Contextual Proximity Retrieval for Biomedical QA
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Solution Overview
Problem
Biomedical question answering systems face challenges in accurately ranking relevant documents due to the limitations of traditional bag of words (BOW) approaches, which struggle to distinguish between similar sentences with different contextual meanings, leading to difficulties in information retrieval.
Innovation Solution
The proposed solution enhances information retrieval by using a word embedding model to generate vector representations of sentences, calculating a similarity matrix, and determining an alignment score to assess the similarity between sentences, thereby improving the ranking of documents based on contextual proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional bag of words (BOW) approach with cosine model is used for document ranking, then search efficiency is improved, but the ability to distinguish sentences with different contextual meanings deteriorates
Solution Approach 1:
The patent transforms the representation parameters of text from discrete bag-of-words vectors to continuous word embedding vectors that capture semantic relationships. This parameter transformation enables the system to distinguish sentences with different contextual meanings while maintaining computational efficiency through vector space operations and similarity metrics.
2Measurement precision
If word embedding model with similarity matrix is used to improve sentence similarity discrimination, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces word embedding models as an intermediary layer between the text input and the similarity comparison process. This intermediary transforms raw text into semantic vectors, enabling precise similarity measurement without requiring complex algorithmic changes in the core retrieval system, thus managing complexity through modular design.
Data Source
AI summary
A method and apparatus include receiving a first sentence including a first set of words, and a second sentence including a second set of words. A first set of vectors corresponding to the first set of words of the first sentence, and a second set of vectors corresponding to the second set of words of the second sentence are generated using a word embedding model. A similarity matrix based on the first set of vectors and the second set of vectors is generated. An alignment score associated with the first set of vectors and the second set of vectors is determined using the similarity matrix. The alignment score is transmitted to permit information retrieval based on a similarity between the first sentence and the second sentence.