Named Entitiy Recognition

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.

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).“

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

Results:

Model results where additional features has been not used:

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 accuracy and loss without additional features

---

Model results where additional features has been used:

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

Model accuracy and loss with additional features