Named-entity recognition_ (NER)__ (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc._

In this project, we will work with a NER dataset provided by kaggle. The dataset can be accessed here. This dataset is the extract from GMB corpus which is tagged, annotated and built specifically to train the classifier to predict named entities such as name, location, etc. Dataset also includes one additional feature (POS) that can be used in classification. In this project, however we are working only with one feature sentence.

1. Load the dataset

Lets begin by loading and visualising the dataset. To download ner_dataset.csv go to this link in kaggle.

We will have to use encoding = ‘unicode_escape’ while loading the data. This function takes a parameter to toggle the addition of the wrapping quotes and escaping of that quote in a string.

import pandas as pd
data = pd.read_csv('ner_dataset.csv', encoding= 'unicode_escape')
data.head()

From the dataset we can see the sentences are already broken into tokens in the column ‘Word’ which will be our feature (X). The column ‘sentence #’ displays the sentence number once and then prints NaN till the next sentence begins. The ‘Tag’ column will be our label (y).

#ner #bidirectional #bi-lstm #named-entity-recognition #keras