Bi-directional RNN Semantic Embeddings for Global Context
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Solution Overview
Problem
Existing word embeddings primarily capture local context, leading to unsatisfactory accuracy in natural language processing tasks, as they fail to represent broader or global semantic context effectively.
Innovation Solution
Training data-driven learning networks using entire word sequences such as sentences or documents to generate prediction-based semantic embeddings that account for global semantic context, allowing for improved performance in tasks like predictive typing and statistical language modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If word embeddings are generated using local context (in-gram window), then the computational complexity is reduced, but the semantic context representation becomes narrow and limited
Solution Approach 1:
The patent segments the context representation task into two distinct components: local context embeddings (capturing immediate surrounding words) and global context embeddings (capturing broader document-level semantics). By dividing the context modeling into these separate segments that are subsequently combined, the system achieves both computational efficiency and comprehensive semantic representation without requiring complex global matrix factorization alone.
2Quantity of substance
If word embeddings capture only local context, then the training data requirements are reduced, but the accuracy in natural language processing tasks degrades
Solution Approach 1:
The patent merges local context embeddings and global context embeddings into a unified word representation. The local embeddings capture immediate contextual relationships while the global embeddings (derived from document-term matrices) capture broader semantic patterns. By combining these two types of embeddings through addition or concatenation, the system achieves improved NLP task accuracy without proportionally increasing training data requirements, as each embedding type complements the other's strengths.
3Loss of information
If global matrix factorization is used to capture global context, then the semantic context coverage is improved, but the ability to capture word analogies in local context is lost
Solution Approach 1:
The patent applies local quality by assigning different representational characteristics to different parts of the embedding system. Local context embeddings maintain the property of capturing fine-grained word relationships and analogies through their focus on immediate surrounding words. Global context embeddings provide broad semantic coverage through document-level analysis. By allowing each component to maintain its specialized quality and then combining them, the system achieves both comprehensive semantic coverage and precise word analogy capture.
Data Source
AI summary
Systems and processes for operating a digital assistant are provided. In accordance with one or more examples, a method includes, receiving training data for a data-driven learning network. The training data include a plurality of word sequences. The method further includes obtaining representations of an initial set of semantic categories associated with the words included in the training data; and training the data-driven learning network based on the plurality of word sequences included in the training data and based on the representations of the initial set of semantic categories. The training is performed using the word sequences in their entirety. The method further includes obtaining, based on the trained data-driven learning network, representations of a set of semantic embeddings of the words included in the training data; and providing the representations of the set of semantic embeddings to at least one of a plurality of different natural language processing tasks.