> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/natural-language-processing-nlp/augmentation.md).

# Augmentation

When there is not enough labeled text, you can make more of it from the text you have. The page has one overview of the options, from synonym swaps and embedding neighbors to back translation, contextualized word embeddings, and text generation, plus a pointer to a second post on the same topic.

The same notes are in [Embedding](/deep-learning/representations.md), [LANGUAGE TRANSLATION](/natural-language-processing-nlp/language-detection-identification-generation-nld-nli-nlg.md#language-translation), and [Synonyms](/natural-language-processing-nlp/foundation-nlp.md#synonyms).

The overview is the Medium post on data augmentation in NLP. It starts with [Synonyms](https://medium.com/data-science/data-augmentation-in-nlp-2801a34dfc28), similar embedded words (w2v), back translation, contextualized word embeddings, text generation, each a step further from the original sentence. Yonatan Hadar also has a medium post about this.

The Towards Data Science copy of the same post, covering synonyms, similar embedded words (w2v), back translation, contextualized word embeddings, and text generation, is <https://towardsdatascience.com/data-augmentation-in-nlp-2801a34dfc28>.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://www.mlcompendium.com/natural-language-processing-nlp/augmentation.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.