Word Embedding Transforms for Probabilistic Topic Modeling
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Solution Overview
Problem
Document processing methods using 'bag of words' representation face challenges due to high dimensionality and the inability to effectively capture semantic relationships between words, making it difficult to adjust for semantic changes and expand vocabulary without altering the vector structure.
Innovation Solution
The approach involves transforming text words into K-dimensional word vectors using word embedding transforms, allowing for lower dimensionality and capturing semantic relationships, enabling the modeling of documents in a probabilistic topic model that can accommodate new words and semantic changes without altering the vector space dimensionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bag of words representation is used for document processing, then document retrieval and classification can be performed, but the high dimensionality makes the system complex and difficult to manage
Solution Approach 1:
The patent applies dimensionality change by transforming documents from high-dimensional bag-of-words representations to low-dimensional latent topic spaces. Instead of representing documents using all possible words (high dimensionality), the system projects them onto a smaller number of latent topics (e.g., 50-200 dimensions), making the data more manageable while preserving essential information for retrieval and classification tasks.
2Productivity
If bag of words representation is used, then document processing is possible, but semantic relationships between words cannot be captured
Solution Approach 1:
The patent introduces latent topics as intermediary concepts that mediate between words and document meaning. Instead of directly representing words in high-dimensional space, the system uses latent topics as intermediate representations that capture semantic relationships. Words are associated with multiple latent topics, allowing the system to infer semantic connections and relationships that are not explicitly present in the text.
3Productivity
If bag of words representation is used, then documents can be processed, but the vocabulary cannot be expanded without altering the vector structure
Solution Approach 1:
The patent applies dynamics by making the topic model adaptable to new vocabulary without requiring structural changes. The latent topic model can dynamically incorporate new words and concepts by updating topic-word associations during training, while maintaining the same low-dimensional topic space structure. This allows the system to adapt to evolving terminology and domains without redesigning the representation framework.
Data Source
AI summary
A set of word embedding transforms are applied to transform text words of a set of documents into K-dimensional word vectors in order to generate sets or sequences of word vectors representing the documents of the set of documents. A probabilistic topic model is learned using the sets or sequences of word vectors representing the documents of the set of documents. The set of word embedding transforms are applied to transform text words of an input document into K-dimensional word vectors in order to generate a set or sequence of word vectors representing the input document. The learned probabilistic topic model is applied to assign probabilities for topics of the probabilistic topic model to the set or sequence of word vectors representing the input document. A document processing operation such as annotation, classification, or similar document retrieval may be performed using the assigned topic probabilities.