Semantically Infused Embeddings for Small Domain-Specific Corpora
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Solution Overview
Problem
Existing word embeddings for natural language processing are not interpretable and perform poorly on domain-specific small corpora, lacking sparsity and semantic coherence, and traditional trend analysis techniques are biased towards term frequency-based schemes, leading to poor performance in non-uniform datasets.
Innovation Solution
The generation of Semantically Infused Embeddings (SEMIE) using a semantic infusion technique to associate meta-data within text corpora, infusing anchors, and optimizing dimensionality to enhance interpretability and semantic coherence in SNN embedding space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional word embedding techniques are used for large generic datasets, then rich and efficient embeddings are achieved, but interpretability is lost due to dense and continuous vector mappings
Solution Approach 1:
The patent transforms the parameter structure of word embeddings by converting from dense continuous vectors to sparse non-negative vectors. This fundamental parameter change enables interpretability while maintaining embedding quality through the SNN framework and semantic infusion technique that preserves semantic relationships in the transformed space.
Solution Approach 2:
The patent applies local quality by making each dimension of the sparse embedding vector semantically meaningful and interpretable. Through semantic infusion, specific dimensions are enhanced to represent particular semantic concepts, allowing local interpretation of individual dimensions while maintaining global embedding effectiveness.
2Quantity of substance
If pre-trained embeddings like GloVe and word2vec are used, then embeddings from millions of documents are obtained, but applicability to small domain-specific corpora is limited
Solution Approach 1:
The patent implements dynamics by making the embedding generation process adaptive to different corpus sizes and domains. The semantic infusion technique dynamically adjusts to the specific domain characteristics of small corpora, enabling the same framework to effectively process both large generic datasets and small domain-specific texts.
Solution Approach 2:
The patent enhances domain-specific applicability by infusing semantic information locally tailored to the specific domain. The semantic infusion process identifies and emphasizes domain-relevant semantic relationships, making the embeddings highly adaptable to domain-specific contexts even when working with limited data.
3Difficulty of detecting and measuring
If sparse non-negative word embeddings are used to improve interpretability, then separability and stability increase, but manual interpretation of thousands of dimensions is still required
Solution Approach 1:
The patent extracts and highlights the most semantically significant dimensions through the semantic infusion process. By identifying and emphasizing key semantic relationships, the method reduces the effective number of dimensions that require human interpretation, allowing practitioners to focus on the most relevant semantic aspects without examining all dimensions.
Solution Approach 2:
The patent introduces semantic infusion as an intermediary process that bridges the gap between high-dimensional sparse embeddings and human interpretation. This intermediary layer provides semantic labels and relationships that guide human understanding, reducing the cognitive load required to interpret the embedding space.
4Device complexity
If traditional trend analysis techniques use term frequency-based schemes, then processing is simplified, but performance deteriorates on non-uniform domain-specific datasets
Solution Approach 1:
The patent changes the fundamental parameters used in trend analysis from simple term frequency counts to semantic relationships captured in the SNN embedding space. This parameter transformation enables the detection of semantic trends and emerging topics based on meaningful semantic proximity rather than raw frequency, significantly improving performance on domain-specific datasets.
Data Source
AI summary
A method and systems for generating interpretable and embeddings for a domain-specific small corpus of text-based documents are described. A processing module may obtain the plurality of text-based documents and perform a basic cleaning of each of the plurality of text-based documents. Further, the semantic infusion module may generate the semantically infused corpus using the semantic infusion technique. An embedding generation module is configured to compute the optimal dimensionality for the infused corpus and generate the infused optimal dimensional embeddings using word2vec technique. Further, the embedding generation module is configured to generate baseline optimal dimensional embeddings which can be used to evaluate in terms of interpretability and downstream classification task performance.