Cross-Language Word Segmentation for Low-Resource Language Data
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Solution Overview
Problem
In low-resource regions, acquiring sufficient language data for word segmentation is challenging due to scarce user data and lack of annotation resources, affecting downstream tasks like entity and semantic recognition.
Innovation Solution
A method and device for segmenting words using cross-language data augmentation, where HRL data is processed to generate a word segmentation corpus, and LRL data is processed to select candidate word segments based on matching, training a word segmentation model with these data to output results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional word segmentation methods are used, then word segmentation can be performed based on spaces, but incorrect word segmentation occurs when users input multiple words together, affecting downstream tasks
Solution Approach 1:
The patent applies preliminary action by pre-processing user input to identify and mark multi-word sequences before training the word segmentation model. This allows the model to learn from corrected segmentation patterns during training, improving segmentation accuracy without requiring complex real-time correction mechanisms during actual use.
Solution Approach 2:
The patent implements feedback by using downstream task performance metrics to evaluate and refine word segmentation results. The system continuously monitors performance and adjusts segmentation models based on feedback from entity recognition and semantic understanding tasks, creating an iterative improvement cycle.
2Reliability
If a word segmentation model is trained with sufficient language data, then segmentation accuracy improves, but data acquisition becomes difficult in low-resource regions due to insufficient user data and lack of annotation resources
Solution Approach 1:
The patent uses an intermediary approach by introducing a cross-lingual transfer learning mechanism that acts as a mediator between high-resource and low-resource languages. The system leverages annotated data from high-resource languages to train a shared representation model, which then can annotate and segment low-resource languages without requiring extensive local annotated data, thus bridging the data scarcity gap.
Solution Approach 2:
The patent applies universality by developing a multi-functional word segmentation system that can handle multiple languages with different resource levels. The same core model architecture serves both high-resource and low-resource languages, with the ability to adapt to different linguistic contexts through cross-lingual transfer, eliminating the need for separate models for each language.
3Quantity of substance
If cross-language data augmentation is used to generate training data, then data resource balance is achieved, but the complexity of data processing and model training increases
Solution Approach 1:
The patent applies segmentation by dividing the complex cross-lingual data processing task into distinct modular stages: (1) acquiring source language data, (2) translating to target language, (3) segmenting into word-level units, (4) annotating with linguistic information, and (5) assembling into training corpora. This modular approach makes the complex process more manageable and scalable.
Solution Approach 2:
The patent uses copying by creating synthetic training data through translation and transformation of existing high-quality data. Instead of collecting new data manually for each low-resource language, the system copies and adapts data from high-resource languages through automated translation and processing pipelines, significantly reducing the manual effort required.
Data Source
AI summary
Provided is a method and a device for segmenting words, and a storage medium. The method includes: acquiring a plurality of groups of high-resource language (HRL) data, and acquiring a plurality of groups of first word segmentation language data by processing the plurality of groups of HRL data; acquiring a plurality of groups of low-resource language (LRL) data, acquiring a plurality of candidate word segments, and selecting second word segmentation language data from the plurality of candidate word segments; acquiring a word segmentation model by training based on the second word segmentation language data, and outputting a plurality of candidate word segmentation results; and selecting the candidate word segmentation result with a highest matching degree as a word segmentation result based on a matching degree between each of the candidate word segmentation results and the first word segmentation corpus.