The question we address is, which data quality requirements can be stated for digital natural language text and which quality measures can be automatically ...
We follow an empirical approach from data quality toward text quality, where the expectations of the consumer, human or machine, take the centre stage.
It is stated that apart from text accessibility today only representational text quality metrics can be derived and computed automatically and Interestingly ...
Feb 26, 2022 · For example, are you classifying text into positive and negative sentiment? You can use accuracy as a metric. Is your model generating new text?
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How do you evaluate natural language processing?
How to measure text quality?
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Feb 15, 2010 · My question is: given a random piece of text, is there a process to determine whether that text is well written, and is a good candidate for use in NLP?
Data quality can be simply described as 'fitness for an intended use' and is usually based on factors such as accuracy, completeness, consistency, reliability.
Jan 24, 2021 · We want to quantify and compare transcription quality to filter out the best samples for semantic inference of our customers' input in a downstream task.
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This article explains how to test and perform evaluation of NLP systems that generate or extract text.
Dec 7, 2020 · I have developed a pipeline for text data preprocessing with different clean up techniques like Stemming , Lemmatization, Stop words removal etc.
Aug 11, 2021 · Automate the evaluation process for NLG outputs with evaluation metrics to both save time and increase the capacity of scored instances.