Context-Sensitive Word Vector Generation for NLP
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Solution Overview
Problem
Existing natural language processing systems, such as IBM Watson, face inaccuracies in processing natural language due to the peculiarities of language constructs and human reasoning, as they often rely on unsupervised data without considering contextual biases, leading to incorrect outcomes.
Innovation Solution
A system and method for generating context-sensitive word vector representations by using a processing unit coupled with a memory and intelligence platform, which includes a document manager, word manager, and director to assess context and document relevance, calculate vector distances, and train models to produce contextually sensitive word vectors, enabling the application of context to improve natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unsupervised data is used for training natural language processing systems, then the system can process a wide range of language inputs, but the accuracy of processing deteriorates due to lack of contextual understanding
Solution Approach 1:
The system performs preliminary action by pre-training word vectors on large unsupervised corpora to capture general language patterns, then subsequently fine-tunes these vectors using supervised contextual data. This two-stage approach allows the system to first acquire broad language versatility and then improve processing accuracy through contextual learning.
Solution Approach 2:
The patent applies local quality by making word vector representations context-dependent rather than uniform. Different contextual environments generate different vector representations for the same word, allowing the system to maintain general adaptability while achieving high accuracy in specific contextual situations through localized vector adjustments.
2Measurement precision
If contextual bias is incorporated into word vector generation, then processing accuracy improves, but system complexity increases due to additional processing components
Solution Approach 1:
The patent achieves universality by designing a multi-functional system where the same word vector generation framework handles both general language processing and context-specific processing. The context manager and vector generation components serve multiple purposes: they generate base vectors, apply contextual transformations, and support various NLP tasks without requiring separate specialized systems.
Solution Approach 2:
The system implements nesting by embedding multiple processing layers within each other: word vectors are nested within document vectors, which are nested within context vectors. This hierarchical nesting allows the system to manage complexity through structured organization, where each layer builds upon and refines the previous layer without requiring independent complex systems.
3Measurement precision
If context-sensitive word vectors are generated through supervised training, then document relevance assessment improves, but training time and computational resources increase
Solution Approach 1:
The system applies preliminary action by pre-computing word vectors from unsupervised data before supervised training. This pre-computation creates a solid foundation that reduces the computational burden and training time required for subsequent supervised contextual training, as the model starts with already-learned language patterns rather than learning from scratch.
Solution Approach 2:
The patent maintains continuity of useful action by using the same word vector framework throughout both unsupervised pre-training and supervised fine-tuning phases. This continuous approach avoids the time loss of transitioning between fundamentally different systems, allowing seamless progression from general language learning to context-specific refinement without interrupting the vector generation process.
Data Source
AI summary
Embodiments relate to a system, program product, and method for use with an intelligent computer platform to create and apply textual data in vector format, and more specifically to apply context to the vector representation. Both context and document vectors are generated and assessed, with a calculated distance between the vectors corresponding to a weight. Word vectors are generated with associated word pairs and frequencies. A word vector generation model is trained. Utilization of the trained model generates one or more context sensitive word vector representations. A summarized sentence document is created and returned through application of the context sensitive word vectors.