Enriched Word Vectors for NLP Emotion Interpretation
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Solution Overview
Problem
Natural language processing (NLP) systems fail to effectively interpret human reactions such as emotions and personality traits from user input due to the lack of incorporation of affective aspects in word distributions, leading to inadequate responses in interactive computing environments.
Innovation Solution
The introduction of a human-reaction lexicon that modifies initial word vectors to incorporate human-reaction similarity, resulting in enriched word distributions that better represent affective aspects, enabling improved interpretation of emotions and personality traits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional word distributions are used for NLP, then semantic similarity can be determined, but human reactions such as emotions and personality traits cannot be interpreted
Solution Approach 1:
The patent combines conventional semantic word vectors with affective word vectors to create enriched word representations. Each word is represented by both its semantic meaning (from conventional NLP models) and its affective properties (from the human-reaction lexicon), allowing the system to simultaneously preserve semantic similarity while adding emotional interpretation capability.
Solution Approach 2:
The enriched word distribution acts as a composite representation, merging two distinct types of information (semantic and affective) into a unified word vector structure. This composite approach allows the system to leverage both semantic understanding and emotional interpretation in NLP tasks.
2Loss of information
If word distributions incorporate only semantic relations, then computational efficiency is maintained, but affective aspects of human communication are lost
Solution Approach 1:
The patent adds an affective dimension to the traditional semantic word space. By introducing a new dimensional layer that captures human reactions (emotions, personality traits, opinions), the system retains all original semantic information while adding affective information without fundamentally restructuring the underlying vector space architecture.
3Measurement precision
If human-reaction lexicon is integrated into word vectors, then emotion interpretation improves, but computational resources increase
Solution Approach 1:
The affective word distributions are pre-computed and stored in a human-reaction lexicon before runtime processing. This preliminary preparation allows the system to quickly retrieve and apply affective information during NLP operations without performing complex computations in real-time, thus improving emotion interpretation while managing computational resources efficiently.
Data Source
AI summary
Certain embodiments involve facilitating natural language processing through enriched distributional word representations. For instance, a computing system receives an initial word distribution having initial word vectors that represent, within a multidimensional vector space, words in a vocabulary. The computing system also receives a human-reaction lexicon indicating human-reaction values respectively associated with words in the vocabulary. The computing system creates an enriched word distribution by modifying one or more of the initial word vectors such that the distance between the pair of initial word vectors representing a pair of words is decreased based on a human-reaction similarity between the pair of words.