Improving sentiment analysis with multi-task learning of negation

Barnes, Jeremy; Velldal, Erik; Øvrelid, Lilja

doi:10.1017/s1351324920000510

Cited by 28 publications

(26 citation statements)

References 51 publications

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“…They use BiLSTMs for both the encoder and decoder. Barnes et al (2019) explore another joint task that has been explored often, namely Sentiment Analysis systems that are jointly trained with Negation Detection systems. They mention that since Sentiment Analysis is a harder task than negation detection, and negation data is used as a task in the pipeline for sentiment analysis, they perform selective sharing of LSTM layers, and use negation as an auxillary task on which the sentiment analysis system is trained.…”

Section: Multitask Learning Using Negation Scope Resolutionmentioning

confidence: 99%

Multitask Learning of Negation and Speculation using Transformers

Khandelwal¹,

Britto²

2020

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

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Detecting negation and speculation in language has been a task of considerable interest to the biomedical community, as it is a key component of Information Extraction systems from Biomedical documents. Prior work has individually addressed Negation Detection and Speculation Detection, and both have been addressed in the same way, using a 2 stage pipelined approach: Cue Detection followed by Scope Resolution. In this paper, we propose Multitask learning approaches over 2 sets of tasks: Negation Cue Detection & Speculation Cue Detection, and Negation Scope Resolution & Speculation Scope Resolution. We utilise transformer-based architectures like BERT, XLNet and RoBERTa as our core model architecture, and finetune these using the Multitask learning approaches. We show that this Multitask Learning approach outperforms the single task learning approach, and report new state-of-the-art results on Negation and Speculation Scope Resolution on the BioScope Corpus and the SFU Review Corpus.

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Section: Multitask Learning Using Negation Scope Resolutionmentioning

confidence: 99%

Multitask Learning of Negation and Speculation using Transformers

Khandelwal¹,

Britto²

2020

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

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“…In the field of sentiment analysis, one of the latest published works proposes a multi-task approach to explicitly incorporate information about negation (Barnes, Velldal, and Øvrelid 2019). Similarly to Fancellu et al .…”

Section: Related Workmentioning

confidence: 99%

Negation detection for sentiment analysis: A case study in Spanish

Jiménez-Zafra

Cruz-Díaz²,

Taboada

et al. 2020

Nat. Lang. Eng.

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Abstract Accurate negation identification is one of the most important tasks in the context of sentiment analysis. In order to correctly interpret the sentiment value of a particular expression, we need to identify whether it is in the scope of negation. While much of the work on negation detection has focused on English, we have seen recent developments that provide accurate identification of negation in other languages. In this paper, we provide an overview of negation detection systems and describe an implementation of a Spanish system for negation cue detection and scope identification. We apply this system to the sentiment analysis task, confirming also for Spanish that improvements can be gained from accurate negation detection. The paper contributes an implementation of negation detection for sentiment analysis in Spanish and a detailed error analysis. This is the first work in Spanish in which a machine learning negation processing system is applied to the sentiment analysis task. Existing methods have used negation rules that have not been assessed, perhaps because the first Spanish corpus annotated with negation for sentiment analysis has only recently become available.

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“…NLP researchers have made great progress in building computational models for these tasks (Barnes et al, 2017 ; Rotsztejn et al, 2018 ). However, these machine learning (ML) models still lack core human language understanding skills that humans perform effortlessly (Poria et al, 2020 ; Barnes et al, 2021 ). Barnes et al ( 2019 ) find that sentiment models struggle with different linguistic elements such as negations or sentences containing mixed sentiment toward several target aspects.…”

Section: Introductionmentioning

confidence: 99%

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

Hollenstein

Renggli

Glaus

et al. 2021

Front. Hum. Neurosci.

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Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.

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Improving sentiment analysis with multi-task learning of negation

Cited by 28 publications

References 51 publications

Multitask Learning of Negation and Speculation using Transformers

Multitask Learning of Negation and Speculation using Transformers

Negation detection for sentiment analysis: A case study in Spanish

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

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