Codemixed Text Token Language Identification via Greedy Search
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Solution Overview
Problem
Current sentence-level and document-level language identifiers are unable to provide per-token language identification for codemixed text, which is essential for multilingual downstream tasks like syntactic analysis and machine translation, due to ambiguity in short texts and the need for extensive human annotation.
Innovation Solution
A method that segments codemixed text into tokens, extracts features from each token, and predicts a probability distribution over possible languages using a language identifier model, which includes character, script, and lexicon features, and applies a greedy search to assign languages, with optional constraints to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sentence-level and document-level language identifiers are used, then language identification is available for metadata, but per-token language identification is unable to be provided due to ambiguity in short texts
Solution Approach 1:
The patent segments the text into individual tokens and performs language identification at the token level rather than sentence or document level. This segmentation allows the model to handle short texts more effectively by analyzing each token independently, thereby improving measurement precision for per-token language identification without requiring overly complex model architectures.
Solution Approach 2:
The patent applies local quality by using character-level and word-level statistics specific to each token rather than global statistics. This allows the model to capture local linguistic patterns and ambiguities in short texts more accurately, improving language identification precision at the token level while maintaining manageable model complexity through focused feature extraction.
2Measurement precision
If per-token language identification is implemented, then token-level labels are obtained for multilingual tasks, but extensive human annotation is required which is infeasible for hundreds of languages
Solution Approach 1:
The patent implements self-service by training the language identifier model to automatically perform per-token language identification without requiring human annotation during deployment. The model uses character-level and word-level statistics to autonomously determine language labels for each token, eliminating the need for extensive manual annotation while maintaining high precision for token-level language identification across multiple languages.
Solution Approach 2:
The patent uses copying by leveraging existing language models and statistical patterns from trained data to generate language labels for new tokens. Instead of requiring human annotators to label each token manually, the system copies and applies learned linguistic patterns and statistics to automatically identify languages in unseen text, dramatically reducing annotation time and effort.
3Adaptability or versatility
If codemixed text is processed, then multilingual content analysis is enabled, but ambiguity increases when input text is short
Solution Approach 1:
The patent applies dynamics by adaptively adjusting the language identification approach based on the characteristics of each token and the overall text. The model dynamically weighs character-level and word-level statistics according to their relevance for each specific token, allowing it to maintain high measurement precision for language prediction in short codemixed texts while preserving versatility for processing multiple language combinations.
Solution Approach 2:
The patent uses parameter changes by modifying the statistical features and model parameters based on the detected language patterns in codemixed text. The system adjusts the weighting and selection of character-level and word-level statistics according to the specific language pair and text characteristics, thereby improving language prediction accuracy for short texts while maintaining adaptability across different multilingual scenarios.
Data Source
AI summary
A method for identifying codemixed text includes receiving codemixed text and segmenting the codemixed text into a plurality of tokens. Each token includes at least one character and is delineated from any adjacent tokens by a space. For each token of the codemixed text, the method also includes extracting features from the token and predicting a probability distribution over possible languages for the token using a language identifier model configured to receive the extracted features from the token as feature inputs. The method also includes assigning a language to each token of the codemixed text by executing a greedy search on the probability distribution over the possible languages predicted for each respective token.