%0 Conference Paper %1 8102402 %A Gambäck, B. %A Sikdar, U. K. %B 2017 IST-Africa Week Conference (IST-Africa) %D 2017 %K NER amharic deepLearning ethiopic %P 1-8 %R 10.23919/ISTAFRICA.2017.8102402 %T Named entity recognition for Amharic using deep learning %U http://ieeexplore.ieee.org/abstract/document/8102402/ %X The paper describes a named entity recognition system for Amharic, an under-resourced language, using a recurrent neural network, a bi-directional long short term memory model to identify and classify tokens into six predefined classes: Person, Location, Organization, Time, Title, and Other (non-named entity tokens). Word vectors based on semantic information are built for all tokens using an unsupervised learning algorithm, word2vec. The word vectors were merged with a set of specifically developed language independent features and together fed to the neural network model to predict the classes of the words. When evaluated by 10-fold cross-validation, the created Amharic named entity recogniser achieved good average precision (77.2%), but did worse on recall (63.4%), for a 69.7% F1-score.

@inproceedings{8102402, abstract = {The paper describes a named entity recognition system for Amharic, an under-resourced language, using a recurrent neural network, a bi-directional long short term memory model to identify and classify tokens into six predefined classes: Person, Location, Organization, Time, Title, and Other (non-named entity tokens). Word vectors based on semantic information are built for all tokens using an unsupervised learning algorithm, word2vec. The word vectors were merged with a set of specifically developed language independent features and together fed to the neural network model to predict the classes of the words. When evaluated by 10-fold cross-validation, the created Amharic named entity recogniser achieved good average precision (77.2%), but did worse on recall (63.4%), for a 69.7% F1-score.}, added-at = {2018-02-26T09:43:28.000+0100}, author = {Gambäck, B. and Sikdar, U. K.}, biburl = {https://www.bibsonomy.org/bibtex/2901e1863f970a171d26409f697b4b3ba/asmelash}, booktitle = {2017 IST-Africa Week Conference (IST-Africa)}, description = {Named entity recognition for Amharic using deep learning - IEEE Conference Publication}, doi = {10.23919/ISTAFRICA.2017.8102402}, interhash = {237f27f659f405ec4106ac1e2c2ef8ee}, intrahash = {901e1863f970a171d26409f697b4b3ba}, keywords = {NER amharic deepLearning ethiopic}, month = may, pages = {1-8}, timestamp = {2018-02-26T09:43:28.000+0100}, title = {Named entity recognition for Amharic using deep learning}, url = {http://ieeexplore.ieee.org/abstract/document/8102402/}, year = 2017 }