@inproceedings{sergeeva-etal-2019-neural,
title = "Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text",
author = "Sergeeva, Elena and
Zhu, Henghui and
Tahmasebi, Amir and
Szolovits, Peter",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6221",
doi = "10.18653/v1/D19-6221",
pages = "178--187",
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.",
}
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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)
ACL