Word Embedding Search Using Context Concatenation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search engines face challenges in accurately retrieving relevant information due to their inability to evaluate the context and semantic meaning of words, leading to poor precision in keyword searches, especially with the increasing complexity of unstructured data resources.
Innovation Solution
A method that utilizes word embeddings generated by a neural network model to compare the context of a selected word with other texts, allowing for the identification of semantically similar words and concepts by concatenating the word and its context and inputting the sequence into a word embedding model for comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search methods are used, then search speed is maintained, but search precision deteriorates due to inability to evaluate context and semantic meaning
Solution Approach 1:
The patent replaces traditional mechanical keyword-matching search systems with a neural network-based semantic analysis system. The word embedding model transforms textual information into numerical vectors, enabling the system to understand semantic meaning and context rather than simply matching keywords. This substitution of mechanical operations with intelligent processing resolves the contradiction by achieving high search precision through semantic understanding while managing complexity through efficient vector operations.
Solution Approach 2:
The patent changes the fundamental parameters of text representation from discrete keywords to continuous vector embeddings. By transforming text into numerical vectors that capture semantic meaning and contextual relationships, the system enables precise search operations based on semantic similarity rather than exact keyword matches. This parameter transformation allows the system to evaluate context and meaning, significantly improving search precision.
2Measurement precision
If word embedding models are used to analyze semantic meaning, then search precision is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing word embeddings for all texts in the database before actual search operations. When a search query is executed, the system only needs to compute the embedding for the query text and compare it with pre-computed embeddings, significantly reducing processing time. This advance preparation resolves the contradiction by enabling fast search operations while maintaining high precision through semantic analysis.
Solution Approach 2:
The patent uses copying by creating vector representations (embeddings) of texts that can be stored and reused multiple times. Instead of re-analyzing the semantic meaning of the same text repeatedly, the system copies the computed embedding and uses it for multiple search comparisons. This copying mechanism dramatically reduces processing time while preserving the full semantic analysis capability.
3Reliability
If context information is included in search analysis, then retrieval accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent merges the target word with its surrounding context into a single concatenated input sequence for the word embedding model. By combining the word and its context together, the model processes them as a unified representation, capturing the semantic meaning of the word within its contextual framework. This merging approach improves retrieval accuracy by considering contextual information while managing computational complexity through efficient single-pass processing.
Solution Approach 2:
The patent transitions from analyzing words in isolation to analyzing words within their contextual dimension by incorporating surrounding text. The word embedding model processes the concatenated word-context sequence, creating vector representations that exist in a higher-dimensional semantic space that captures contextual relationships. This dimensional expansion enables more accurate retrieval by considering contextual nuances without proportionally increasing computational complexity.
Data Source
AI summary
A method, computer system, and a computer program product for searching are provided. The method may include receiving a word and a context of the word. The context may include additional words. A first word embedding may be generated by inputting a sequence into a word embedding model that resultantly outputs the first word embedding. The sequence may include the word and the context that are concatenated to each other in the sequence. The first word embedding may be compared with other word embeddings. The other word embeddings may have been generated by inputting respective text portions of other texts into the word embedding model. A candidate match of the other texts may be presented. A respective word embedding of the candidate match may be, of the other word embeddings, most similar to the first word embedding according to the comparing.