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Found 16 papers, 5 papers with code
Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods
Information Extraction from scientific literature can be challenging due to the highly specialised nature of such text.
An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification
Non-hierarchical sparse attention Transformer-based models, such as Longformer and Big Bird, are popular approaches to working with long documents.
Tensorial tomographic differential phase-contrast microscopy
We report Tensorial Tomographic Differential Phase-Contrast microscopy (T2DPC), a quantitative label-free tomographic imaging method for simultaneous measurement of phase and anisotropy.
Revisiting Transformer-based Models for Long Document Classification
The recent literature in text classification is biased towards short text sequences (e. g., sentences or paragraphs).
MDAPT: Multilingual Domain Adaptive Pretraining in a Single Model
Domain adaptive pretraining, i. e. the continued unsupervised pretraining of a language model on domain-specific text, improves the modelling of text for downstream tasks within the domain.
Recognising Biomedical Names: Challenges and Solutions
However, there are several open challenges of applying these models to recognise biomedical names: 1) Biomedical names may contain complex inner structure (discontinuity and overlapping) which cannot be recognised using standard sequence tagging technique; 2) The training of NER models usually requires large amount of labelled data, which are difficult to obtain in the biomedical domain; and, 3) Commonly used language representation models are pre-trained on generic data; a domain shift therefore exists between these models and target biomedical data.
NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction
The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks.
An Analysis of Simple Data Augmentation for Named Entity Recognition
Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks.
Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media
Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data.
Ranked #3 on
Clinical Concept Extraction
on 2010 i2b2/VA
An Effective Transition-based Model for Discontinuous NER
Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans.
NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks.
Using Similarity Measures to Select Pretraining Data for NER
Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks.
Ranked #1 on
Named Entity Recognition (NER)
on WetLab
Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection
Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine.
Recognizing Complex Entity Mentions: A Review and Future Directions
Standard named entity recognizers can effectively recognize entity mentions that consist of contiguous tokens and do not overlap with each other.
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding.
