Named Entity Recognition Template Save
Build a deep learning model for predicting the named entities from text.
Named Entity Recognition
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!
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Click this button to open a Workspace on FloydHub that will train this model.
Predicting named entities of GMB(Groningen Meaning Bank) corpus
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!
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