Neural Architectures for Named Entity Recognition

Lample, Guillaume; Ballesteros, Miguel; Subramanian, Sandeep; Kawakami, K.; Dyer, Chris

doi:10.48550/arxiv.1603.01360

Cited by 239 publications

(359 citation statements)

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“…The downside of such models is the requirement of extensive feature engineering. Another method for NER is to use DL models (Ma and Hovy, 2016;Lample et al, 2016). This models not only select text spans containing named entities but also extract quality entity representations which can be used as input for NEN.…”

Section: Related Workmentioning

confidence: 99%

Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

Nesterov¹,

Umerenkov²

2022

Preprint

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Medical entity extraction (EE) is a standard procedure used as a first stage in medical texts processing. Usually Medical EE is a two-step process: named entity recognition (NER) and named entity normalization (NEN). We propose a novel method of doing medical EE from electronic health records (EHR) as a single-step multi-label classification task by fine-tuning a transformer model pretrained on a large EHR dataset. Our model is trained end-to-end in an distantly supervised manner using targets automatically extracted from medical knowledge base. We show that our model learns to generalize for entities that are present frequently enough, achieving human-level classification quality for most frequent entities. Our work demonstrates that medical entity extraction can be done end-to-end without human supervision and with human quality given the availability of a large enough amount of unlabeled EHR and a medical knowledge base.

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Section: Related Workmentioning

confidence: 99%

Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

Nesterov¹,

Umerenkov²

2022

Preprint

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“…Conditional Random Field To model the label consistency, i.e., the emotion transfer on a dialogue, a linear chain conditional random field is employed to yield final emotion tags of each utterance. Following the description of (Lample et al 2016), for an input set of utterances U = u 1 , u 2 , ..., u n and a sequence of tag predictions y = y 1 , y 2 , .., y n , y i ∈ 1, • • • , K (K is the number of emotion tags), the score of the sequence is defined as:…”

Section: Consistency Modelingmentioning

confidence: 99%

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

Chen¹,

Chong²,

Xu³

et al. 2021

Preprint

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Emotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. Existing ERC methods mostly model the self and inter-speaker context separately, posing a major issue for lacking enough interaction between them. In this paper, we propose a novel Speaker and Position-Aware Graph neural network model for ERC (S+PAGE), which contains three stages to combine the benefits of both Transformer and relational graph convolution network (R-GCN) for better contextual modeling. Firstly, a two-stream conversational Transformer is presented to extract the coarse self and inter-speaker contextual features for each utterance. Then, a speaker and position-aware conversation graph is constructed, and we propose an enhanced R-GCN model, called PAG, to refine the coarse features guided by a relative positional encoding. Finally, both of the features from the former two stages are input into a conditional random field layer to model the emotion transfer. Extensive experiments demonstrate that our model achieves state-of-the-art performance on three ERC datasets.

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“…This system achieved encouraging results and demonstrated the feasibility of using deep learning methods to extract medication information from raw clinical texts. Several other research papers have proposed BiLSTM with conditional random fields for their NER model [18], [19]. However, this type of approach requires working with words and phrases that have meaning in their sequence.…”

Section: Related Workmentioning

confidence: 99%

Automated Drug-Related Information Extraction from French Clinical Documents: ReLyfe Approach

Alwan¹,

Maayane²,

Rubin³

et al. 2021

Preprint

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Structuring medical data in France remains a challenge mainly because of the lack of medical data due to privacy concerns and the lack of methods and approaches on processing the French language. One of these challenges is structuring drug-related information in French clinical documents. To our knowledge, over the last decade, there are less than five relevant papers that study French prescriptions. This paper proposes a new approach for extracting drug-related information from French clinical scanned documents while preserving patients' privacy. In addition, we deployed our method in a health data management platform where it is used to structure drug medical data and help patients organize their drug schedules. It can be implemented on any web or mobile platform. This work closes the gap between theoretical and practical work by creating an application adapted to real production problems. It is a combination of a rule-based phase and a Deep Learning approach. Finally, numerical results show the outperformance and relevance of the proposed methodology.

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Neural Architectures for Named Entity Recognition

Cited by 239 publications

References 0 publications

Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

Automated Drug-Related Information Extraction from French Clinical Documents: ReLyfe Approach

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