Data Acquisition Step Required for Natural Language Processing

Data acquisition is a crucial and preliminary step for doing any Project. In this blog, we will see how we can accumulate data for any project.

However, the availability of the data can vary widely. Let’s understand different scenarios we may face while doing NLP Project.

Case 1. When Data is Readily Available:

If the data is readily available in a table format or any structured form, it simplifies the process. You can directly use it for analysis or training models.

Case 2. Available but Unorganized:

In cases where the data is available but unorganized, Data Engineers or data preprocessing tools can be employed.

Case 3. Less Data Available:

If you have data but that is not sufficient for your job. Hence, Some techniques that can be applied to increase the amount of data when data is scarce. A data augmentation technique is applied to increase the diversity of Data.

Data augmentation: This involves creating new training instances by applying various transformations to the existing data.

i. Text Paraphrasing: Generating new sentences with similar meaning but different wording. This can involve using synonym replacement, rephrasing sentences, or changing the sentence structure while maintaining the original intent.

ii. Back Translation: Translating sentences from the original language to another language and then back to the original language. This introduces variations in the language while preserving the semantic meaning.

iii. Word Embedding-based Approaches: Using word embeddings to find synonyms and replace words in the text. This can help generate new sentences that have similar meanings but different word choices.

iv. Rule-based Transformations: Applying specific linguistic rules to modify the text. For example, changing verb tenses, converting active voice to passive voice, or altering grammatical structures.

v. Bigram Flip: a bigram is a sequence of two adjacent words. Bigram flip involves swapping adjacent word pairs in a sentence, effectively flipping the positions of two consecutive words. Ex: Original Sentence: The quick brown fox jumps over the lazy dog. Bigram Flip: The quick fox brown jumps over the lazy dog.

4. Other Methods:

Depending on the specific scenario, there are various other methods for data acquisition:

i. Public Dataset: Data can be collected from any Public dataset. Ex: Kaggle.

ii. Web Scraping: Extracting data from websites, forums, or social media platforms.

iii. APIs (Application Programming Interfaces): Accessing data from online services or databases.

iv. Text Mining: Analyzing and extracting information from large amounts of textual data.

v. PDF, Image, Audio: Different techniques like Speech to text, Image to text can be applied to create/generate more data from these files.

Each scenario requires a tailored approach, and the choice of method depends on the nature of the data, the problem you are trying to solve, and the resources available. The goal is to have a clean, representative, and sufficient dataset for training and evaluating NLP models.

Once you have the data, the next step is Text Processing.

Please explore Complete Data Science Roadmap..

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