Semantic Text Search Using Word Embeddings
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Solution Overview
Problem
Existing text-searching solutions rely on string matching and fail to return relevant results due to limitations in handling spelling variations, context, and semantic meaning, often missing results when users enter incorrect spellings or different word versions.
Innovation Solution
A machine learning-based semantic text-searching system that uses a deep learning model to determine semantic relationships between words, associating them based on context rather than spelling or syntax, returning words with matching and related semantic meanings within electronic documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If string matching is used for text search, then exact matches can be found quickly, but relevant results are missed when spelling variations or different word versions are used
Solution Approach 1:
The patent transforms the search parameter from exact string matching to semantic similarity based on word embeddings. Instead of comparing strings character-by-character, the system converts words to vector representations and computes cosine similarity, allowing spelling variations and different word versions to be matched based on their semantic meaning rather than exact character sequences
Solution Approach 2:
The patent replaces the mechanical string matching mechanism with a machine learning-based semantic comparison mechanism. Word embedding models transform textual data into numerical vectors, and cosine similarity computation substitutes the character-by-character comparison process, enabling the system to understand semantic relationships between words with different spellings or forms
2Adaptability or versatility
If stemming or lemmatization is used to broaden search results, then some semantic variations are captured, but context and semantic meaning are still not fully considered
Solution Approach 1:
The patent moves beyond morphological transformations (stemming/lemmatization) by changing the fundamental parameter from word form to semantic vector representation. Word embeddings capture contextual meaning and semantic relationships, allowing the system to distinguish between words that are morphologically related but semantically different, and to match words that are semantically similar despite different forms
Solution Approach 2:
The patent introduces word embeddings as an intermediary representation between the search query and the document corpus. Instead of directly comparing words or their stems, the system translates both to vector spaces where semantic relationships are preserved, allowing for more accurate semantic matching while maintaining contextual information
3Measurement precision
If semantic text search using word embeddings is implemented, then semantic relevance is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing word embeddings in a lookup table before the actual search operation. When a search query is executed, the system only needs to compute the embedding for the query word and perform a simple cosine similarity calculation against pre-computed document embeddings, significantly reducing the computational complexity during the search phase
Data Source
AI summary
Techniques and systems are described for performing semantic text searches. A semantic text-searching solution uses a machine learning system (such as a deep learning system) to determine associations between the semantic meanings of words. These associations are not limited by the spelling, syntax, grammar, or even definition of words. Instead, the associations can be based on the context in which characters, words, and/or phrases are used in relation to one another. In response to detecting a request to locate text within an electronic document associated with a keyword, the semantic text-searching solution can return strings within the document that have matching and/or related semantic meanings or contexts, in addition to exact matches (e.g., string matches) within the document. The semantic text-searching solution can then output an indication of the matching strings.