Semantic Indexing Model Query Expansion and Time Features
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Solution Overview
Problem
Supervised Semantic Indexing (SSI) models face ranking errors due to a lack of training data and changes in query distributions over time, leading to overfitting and obsolescence.
Innovation Solution
The method involves expanding query terms by merging them with top-ranked documents and incorporating a time-based measure to adjust similarity scores, using a system with a search engine, query generator, and controller to train the semantic indexing model, ensuring that newer documents are biased over older ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised semantic indexing models are trained using a limited set of queries and documents, then the model can be trained with available data, but the model overfits the data and produces ranking errors
Solution Approach 1:
The system performs preliminary actions by expanding queries with related terms from documents before training the model. This preprocessing step enriches the training data representation, allowing the model to learn from more diverse query formulations and reduce overfitting to the original limited query set.
Solution Approach 2:
The system changes parameters by introducing time-based features that capture the temporal distribution of queries and documents. By incorporating time stamps and adjusting for query distribution shifts over time, the model adapts to changing data patterns and maintains generalization capability while improving ranking accuracy on current data.
2Reliability
If the semantic indexing model is trained on historical data, then the model can be trained with existing data, but the model becomes obsolete when query distributions change over time
Solution Approach 1:
The system introduces dynamic elements by incorporating time-based features that allow the model to adapt to changing query distributions. The time stamps associated with queries and documents enable the model to understand temporal patterns and adjust its predictions accordingly, extending the model's validity period despite distribution shifts.
Solution Approach 2:
The system implements feedback mechanisms by using time-based features to continuously adapt to new query patterns. The temporal information provides feedback about distribution changes, allowing the model to learn from evolving data patterns and maintain performance over extended periods.
3Adaptability or versatility
If more training data is collected to improve model performance, then the model can generalize better, but the cost and complexity of data collection and processing increases
Solution Approach 1:
The system uses an intermediary approach by expanding queries with related terms from existing documents rather than collecting entirely new training data. This intermediary step leverages existing document content to enrich query representations, achieving better generalization without the full complexity of large-scale data collection and processing.
Data Source
AI summary
Semantic indexing methods and systems are disclosed. One such method is directed to training a semantic indexing model by employing an expanded query. The query can be expanded by merging the query with documents that are relevant to the query for purposes of compensating for a lack of training data. In accordance with another exemplary aspect, time difference features can be incorporated into a semantic indexing model to account for changes in query distributions over time.