Word Similarity Extraction via User-Contextualized Distributed Representations
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Solution Overview
Problem
Existing conference coordination systems require users to specify multiple reservation conditions manually, leading to user burden and inefficiency, and struggle to accurately extract words with similar concepts due to noise in similarity calculations based on simple distributed representation comparisons.
Innovation Solution
An information processing device that calculates distributed representations of character strings combining user names and words, then uses similarity calculations and normalization to extract words with similar concepts by analyzing the frequency and rank of these representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple distributed representation comparison is used for word similarity calculation, then the calculation process is simple, but noise in similarity calculations increases and detection accuracy decreases
Solution Approach 1:
The patent segments the word similarity calculation process into multiple stages: initial distributed representation calculation, similarity score computation, rank determination, and frequency-based noise filtering. This segmentation allows each stage to address specific aspects of the problem, improving overall accuracy while maintaining manageable complexity
Solution Approach 2:
The patent introduces intermediate variables (similarity scores, ranks, and frequency counts) as mediators between the raw distributed representations and the final similarity determination. These intermediaries enable systematic noise filtering and multi-criteria evaluation, resolving the contradiction between simple calculation and accurate detection
2Measurement precision
If multiple reservation conditions are specified manually in existing conference coordination systems, then conference room search accuracy is maintained, but user burden increases and efficiency decreases
Solution Approach 1:
The system performs self-service by automatically calculating word similarities and extracting relevant keywords without requiring users to manually specify multiple reservation conditions. The automated similarity calculation and keyword extraction mechanisms enable the system to independently determine appropriate conference rooms based on minimal user input
Solution Approach 2:
The patent implements preliminary action by pre-calculating distributed representations and similarity metrics for potential conference room keywords before user queries. This preprocessing enables rapid response to user inputs while maintaining search accuracy, eliminating the need for users to manually specify multiple conditions
Data Source
AI summary
An extraction method executed by a computer includes: acquiring a first word input by a first user; acquiring a second word input by a second user; calculating a first distributed representation of a first character string, the first character string including the first word and a character string that identifies the first user; calculating a second distributed representation of a second character string, the second character string including the second word and a character string that identifies the second user; calculating a similarity between the first distributed representation and the second distributed representation; and extracting a word associated with the second distributed representation on a basis of the similarity.