The cost of training data in projects heavily influences the choice of using a mixture of supervised and unsupervised methods, primarily due to human labour and complexity of tasks.
Developing training data for simple zoning tasks remains low-cost and straightforward, as these tasks do not necessitate specialized tools, allowing for efficient annotation with minimal complexities.
In contrast, tasks such as lot item detection and lot parsing require a more intensive approach since they involve examining content at the sentence level, thus increasing costs and labor.
Annotation processes for sophisticated tasks are challenged by data formats incompatible with popular tools, highlighting the need for accessible formats in machine learning applications.
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