| --- |
| license: wtfpl |
| datasets: |
| - ShoAnn/legalqa_klinik_hukumonline |
| language: |
| - id |
| pipeline_tag: feature-extraction |
| --- |
| # PurpleBoW |
|
|
| A very simple implementation of Bag-of-Words for those learning about Natural Language Processing. |
|
|
| ## About BoW |
|
|
| BoW is a simple count algorithm used in old spam email detection and search engine. |
| Essentially, we can train a model to remember a set of words we call ordered vocabulary to later count each words from a paragraph or sentence. |
| The resulting "prediction" is a vector of ordered counts of those words and their position doesn't matter. |
| This is quite good for simple detection, like spam emails which contains a lot of "quick", "win", or "prizes" word. |
| However, when it comes to positional meaning BoW performs very poorly. It's like instructing a gold fish to climb a coconut tree. |
