Contextual Topic Model Integrating LSTM for Word Order
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Probabilistic topic models ignore word order and language structure, leading to ineffective estimation of word probabilities in context, particularly in short-text documents with limited context and few documents, where traditional models fail to capture semantic and syntactical features.
Innovation Solution
The ctx-DocNADE model incorporates deep contextualized Long Short-Term Memory (LSTM-LM) to account for language structures and uses pre-trained word embeddings as distributed compositional priors, combining neural autoregressive topic modeling with language modeling to generate contextualized topic representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional probabilistic topic models (LDA, RSM, DocNADE) are used, then computational simplicity and ease of implementation are maintained, but word order and language structure information is lost, leading to poor semantic representation
Solution Approach 1:
The patent merges traditional topic models (LDA, RSM, DocNADE) with neural language models (LSTM-LM) into a unified framework. The neural language model component captures word order and language structure through sequential processing, while the topic model component maintains computational efficiency and interpretability. This combination resolves the contradiction by integrating structural information without completely abandoning the simplicity of traditional models.
Solution Approach 2:
The patent introduces an intermediary neural network layer that processes word sequences and captures language structure before feeding information to the topic model. This intermediary component (LSTM-LM) acts as a bridge that transforms raw text into structured representations, allowing the topic model to benefit from language structure information without directly handling the complexity of sequential processing itself.
2Measurement precision
If neural language models (LSTM-LM) are used to capture language structure, then word probability estimation improves, but computational complexity and training difficulty increase significantly
Solution Approach 1:
The patent segments the language modeling task into multiple layers within the LSTM architecture, where different layers capture different levels of language structure (syntax, semantics, pragmatics). This segmentation allows the model to progressively build understanding without overwhelming computational complexity at any single layer, making training more manageable while maintaining high precision in word probability estimation.
3Productivity
If traditional topic models ignore functional words and focus on content words, then computational efficiency is maintained, but semantic accuracy deteriorates in contexts where functional words carry important meaning
Solution Approach 1:
The patent applies local quality by allowing different types of words (content words and functional words) to be processed with different levels of attention and detail. The neural language model component can selectively focus on functional words when they appear in structurally important positions, while maintaining efficiency for less critical words. This resolves the contradiction by applying computational resources locally where they are most needed for semantic accuracy.
Data Source
AI summary
The invention refers to a natural language processing system configured for receiving an input sequence ci of input words (v1, v2, . . . vN) representing a first sequence of words in a natural language of a first text and generating an output sequence of output words (, , . . . ) representing a second sequence of words in a natural language of a second text and modeled by a multinominal topic model, wherein the multinominal topic model is extended by an incorporation of language structures using a deep contextualized Long-Short-Term Memory model.