End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

Ma, Xuezhe; Hovy, Eduard

doi:10.48550/arxiv.1603.01354

Cited by 176 publications

(243 citation statements)

References 28 publications

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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%

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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%

“…This models not only select text spans containing named entities but also extract quality entity representations which can be used as input for NEN. For example in (Ma and Hovy, 2016) authors combine DL bidirectional long short-term memory network and conditional random fields.…”

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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“…We take advantage of the recent advances in Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) [24]. The former has been setting the state-of-the-art in computer vision [19]- [21] while the latter has made some breakthroughs in natural language processing [22], [23], [25]. As such we propose a solution based on each algorithm below.…”

Section: B Deep Learning Modelmentioning

confidence: 99%

Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration

Wu¹,

Alrabeiah²,

Chakrabarti³

et al. 2021

Preprint

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Overcoming the link blockage challenges is essential for enhancing the reliability and latency of millimeter wave (mmWave) and sub-terahertz (sub-THz) communication networks. Previous approaches relied mainly on either (i) multipleconnectivity, which under-utilizes the network resources, or on (ii) the use of out-of-band and non-RF sensors to predict link blockages, which is associated with increased cost and system complexity. In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line-of-sight (LOS) link blockages. The proposed solution utilizes deep neural networks and special patterns of received signal power, that we call pre-blockage wireless signatures to infer future blockages. Specifically, the developed machine learning models attempt to predict: (i) If a future blockage will occur? (ii) When will this blockage happen? (iii) What is the type of the blockage? And (iv) what is the direction of the moving blockage? To evaluate our proposed approach, we build a large-scale real-world dataset comprising nearly 0.5 million data points (mmWave measurements) for both indoor and outdoor blockage scenarios. The results, using this dataset, show that the proposed approach can successfully predict the occurrence of future dynamic blockages with more than 85% accuracy. Further, for the outdoor scenario with highlymobile vehicular blockages, the proposed model can predict the exact time of the future blockage with less than 80ms error for blockages happening within the future 500ms. These results, among others, highlight the promising gains of the proposed proactive blockage prediction solution which could potentially enhance the reliability and latency of future wireless networks.

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“…It is only very recently that a small number of studies have explored deep learning-based approaches [15,28,85,92], which include Long Short Term Memory (LSTM) neural networks [100] and Gated Recurrent Unit (GRU) [43] and a combination of LSTM, Convolution Neural Networks (CNN) [129] and CRFs [103,128,135]. Typically, these algorithms are used with distributed words and character representations called "word embeddings" and "character embeddings, " respectively.…”

Section: Introductionmentioning

confidence: 99%

A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models

Alam¹,

Hasan²,

Alam³

et al. 2021

Preprint

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Bangla -ranked as the 6 𝑡ℎ most widely spoken language across the world, 1 with 230 million native speakersis still considered as a low-resource language in the natural language processing (NLP) community. With three decades of research, Bangla NLP (BNLP) is still lagging behind mainly due to the scarcity of resources and the challenges that come with it. There is sparse work in different areas of BNLP; however, a thorough survey reporting previous work and recent advances is yet to be done. In this study, we first provide a review of Bangla NLP tasks, resources, and tools available to the research community; we benchmark datasets collected from various platforms for nine NLP tasks using current state-of-the-art algorithms (i.e., transformer-based models). We provide comparative results for the studied NLP tasks by comparing monolingual vs. multilingual models of varying sizes. We report our results using both individual and consolidated datasets and provide data splits for future research. We reviewed a total of 108 papers and conducted 175 sets of experiments. Our results show promising performance using transformer-based models while highlighting the trade-off with computational costs. We hope that such a comprehensive survey will motivate the community to build on and further advance the research on Bangla NLP.

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End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

Cited by 176 publications

References 28 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

Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration

A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models

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