TWEE Framework Topic Sparse Autoencoder for Question Classification
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Solution Overview
Problem
Conventional approaches for question classification struggle to effectively extract discriminative representations from short texts due to their limited number of words, failing to adequately incorporate both local and global contextual information.
Innovation Solution
A unified neural network framework, TWEE, integrates topic modeling, word embedding, and entity embedding, utilizing a Topic Sparse AutoEncoder (TSAE) to generate global topical representations and learn comprehensive question representations by combining local contexts and global topical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches are used for question classification, then the system is simple to implement, but the representation learning from short texts is ineffective
Solution Approach 1:
The patent merges topic modeling, word embedding, and entity embedding into a unified neural network framework (TWEE). This integration allows the system to simultaneously leverage global topical structures and local contextual information from short questions, resolving the contradiction by combining multiple representation learning techniques into a cohesive architecture that improves effectiveness while managing complexity through unified design
Solution Approach 2:
The patent implements nested representation structures where topic embeddings, word embeddings, and entity embeddings are hierarchically organized. The TSAE generates topic distributions that serve as global context, while word and entity embeddings provide local details. These nested layers are combined through concatenation and transformation operations, allowing the system to capture representations at multiple granularity levels simultaneously
2Measurement precision
If questions are processed as short texts with limited words, then the input processing is fast, but discriminative representations cannot be effectively extracted
Solution Approach 1:
The patent transforms the limited word-based input into enriched representation spaces by introducing topic dimensions through TSAE. Instead of relying solely on the limited word tokens, the system projects questions into topic distribution spaces and combines them with word and entity embedding spaces, effectively adding dimensional depth to compensate for the scarcity of words in short questions
Solution Approach 2:
The patent introduces topic embeddings as intermediary representations that bridge the gap between limited word inputs and discriminative classification outputs. The TSAE generates topic distributions that mediate between raw question text and final classification decisions, allowing the system to extract discriminative information indirectly through topical abstraction when direct word-level signals are insufficient
3Reliability
If both local and global contextual information are incorporated, then the question understanding is more comprehensive, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training the TSAE model to generate topic distributions before the main classification task. This pre-computed global topical context is then integrated with local word and entity embeddings during inference, allowing the system to have comprehensive understanding ready in advance rather than computing everything from scratch during classification
Solution Approach 2:
The patent implements dynamic information fusion where the contribution of global topic embeddings versus local word/entity embeddings is adaptively balanced. The neural network dynamically weights and combines these different information sources based on the specific question characteristics, allowing comprehensive understanding while managing computational resources efficiently through adaptive processing
Data Source
AI summary
Described herein are embodiments of a unified neural network framework to integrate Topic modeling, Word embedding and Entity Embedding (TWEE) for representation learning of inputs. In one or more embodiments, a novel topic sparse autoencoder is introduced to incorporate discriminative topics into the representation learning of the input. Topic distributions of inputs are generated from a global viewpoint and are utilized to enable autoencoder to learn topical representations. A sparsity constraint may be added to ensure that the most discriminative representations are related to topics. In addition, both words and entity related information may be embedded into the network to help learn a more comprehensive input representation. Extensive empirical experiments show that embodiments of the TWEE framework outperform the state-of-the-art methods on different datasets.