Weighted Embedding Data Structure for Text Representation
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Solution Overview
Problem
Current natural language processing systems face inefficiencies in representing text, particularly in capturing domain-specific knowledge and contextual information, as traditional methods like Bag of Words and TF-IDF generate sparse representations that fail to account for word sequences, and word embeddings trained on large datasets can be costly and time-consuming to train.
Innovation Solution
The method involves using a pre-trained embedding model and a feature vector model to generate a weighted embedding data structure by applying weights to domain-specific word embeddings, which are then summed to create a multi-dimensional representation of text, incorporating contextual information and domain knowledge for improved text classification and summarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods like Bag of Words and TF-IDF are used to represent text, then the implementation is simple and fast, but the representations are sparse and fail to capture word sequences and contextual information
Solution Approach 1:
The patent applies preliminary action by using pre-trained word embedding models that have already been trained on large datasets. These pre-trained embeddings capture contextual information and word sequences beforehand, allowing the system to benefit from sophisticated text representation without performing the computationally expensive training process. This resolves the contradiction by providing high-quality representations (improving accuracy) while avoiding the complexity of training large models from scratch.
Solution Approach 2:
The patent uses copying by leveraging pre-trained word embedding models that have been developed and trained separately. Instead of creating new complex models, the system copies and utilizes existing embedding models that already contain learned contextual information. This approach improves text representation accuracy while avoiding the complexity and computational cost of developing new models.
2Measurement precision
If word embeddings are trained on large datasets to capture domain-specific knowledge, then the text representation becomes more accurate, but the training process becomes costly and time-consuming
Solution Approach 1:
The patent applies preliminary action by using pre-trained word embedding models that have already been trained on large datasets. These pre-trained embeddings capture contextual information and word sequences beforehand, allowing the system to benefit from sophisticated text representation without performing the computationally expensive training process. This resolves the contradiction by providing high-quality representations (improving accuracy) while avoiding the complexity of training large models from scratch.
3Productivity
If standard word embeddings are used without domain-specific weighting, then the processing is faster and simpler, but the representations fail to capture domain-specific knowledge and contextual nuances
Solution Approach 1:
The patent applies local quality by introducing domain-specific weighting to word embeddings. Instead of treating all words uniformly, the system applies different weights to different words based on their importance and relevance to the specific domain. This allows the representation to capture domain-specific nuances and contextual information (improving accuracy) while still building upon efficient pre-trained embeddings (maintaining processing speed).
Solution Approach 2:
The patent applies parameter changes by modifying the weights of word embeddings based on domain-specific importance. By adjusting the weighting parameters of selected words in the text, the system enhances the representation to capture domain-specific knowledge. This resolves the contradiction by improving domain-specific accuracy through parameter adjustment while maintaining the efficiency of the underlying embedding model.
Data Source
AI summary
Provided are a method, system, and computer program product for representing text, in which a text is received and analyzed by utilizing a pre-trained embedding model and a feature vector model, wherein selected words in the text have corresponding weights. Operations whose parameters include weights of a feature vector and an embedding are performed to generate a weighted embedding data structure. A summation is performed of all corresponding columns of a plurality of rows of the weighted embedding data structure to generate a data structure that represents the text. The data structure that represents the text is utilized to generate at least one of a classification metadata for the text and a summarization of the text.