Language Selection for Text Segmentation Accuracy
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Solution Overview
Problem
Existing text processing methods face difficulties in interpreting strings of characters without breaks, as they struggle to accurately segment tokens such as words, acronyms, and names, leading to multiple ambiguous combinations and suboptimal results due to the lack of effective language selection during the segmentation process.
Innovation Solution
The method involves identifying candidate languages for a string of characters based on linguistic signals, IP address, character set, browser settings, and top-level domain, segmenting the string into multiple results, and selecting the operable language and result based on frequency of occurrence in documents and search queries, using a segmentation engine and search engine to determine the most probable segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing text processing methods segment characters into tokens without language selection, then multiple combinations of segmented strings can be produced, but the accuracy and meaningfulness of the segmentation results deteriorates due to ambiguity
Solution Approach 1:
The patent applies preliminary action by identifying candidate languages and selecting the most appropriate language before performing text segmentation. The system pre-processes the input by determining which language (e.g., English, French, German) should be used for segmentation based on various signals, thereby eliminating ambiguity before the actual segmentation occurs. This preliminary language identification step ensures that subsequent segmentation produces accurate and meaningful results.
Solution Approach 2:
The patent introduces an intermediary language selection mechanism that acts as a mediator between the raw character string and the segmentation process. This intermediary component analyzes linguistic signals, IP addresses, character sets, and other indicators to determine the appropriate language, then uses this information to guide the segmentation process. The intermediary layer resolves the contradiction by adding controlled complexity that significantly improves segmentation accuracy.
2Reliability
If multiple candidate languages are considered for segmentation, then the quality of segmented results improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by considering multiple candidate languages but not exhaustively processing all possible languages. Instead, the system identifies a limited set of most likely candidate languages based on various signals and focuses computational resources on these candidates. This approach achieves high segmentation reliability by considering sufficient language options without the excessive time cost of evaluating all possible languages.
Solution Approach 2:
The patent changes parameters by using multiple signals (linguistic patterns, IP address geolocation, character set analysis, browser settings) to dynamically adjust the language identification process. By changing and weighing multiple parameters simultaneously, the system can quickly determine the most probable language with high confidence, reducing the time needed for language identification while maintaining high reliability.
3Loss of information
If language-specific segmentation is performed, then meaningful tokenization is achieved, but the system complexity increases due to multiple language models
Solution Approach 1:
The patent applies segmentation by dividing the language processing task into distinct modules: language signal collection, candidate language identification, language selection, and language-specific text segmentation. Each module handles a specific aspect of the process, which reduces overall system complexity while preserving linguistic information. The segmentation principle is applied both to the processing pipeline and to the actual text segmentation based on the selected language.
Solution Approach 2:
The patent implements universality by creating a language-agnostic framework that can handle multiple languages through a single unified system. The core architecture remains universal and language-independent, while language-specific processing is applied only when needed based on the identified candidate languages. This multi-functional approach allows the system to preserve linguistic information for different languages without requiring completely separate processing systems for each language.
Data Source
AI summary
Methods and systems for selecting a language for text segmentation are disclosed. In one embodiment, at least a first candidate language and a second candidate language associated with a string of characters are identified, at least a first segmented result associated with the first candidate language and a second segmented result associated with the second candidate language are determined, a first frequency of occurrence for the first segmented result and a second frequency of occurrence for the second segmented result are determined, and an operable language is identified from the first candidate language and the second candidate language based at least in part on the first frequency of occurrence and the second frequency of occurrence.