Speculation and Negation Scope Detection via Convolutional Neural Networks

Qian, Zhong; Li, Peifeng; Zhu, Qiaoming; Zhou, Guodong; Luo, Zhigang; Luo, Wei

doi:10.18653/v1/d16-1078

Cited by 52 publications

(36 citation statements)

References 17 publications

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“…Work by Fancellu, Lopez, and Webber (2016) and Qian et al . (2016), although not focused on the sentiment analysis domain, should be highlighted. Fancellu et al .…”

Section: Related Workmentioning

confidence: 99%

“…Qian et al . (2016) propose a convolutional neural network-based model with probabilistic weighted average pooling to also address negation scope detection. This system first extracts path features from syntactic trees with a convolutional layer and concatenates them with their relative positions into one feature vector, which is then fed into a soft-max layer to compute the confidence scores of its location labels.…”

Section: Related Workmentioning

confidence: 99%

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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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“…Work by Fancellu, Lopez, and Webber (2016) and Qian et al . (2016), although not focused on the sentiment analysis domain, should be highlighted. Fancellu et al .…”

Section: Related Workmentioning

confidence: 99%

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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show abstract

“…By contrast, machine learning-based systems do not require active human expert participation to adapt to a new dataset/domain. Earlier works utilizing the statistical approaches for negation scope detection include Support Vector Machines (SVM), Conditional Random Fields based models (CRF) (Agarwal and Yu, 2010; Councill et al, 2010) as well as hybrid CRF-SVM ensemble models (Zhu et al, 2010) (Morante and Daelemans, 2009) Recently, Neural Network-based approaches have been proposed for such tasks, including Convolutional Neural Network (CNN)-based (Qian et al, 2016) and Long Short Term Memory (LSTM)-based (Fancellu et al, 2017;Sergeeva et al, 2019) models.…”

Section: Previous Workmentioning

confidence: 99%

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

Sergeeva¹,

Zhu²,

Tahmasebi³

et al. 2019

Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there have been numerous reports on improved performance on a variety of natural language tasks. Nevertheless, the degree to which the resulting context-aware representations can encode information about morpho-syntactic properties of the tokens in a sentence remains unclear. In this paper, we investigate the application and impact of state-of-the-art neural token representations for automatic cueconditional 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 corpora. Furthermore, we provide a thorough analysis of neural representations and additional features interactions, cue-representation for conditioning, discussing model behavior on different datasets and, finally, address the annotation-induced biases in the learned representations.

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“…Machine learning methods have been applied to cope with the negation detection task, using mainly a conditional random field (CRF) algorithm with dense vector features, such as character or word embedding [ 13 , 14 ]. More recently, deep learning approaches using recurrent neural networks (RNNs), convolutional neuronal networks (CNNs), and encoder-decoder models have also been exploited to solve this task [ 15 - 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Some approaches were proposed to rely on syntactic parse information to automatically extract the most relevant features [ 51 ]. Qian et al [ 15 ] designed a CNN-based model with probabilistic weighted average pooling to address speculation and negation scope detection. Evaluation of the BioScope corpus showed that their approach achieved substantial improvement.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Pretrained Language Models on Negation and Speculation Detection in Cross-Lingual Medical Text: Comparative Study

Zavala¹,

Martı́nez²

2020

JMIR Med Inform

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Background Negation and speculation are critical elements in natural language processing (NLP)-related tasks, such as information extraction, as these phenomena change the truth value of a proposition. In the clinical narrative that is informal, these linguistic facts are used extensively with the objective of indicating hypotheses, impressions, or negative findings. Previous state-of-the-art approaches addressed negation and speculation detection tasks using rule-based methods, but in the last few years, models based on machine learning and deep learning exploiting morphological, syntactic, and semantic features represented as spare and dense vectors have emerged. However, although such methods of named entity recognition (NER) employ a broad set of features, they are limited to existing pretrained models for a specific domain or language. Objective As a fundamental subsystem of any information extraction pipeline, a system for cross-lingual and domain-independent negation and speculation detection was introduced with special focus on the biomedical scientific literature and clinical narrative. In this work, detection of negation and speculation was considered as a sequence-labeling task where cues and the scopes of both phenomena are recognized as a sequence of nested labels recognized in a single step. Methods We proposed the following two approaches for negation and speculation detection: (1) bidirectional long short-term memory (Bi-LSTM) and conditional random field using character, word, and sense embeddings to deal with the extraction of semantic, syntactic, and contextual patterns and (2) bidirectional encoder representations for transformers (BERT) with fine tuning for NER. Results The approach was evaluated for English and Spanish languages on biomedical and review text, particularly with the BioScope corpus, IULA corpus, and SFU Spanish Review corpus, with F-measures of 86.6%, 85.0%, and 88.1%, respectively, for NeuroNER and 86.4%, 80.8%, and 91.7%, respectively, for BERT. Conclusions These results show that these architectures perform considerably better than the previous rule-based and conventional machine learning–based systems. Moreover, our analysis results show that pretrained word embedding and particularly contextualized embedding for biomedical corpora help to understand complexities inherent to biomedical text.

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Speculation and Negation Scope Detection via Convolutional Neural Networks

Cited by 52 publications

References 17 publications

Negation detection for sentiment analysis: A case study in Spanish

Negation detection for sentiment analysis: A case study in Spanish

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

The Impact of Pretrained Language Models on Negation and Speculation Detection in Cross-Lingual Medical Text: Comparative Study

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