Spatiotemporal Distributed Representation for Semantic Judgment
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Solution Overview
Problem
Conventional document search technologies fail to accurately acquire semantic relationships between words due to the lack of consideration for spatiotemporal information, leading to incorrect identification of synonyms and other semantic relationships.
Innovation Solution
An information processing device and method that incorporates spatiotemporal information by analyzing object names into words, acquiring vicinal object information, calculating word distributions, and converting them into spatiotemporal information-considered distributed representations to accurately judge semantic relationships between words or sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed representations are acquired based on distribution hypothesis using vicinal words, then word meaning acquisition becomes possible, but words representing different objects are incorrectly judged to have the same meaning
Solution Approach 1:
The patent introduces spatiotemporal dimensions to the traditional distributional hypothesis by incorporating entity coordinates and temporal information. Instead of relying solely on linguistic co-occurrence, the system now considers where and when entities appear together in documents, adding spatial and temporal constraints to the semantic relationship judgment process.
Solution Approach 2:
The patent changes the parameters used for semantic judgment from purely linguistic distributional features to include spatiotemporal parameters. By incorporating entity coordinates, time stamps, and frequency of co-occurrence in specific spatiotemporal contexts, the system transforms the feature space for semantic relationship detection.
2Productivity
If conventional document search technology is used, then processing speed is maintained, but semantic relationship identification accuracy deteriorates
Solution Approach 1:
The patent performs preliminary extraction and organization of entity spatiotemporal information from documents before conducting semantic relationship analysis. By pre-processing documents to identify entities, their coordinates, and temporal contexts, the system prepares structured data that can be quickly queried during semantic judgment without requiring full document re-processing.
Solution Approach 2:
The patent creates distributed representation vectors as compressed copies of entity semantic information based on their spatiotemporal distribution patterns. These vector representations serve as efficient proxies that capture semantic relationships without requiring access to the full original document corpus during query processing.
Data Source
AI summary
An information processing device includes processing circuitry to acquire object spatiotemporal information including spatiotemporal information indicating coordinates of objects in time and space and a name of each of the objects and to generate morphological analysis-undergone object spatiotemporal information by executing a morphological analysis as a process of analyzing the name of each of the objects included in the object spatiotemporal information into one or more words; to acquire morphological analysis-undergone names of vicinal objects, as objects existing in a vicinity of each of the objects in time and space, from the morphological analysis-undergone object spatiotemporal information; to calculate a distribution of vicinal object name words, as words included in the names of the vicinal objects of each of the objects, from the morphological analysis-undergone names; and to convert the distribution of the vicinal object name words to a spatiotemporal information-considered distributed representation regarding words.