Contributors:
- Luka Bašek - https://github.com/lbasek
- Luka Dulčić - https://github.com/ldulcic
Named entity recognition or entity extraction refers to a data extraction task that is responsible for finding and classification words of sentence into predetermined categories such as the names of persons, organizations, locations, expressions of times, etc.
”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).“
In the project, we used the Python programming language and the Keras library. We tested different architectures of recurrent neural networks that use LSTM and GRU memory cells. We also performed various experiments in which we searched for the optimal parameters of the neural network with the intent to accurately recognize and classify name entities.
Dataset: CoNLL2003
Labels: B-PER, I-PER, B-LOC, I-LOC, B-MISC, I-MISC, B-ORG, I-ORG, O
Used libraries: keras, spacy, numpy, scikit-learn, matplotlib
| Model | Precision | Recall | F1 |
|---|---|---|---|
| LSTM | 0.6487 | 0.7527 | 0.6821 |
| GRU | 0.6985 | 0.7589 | 0.7161 |
| BI-LSTM | 0.7995 | 0.7098 | 0.7414 |
| BI-GRU | 0.8529 | 0.7372 | 0.7861 |
| Model | Precision | Recall | F1 |
|---|---|---|---|
| LSTM | 0.8123 | 0.8162 | 0.8138 |
| GRU | 0.8085 | 0.8071 | 0.8056 |
| BI-LSTM | 0.8408 | 0.8616 | 0.8493 |
| BI-GRU | 0.8610 | 0.8604 | 0.8604 |
