K-Nugget Retrofitted Embeddings for Ambiguous NLP Contexts
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Solution Overview
Problem
Existing NLP methods fail to adequately preserve linguistic characteristics of words, leading to suboptimal performance due to ambiguous word representations and the lack of comprehensive contextual understanding in word embeddings.
Innovation Solution
A system and method utilizing a Self-Organizing Tree Algorithm (SOTA) framework for K-nugget discovery and retrofitting, which includes lexical, syntactic, semantic, transactional, and language-agnostic stages to enhance word embeddings by discovering user meta data and determining similarity and dissimilarity scores, leveraging dynamic retrofitted embeddings to improve context awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If word embeddings are formed using distributional features (frequency of co-occurrence), then the embeddings capture contextual information, but the embeddings fail to preserve linguistic characteristics and semantic awareness
Solution Approach 1:
The patent combines distributional features (frequency of co-occurrence) with linguistic features (synonyms, antonyms, hypernymy, hyponymy) to create enhanced word embeddings. This merging allows the embeddings to simultaneously capture contextual information from distributional data and preserve linguistic characteristics from structured lexical relationships, resolving the contradiction between information retention and semantic precision
Solution Approach 2:
The patent creates composite word embeddings by integrating multiple feature types: distributional features from corpus data, linguistic features from lexical databases, and structural features from word relationships. This composite approach produces embeddings that maintain both contextual richness and linguistic awareness, overcoming the limitations of single-feature embeddings
2Device complexity
If a single vector representation is used for all meanings of a word, then the embedding is simple, but the ML model performance deteriorates due to ambiguity
Solution Approach 1:
The patent applies local quality by creating different embedding representations for different meanings of ambiguous words based on their contextual usage. Instead of a single uniform representation, the system generates context-specific embeddings that capture the local semantic characteristics of each meaning, thereby improving ML model performance while maintaining reasonable complexity through selective representation
Solution Approach 2:
The patent segments the meaning space of ambiguous words by identifying distinct senses and creating separate vector representations for each sense. This segmentation allows the system to handle polysemy by representing different meanings independently, improving model reliability while managing complexity through structured organization of multiple representations
3Loss of information
If existing NLP methods (Word2Vec, BERT, ATTRACT-REPEL) are used to train or fine-tune embeddings, then the embeddings capture contextual information, but the linguistic constraints and characteristics are not adequately preserved
Solution Approach 1:
The patent introduces linguistic databases and structured lexical resources as intermediary components between the training data and the embedding learning process. These intermediaries provide explicit linguistic constraints (synonyms, antonyms, hypernymy relationships) that guide the embedding training, ensuring that contextual understanding from corpus data is combined with precise linguistic structure from external knowledge sources
Data Source
AI summary
The present disclosure provides a system (110) for retrofitting words represented using the vectors for Natural Language Processing (NLP) models and a streamlined process which is an ideal pipeline for any NLP tasks. The system (110) may discover the user meta data or k-nuggets in five stages for retrofitting and stacking the retrofitted embeddings. Further, the system (110) may use the retrofitted embeddings for NLP Tasks. The five stages of the k-nugget discovery pipeline are Lexical, Syntactic, Semantic, transactional, and language agnostic stages for retrofitting the word embeddings. The proposed embedding layer is replaced with the retrofitted embedding which may be obtained after the fifth stage and improved performance can be achieved. To validate the approach, the K-nugget discovery pipeline has been tested on the SemEval (Hinglish and English Tweet dataset) and HOT dataset (Hinglish Tweet dataset) and to achieve state of the art results on the test dataset.