2:
The patent performs preliminary processing by pre-determining topic labels for sentences and pre-processing user data to identify patterns. This preliminary action reduces the computational complexity during the actual suggestion generation by having the heavy lifting done in advance, allowing the system to handle contextual analysis efficiently.
3Measurement precision
If basic word sequence analysis is used, then simple word suggestions can be generated quickly, but suggestions lack contextual accuracy and relevance
Solution Approach 1:
The patent applies partial action by processing only the necessary portions of the text - specifically determining topic labels for sentences and analyzing word sequences within those labeled contexts. This selective processing achieves sufficient contextual accuracy without the excessive computational cost of analyzing every possible contextual variation, maintaining reasonable generation speed.
Solution Approach 2:
The patent changes the parameters of analysis by introducing topic label determinations as a new dimension to word sequence analysis. Instead of merely analyzing word sequences in isolation, the system modifies the analysis parameters to include contextual topic information, thereby improving suggestion accuracy while managing computational requirements through efficient parameter integration.
Data Source
AI summary
Concepts and technologies are described herein for providing topically aware word suggestions. Using a text input, the system determines a conditional count and an unconditional count. The system then determines an adjustment factor for a pair of words of the plurality of words based on the unconditional count and the conditional count. The system then generates a data structure defining a plurality of word clusters. The system then reconstructs the adjustment factor of the pair of words based on a number of common clusters between individual words of the pair of words. The adjustment factor is combined with other data, such as data from a language model dictionary and a freshness factor from an average cluster activation state table to determine a probability associated with a word candidate, which is displayed to a user.