Seeded Neural Topic Modeling Framework
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Solution Overview
Problem
Traditional topic modeling techniques, such as LDA and CTM, often yield poor results when applied to real-world data and require significant tuning, failing to produce domain-relevant topics suitable for production use due to lack of effective seeding mechanisms.
Innovation Solution
A seeded neural topic modeling framework is introduced, which initializes a CTM architecture with seed topic words, using a novel loss function that combines reward and penalty factors to guide the model towards generating cleaner, domain-relevant topics by penalizing divergence and rewarding similarity to the seed words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional topic modeling techniques (LDA, CTM) are used on real-world data, then unsupervised analysis is achieved without labeled data, but the results are poor and require significant specialist tuning
Solution Approach 1:
The patent applies preliminary action by pre-procassing documents before they enter the topic modeling process. Specifically, documents are pre-processed to extract salient terms and construct topic seed distributions, which are then used to initialize the topic modeling algorithm. This preliminary preparation ensures that the model starts with domain-relevant topics already established, eliminating the need for significant post-hoc tuning and specialist intervention.
Solution Approach 2:
The patent implements feedback mechanisms through the iterative training process where the model continuously refines topic assignments and word distributions based on the pre-processed document features. The algorithm uses feedback from the pre-computed topic seed distributions to adjust and optimize topic modeling parameters throughout training, ensuring convergence toward high-quality, domain-relevant topics without requiring manual intervention.
2Adaptability or versatility
If traditional topic modeling techniques are used, then no labeled data is required, but significant specialist tuning and post-processing are needed
Solution Approach 1:
The patent applies self-service by enabling the system to automatically construct topic seed distributions from pre-processed document features without requiring manual specification of topics. The pre-processing stage automatically identifies salient terms and computes topic seed distributions that serve as initialization parameters, allowing the system to self-configure for different domains without expert intervention or complex tuning procedures.
3Ease of manufacture
If existing open-source solutions are used, then topic modeling can be performed, but few allow effective seeding and majority yield results unfit for production
Solution Approach 1:
The patent applies preliminary action by pre-computing topic seed distributions from pre-processed document features before the main topic modeling process. This pre-computation of salient term-based seed distributions provides robust initialization that enables effective seeding without requiring complex user input or manual configuration, making the system ready for production use.
Data Source
AI summary
A method may include: receiving a seed topic word distribution; receiving a corpus of documents; generating bag of words representations for the corpus of documents; converting the corpus of documents to vector representations; training a topic modeling system using the seed topic word distribution and concatenated bag of words representations and the vector representations resulting in a topic word distribution and a document word distribution; generating a plurality of new generated topics based on the topic word distribution; precomputing a topic word distribution penalty and a topic word distribution reward for the plurality of topics; penalizing the topic modeling system in response to a divergence and rewarding the topic modeling system in response to a similarity; determining a total loss from a neural network loss, the topic word distribution penalty, and the topic word distribution reward; and training the topic modeling system based on the total loss.