Inductive Machine Learning Hyphenation Patterns
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Solution Overview
Problem
Existing dictionary-based approaches for automatic word hyphenation are costly to maintain, prone to errors, and inefficient in handling obscure 'out-of-vocabulary' words due to storage constraints, which limits their effectiveness across multiple languages.
Innovation Solution
The use of inductive machine learning on training data to generate hyphenation patterns for multi-lingual word hyphenation, where patterns are stored in a language-specific lexicon file and used to determine appropriate hyphenation points within input words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dictionary-based approaches are used for word hyphenation, then hyphenation accuracy for common words is improved, but maintenance cost and storage space increase
Solution Approach 1:
The patent segments the hyphenation problem by creating separate language-specific lexicon files rather than maintaining one large universal dictionary. Each lexicon file contains only the hyphenation patterns relevant to a specific language, reducing storage requirements while maintaining accuracy for that language. The system divides the vocabulary into language-specific subsets that can be independently managed and stored.
Solution Approach 2:
The patent extracts only the essential hyphenation patterns from comprehensive dictionaries, storing minimal necessary data in lexicon files. Instead of storing complete dictionary entries with all word information, the system extracts and stores only the hyphenation-specific patterns and rules needed for the hyphenation function, significantly reducing storage space while preserving hyphenation accuracy.
2Adaptability or versatility
If expanded dictionaries are used to include obscure words, then coverage of out-of-vocabulary words is improved, but maintenance effort and storage cost increase
Solution Approach 1:
The patent implements self-service through automatic generation of hyphenation patterns from training data. The system uses machine learning algorithms to automatically analyze training corpora and generate hyphenation patterns for obscure and out-of-vocabulary words without requiring manual dictionary compilation. This automated approach reduces maintenance effort while improving word coverage, as the system can adapt to new words and languages by processing training data rather than requiring manual dictionary updates.
Solution Approach 2:
The patent changes the fundamental parameter from static dictionary entries to dynamic generated patterns. Instead of relying on pre-computed dictionary coverage, the system generates hyphenation patterns on-demand from training data using induction parameters. This allows the system to adapt to obscure words and new languages by changing the generation parameters rather than expanding fixed dictionaries, reducing maintenance complexity.
3Measurement precision
If manual techniques are used to augment dictionary-based approaches, then hyphenation accuracy is improved, but time and effort required increase
Solution Approach 1:
The patent replaces the mechanical manual process of dictionary compilation and augmentation with an automated machine learning system. The inductive machine learning algorithms automatically analyze training data, extract hyphenation patterns, and generate lexicon files without human intervention. This substitution of manual mechanical work with automated computational processes maintains high hyphenation accuracy while dramatically reducing the time and effort required for maintenance.
Solution Approach 2:
The patent performs preliminary action by pre-processing training data to generate hyphenation patterns in advance. The system uses induction algorithms to pre-compute hyphenation rules from training corpora and stores them in lexicon files before they are needed. This preliminary automated processing eliminates the need for time-consuming manual augmentation when hyphenation is actually performed, as the patterns are already generated and ready for use.
Data Source
AI summary
Tools and techniques are described for providing multi-lingual word hyphenation using inductive machine learning on training data. Methods provided by these techniques may receive training data that includes hyphenated words, and may inductively generate hyphenation patterns that represent substrings of these words. The hyphenation patterns may include the substrings and hyphenation codes associated with characters occurring in the substrings. The methods may receive induction parameters applicable to generating the hyphenation patterns, and may store the hyphenation patterns into a language-specific lexicon file. These methods may also receive requests to hyphenate input words that occur in a human language, and may evaluate how to process the request based on the language. The methods may search for hyphenation patterns occurring in the input words, with the hyphenation patterns being stored in the lexicon file. Finally, the methods may respond to the request, indicating whether the hyphenation patterns occurred in the input words.