What are the best practices for NLP data preparation?

2 Collect relevant data

The next step is to collect data that is relevant to your goal and domain. Depending on your project, you may use existing data sources, such as online repositories, public APIs, or web scraping, or create your own data, such as surveys, interviews, or user-generated content. You should aim to collect as much data as possible, but also ensure that it is diverse, balanced, and representative of your target audience and scenario.

3 Clean and normalize data

Once you have collected your data, you need to clean and normalize it to eliminate any noise, errors, or inconsistencies that may impact your NLP model's performance. This can involve deleting duplicates, irrelevant records, or missing values; correcting spelling, grammar, or punctuation mistakes; converting text to lowercase or uppercase; removing stopwords, numbers, symbols, or HTML tags; tokenizing, lemmatizing, or stemming text; and encoding text to numeric or categorical values.

4 Explore and analyze data

Once you have cleaned and normalized your data, it's time to explore and analyze it to gain insights and identify patterns, trends, or anomalies. This can involve calculating descriptive statistics such as mean, median, mode, standard deviation, or frequency. You can also visualize data distributions, correlations, or outliers. Additionally, you may apply natural language understanding techniques like part-of-speech tagging, named entity recognition, or sentiment analysis. Finally, feature engineering, selection, or extraction can be performed.

5 Split and augment data

The final step in preparing for training and testing your NLP model is to split and augment your data. The data should be split into three subsets: training, validation, and test. Each subset should have a similar distribution of features and labels, and the splitting should be done randomly and proportionally. You can then augment your data to increase its size and diversity by using techniques such as synonym replacement, back translation, text generation, and data mixing. By following these best practices for NLP data preparation, you can improve the quality and performance of your project while avoiding common pitfalls and challenges. However, it is important to note that data preparation is an iterative and dynamic process that may require different steps depending on the goal, domain, and data.