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    <abstract>Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks. Nevertheless, the degree to which the resulting context-aware representations encode information about morpho-syntactic properties of the word/token in a sentence remains unclear. In this paper, we investigate the application and impact of state-of-the-art neural token representations for automatic cue-conditional speculation and negation scope detection coupled with the independently computed morpho-syntactic information. Through this work, We establish a new state-of-the-art for the BioScope and NegPar corpus. More importantly, we provide a thorough analysis of neural representations and additional features interactions, cue-representation for conditioning, discuss model behavior on different datasets and address the annotation-induced biases in the learned representations.</abstract>
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%0 Conference Proceedings
%T Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text
%A Sergeeva, Elena
%A Zhu, Henghui
%A Tahmasebi, Amir
%A Szolovits, Peter
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F sergeeva-etal-2019-neural
%X Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks. Nevertheless, the degree to which the resulting context-aware representations encode information about morpho-syntactic properties of the word/token in a sentence remains unclear. In this paper, we investigate the application and impact of state-of-the-art neural token representations for automatic cue-conditional speculation and negation scope detection coupled with the independently computed morpho-syntactic information. Through this work, We establish a new state-of-the-art for the BioScope and NegPar corpus. More importantly, we provide a thorough analysis of neural representations and additional features interactions, cue-representation for conditioning, discuss model behavior on different datasets and address the annotation-induced biases in the learned representations.
%R 10.18653/v1/D19-6221
%U https://aclanthology.org/D19-6221/
%U https://doi.org/10.18653/v1/D19-6221
%P 178-187

Markdown (Informal)

[Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text](https://aclanthology.org/D19-6221/) (Sergeeva et al., Louhi 2019)

• Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text (Sergeeva et al., Louhi 2019)

ACL

• Elena Sergeeva, Henghui Zhu, Amir Tahmasebi, and Peter Szolovits. 2019. Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 178–187, Hong Kong. Association for Computational Linguistics.