Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text

Sergeeva, Elena; Zhu, Henghui; Tahmasebi, Amir M.; Szolovits, Peter

doi:10.18653/v1/d19-6221

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…BiLSTM approaches for negation detection have been successful, with Fancellu et al (2017) reporting state of the art results for Bio-Scope (Vincze et al, 2008) abstracts. Sergeeva et al (2019) outperformed the latter using pretrained transformer models.…”

Section: Related Workmentioning

confidence: 98%

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports

Grivas¹,

Alex²,

Grover³

et al. 2020

Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

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We present an in-depth comparison of three clinical information extraction (IE) systems designed to perform entity recognition and negation detection on brain imaging reports: EdIE-R, a bespoke rule-based system, and two neural network models, EdIE-BiLSTM and EdIE-BERT, both multi-task learning models with a BiLSTM and BERT encoder respectively. We compare our models both on an in-sample and an out-of-sample dataset containing mentions of stroke findings and draw on our error analysis to suggest improvements for effective annotation when building clinical NLP models for a new domain. Our analysis finds that our rulebased system outperforms the neural models on both datasets and seems to generalise to the out-of-sample dataset. On the other hand, the neural models do not generalise negation to the out-of-sample dataset, despite metrics on the in-sample dataset suggesting otherwise.

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Section: Related Workmentioning

confidence: 98%

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports

Grivas¹,

Alex²,

Grover³

et al. 2020

Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

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“…Khandelwal and Sawant (2020) employ BERT (Devlin et al, 2019) for negation detection, including negation cue detection, scope detection, and event detection. Sergeeva et al (2019) apply ELMo (Peters et al, 2018) and BERT to negation scope detection and achieve new state-of-the-art results on two negation data sets. Instead of pursuing better results, here we aim to probe how much information about negation has been encoded in hidden states in a negation detection task.…”

Section: Negation In Other Areas Of Nlpmentioning

confidence: 99%

Revisiting Negation in Neural Machine Translation

Tang

Rönchen

Sennrich

et al. 2021

Transactions of the Association for Computational Linguistics

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In this paper, we evaluate the translation of negation both automatically and manually, in English–German (EN–DE) and English– Chinese (EN–ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, although the performance varies between language pairs and translation directions. The accuracy of manual evaluation in EN→DE, DE→EN, EN→ZH, and ZH→EN is 95.7%, 94.8%, 93.4%, and 91.7%, respectively. In addition, we show that under-translation is the most significant error type in NMT, which contrasts with the more diverse error profile previously observed for statistical machine translation. To better understand the root of the under-translation of negation, we study the model’s information flow and training data. While our information flow analysis does not reveal any deficiencies that could be used to detect or fix the under-translation of negation, we find that negation is often rephrased during training, which could make it more difficult for the model to learn a reliable link between source and target negation. We finally conduct intrinsic analysis and extrinsic probing tasks on negation, showing that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation in hidden states but nevertheless leave room for improvement.

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“…Fancellu et al (2016 continued this trend, additionally treating each negation instance separately, and successfully used BiL-STM (bidirectional Long Short-Term Memory recurrent neural networks; Hochreiter and Schmidhuber, 1997). Recently, Sergeeva et al (2019) used pre-trained transformers (Vaswani et al, 2017), namely BERT (Devlin et al, 2019), to further improve performance, albeit on a derivative of the original dataset (Liu et al, 2018). Using BERT in a two-stage sequence-labelling approach on the original ConanDoyle-neg corpus and other relevant negation corpora, Khandelwal and Sawant (2020) successfully improved previous results by a considerable margin.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Negation Resolution as Graph Parsing

Kurtz¹,

Oepen²,

Kuhlmann³

2020

Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing Into Enhanced

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We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem. Our approach allows for the straightforward inclusion of many types of graph-structured features without the need for representationspecific heuristics. In our experiments, we specifically gauge the usefulness of syntactic information for negation resolution. Despite the conceptual simplicity of our architecture, we achieve state-of-the-art results on the Conan Doyle benchmark dataset, including a new top result for our best model.

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Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text

Cited by 9 publications

References 17 publications

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports

Revisiting Negation in Neural Machine Translation

End-to-End Negation Resolution as Graph Parsing

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