Named Entity Recognition is one of the most common NLP problems. The goal is classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. What can you use it for? Here are a few ideas - social media, chatbot, customer support tickets, survey responses, and data mining!
Click this button to open a Workspace on FloydHub that will train this model.
In this notebook we will perform a Sequence Tagging with a LSTM-CRF model to extract the named entities from the annotated corpus.
Entity tags are encoded using a BIO annotation scheme, where each entity label is prefixed with either B or I letter. B- denotes the beginning and I- inside of an entity. The prefixes are used to detect multiword entities, e.g. sentence:"World War II", tags:(B-eve, I-eve, I-eve). All other words, which don’t refer to entities of interest, are labeled with the O tag.
| Tag | Label meaning | Example Given |
|---|---|---|
| geo | Geographical Entity | London |
| org | Organization | ONU |
| per | Person | Bush |
| gpe | Geopolitical Entity | British |
| tim | Time indicator | Wednesday |
| art | Artifact | Chrysler |
| eve | Event | Christmas |
| nat | Natural Phenomenon | Hurricane |
| O | No-Label | the |
We will:
- Preprocess text data for NLP
- Build and train a Bi-directional LSTM-CRF model using Keras and Tensorflow
- Evaluate our model on the test set
- Run the model on your own sentences!