Phrase Generation Using Context Vectors for Language Independence
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Solution Overview
Problem
Conventional phrase generation techniques are language dependent and struggle to identify quality phrases effectively, making it difficult to improve search results precision and latency in natural language processing applications.
Innovation Solution
A method that identifies phrase candidates by calculating context vectors and determining similarity between them using mutual information scores, allowing for language-independent phrase generation and filtering out non-phrases, with a seed collection of good or bad phrases to refine the identification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional phrase generation techniques are used, then language-dependent phrase identification can be achieved, but the ability to identify quality phrases across multiple languages deteriorates
Solution Approach 1:
The patent segments the phrase identification process into distinct components: extracting n-grams from corpus, computing context vectors for each n-gram, calculating similarity metrics between candidates, and filtering based on quality thresholds. This segmentation allows the system to handle multiple languages independently while maintaining high identification accuracy through systematic processing of each language's specific characteristics
Solution Approach 2:
The patent introduces context vectors as an intermediary representation that bridges the gap between raw n-grams and quality assessment. These vectors capture semantic and contextual information in a language-agnostic manner, enabling accurate phrase identification across different languages without requiring language-specific grammatical rules
2Manufacturing precision
If conventional phrase generation techniques are used, then simple processing can be maintained, but search results precision and latency improvement deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-computing context vectors for all n-grams in the corpus before actual phrase identification is needed. This pre-processing step stores contextual information that can be quickly retrieved and compared during search operations, improving both precision and latency without adding complexity to the real-time processing pipeline
Solution Approach 2:
The system uses its own computed context vectors and similarity metrics to automatically identify and filter quality phrases without requiring external linguistic expertise or manual intervention for each language. The methodology self-adapts to different languages by processing their specific textual patterns through the same unified framework
3Reliability
If a large collection of phrase candidates is processed, then comprehensive phrase identification can be achieved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by filtering the large collection of n-gram candidates through multiple stages: initial context vector computation, similarity threshold filtering, and quality phrase selection. This staged approach processes only the most promising candidates in detail while quickly eliminating unlikely candidates, achieving comprehensive identification without processing every single n-gram equally
Solution Approach 2:
The system dynamically adjusts processing parameters such as similarity thresholds and context window sizes based on the specific language and corpus characteristics. This allows the system to maintain high identification completeness while optimizing processing speed for different language pairs and text types
Data Source
AI summary
Aspects directed to phrase generation are provided. A method is provided that includes identifying a plurality of phrase candidates from a plurality of text string entries in a corpus. For each phrase candidate: identifying a plurality of left contexts and a plurality of right contexts for the phrase candidate, each left context of the plurality of left contexts being a nearest unique feature to the right of the phrase candidate in a text string entry and each right context of the plurality of right contexts being the nearest unique feature to the right of the phrase candidate, and calculating a left context vector including a score for each left context feature and a right context vector including a score for each right context feature of the phrase candidate. A similarity is determined between pairs of phrase candidates using the respective left and right context vectors for each phrase candidate.