Named Entity Recognition Using LSTM's and Keras
Introduction
Named entity recognition (NER) ‒ also called entity identification or entity extraction ‒ is an information extraction technique that automatically identifies named entities in a text and classifies them into predefined categories. Entities can be names of people, organizations, locations, times, quantities, monetary values, percentages, and more.
Example:
”Android Inc. was founded in Palo Alto, California, in October 2003 by Andy Rubin, Rich Miner, Nick Sears, and Chris White.”
”ORG(Android Inc.) O(was founded in) LOC(Palo Alto), LOC(California), O(in) TIME(October 2003) O(by) PER(Andy Rubin), PER(Rich Miner), PER(Nick Sears), O(and) PER(Chris White).“
Dataset: ner_dataset.csv
Labels: 'O', 'B-geo', 'B-gpe', 'B-per', 'I-geo', 'B-org', 'I-org', 'B-tim','B-art', 'I-art', 'I-per', 'I-gpe', 'I-tim', 'B-nat', 'B-eve','I-eve', 'I-nat'
Used libraries: keras, tensorflow, numpy, scikit-learn, matplotlib, pandas
Comparing the length of sentences
Results:
| Word | True | Pred |
|---|---|---|
| The | O | O |
| Vatican | B-org | B-org |
| has | O | O |
| announced | O | O |
| that | O | O |
| U.S. | B-org | B-geo |
| President | B-per | B-per |
| George | I-per | I-per |
| Bush | I-per | I-per |
| will | O | O |
| meet | O | O |
| with | O | O |
| Pope | O | O |
| Benedict | B-per | I-per |
| in | O | O |
| June | B-tim | B-Tim |