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An Experimental Study of Hybrid Machine Learning Models for Extracting Named Entities

11 pagesPublished: March 18, 2019

Abstract

The paper describes two hybrid neural network models for named entity recognition (NER) in texts, as well as results of experiments with them. The first model, namely Bi-LSTM-CRF, is known and used for NER, while the other model named Gated-CNN- CRF is proposed in this work. It combines convolutional neural network (CNN), gated linear units, and conditional random fields (CRF). Both models were tested for NER on three different language datasets, for English, Russian, and Chinese. All resulted scores of precision, recall and F1-measure for both models are close to the state-of-the-art for NER, and for the English dataset CoNLL-2003, Gated-CNN-CRF model achieves 92.66 of F1-measure, outperforming the known result.

Keyphrases: hybrid machine learning models, machine learning based ner, named entity recognition, neural networks

In: Gerhard Wohlgenannt, Ruprecht von Waldenfels, Svetlana Toldova, Ekaterina Rakhilina, Denis Paperno, Olga Lyashevskaya, Natalia Loukachevitch, Sergei O. Kuznetsov, Olga Kultepina, Dmitry Ilvovsky, Boris Galitsky, Ekaterina Artemova and Elena Bolshakova (editors). Proceedings of Third Workshop "Computational linguistics and language science", vol 4, pages 50-60.

BibTeX entry
@inproceedings{CLLS2018:Experimental_Study_Hybrid_Machine,
  author    = {Lei Jiang and Elena Bolshakova},
  title     = {An Experimental Study of Hybrid Machine Learning Models for Extracting Named Entities},
  booktitle = {Proceedings of Third Workshop "Computational linguistics and language science"},
  editor    = {Gerhard Wohlgenannt and Ruprecht von Waldenfels and Svetlana Toldova and Ekaterina Rakhilina and Denis Paperno and Olga Lyashevskaya and Natalia Loukachevitch and Sergei O. Kuznetsov and Olga Kultepina and Dmitry Ilvovsky and Boris Galitsky and Ekaterina Artemova and Elena Bolshakova},
  series    = {EPiC Series in Language and Linguistics},
  volume    = {4},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5283},
  url       = {/publications/paper/4jj4},
  doi       = {10.29007/dp5m},
  pages     = {50-60},
  year      = {2019}}
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