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        <title>Augmenting Named Entity Recognition with Commonsense Knowledge</title>
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    <abstract>Commonsense can be vital in some applications like Natural Language Understanding (NLU), where it is often required to resolve ambiguity arising from implicit knowledge and underspecification. In spite of the remarkable success of neural network approaches on a variety of Natural Language Processing tasks, many of them struggle to react effectively in cases that require commonsense knowledge. In the present research, we take advantage of the availability of the open multilingual knowledge graph ConceptNet, by using it as an additional external resource in Named Entity Recognition (NER). Our proposed architecture involves BiLSTM layers combined with a CRF layer that was augmented with some features such as pre-trained word embedding layers and dropout layers. Moreover, apart from using word representations, we used also character-based representation to capture the morphological and the orthographic information. Our experiments and evaluations showed an improvement in the overall performance with +2.86 in the F1-measure. Commonsense reasonnig has been employed in other studies and NLP tasks but to the best of our knowledge, there is no study relating the integration of a commonsense knowledge base in NER.</abstract>
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%0 Conference Proceedings
%T Augmenting Named Entity Recognition with Commonsense Knowledge
%A Dekhili, Gaith
%A Le, Tan Ngoc
%A Sadat, Fatiha
%Y Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F dekhili-etal-2019-augmenting
%X Commonsense can be vital in some applications like Natural Language Understanding (NLU), where it is often required to resolve ambiguity arising from implicit knowledge and underspecification. In spite of the remarkable success of neural network approaches on a variety of Natural Language Processing tasks, many of them struggle to react effectively in cases that require commonsense knowledge. In the present research, we take advantage of the availability of the open multilingual knowledge graph ConceptNet, by using it as an additional external resource in Named Entity Recognition (NER). Our proposed architecture involves BiLSTM layers combined with a CRF layer that was augmented with some features such as pre-trained word embedding layers and dropout layers. Moreover, apart from using word representations, we used also character-based representation to capture the morphological and the orthographic information. Our experiments and evaluations showed an improvement in the overall performance with +2.86 in the F1-measure. Commonsense reasonnig has been employed in other studies and NLP tasks but to the best of our knowledge, there is no study relating the integration of a commonsense knowledge base in NER.
%U https://aclanthology.org/W19-3644
%P 142

Markdown (Informal)

[Augmenting Named Entity Recognition with Commonsense Knowledge](https://aclanthology.org/W19-3644) (Dekhili et al., WiNLP 2019)

• Augmenting Named Entity Recognition with Commonsense Knowledge (Dekhili et al., WiNLP 2019)

ACL

• Gaith Dekhili, Tan Ngoc Le, and Fatiha Sadat. 2019. Augmenting Named Entity Recognition with Commonsense Knowledge. In Proceedings of the 2019 Workshop on Widening NLP, page 142, Florence, Italy. Association for Computational Linguistics.