Named entity recognition with bidirectional LSTM-CNNs
JPC Chiu, E Nichols - … of the association for computational linguistics, 2016 - direct.mit.edu
Named entity recognition is a challenging task that has traditionally required large amounts
of knowledge in the form of feature engineering and lexicons to achieve high performance …
of knowledge in the form of feature engineering and lexicons to achieve high performance …
Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss
Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful
for various NLP sequence modeling tasks, but little is known about their reliance to input …
for various NLP sequence modeling tasks, but little is known about their reliance to input …
De-identification of patient notes with recurrent neural networks
Objective: Patient notes in electronic health records (EHRs) may contain critical information
for medical investigations. However, the vast majority of medical investigators can only …
for medical investigations. However, the vast majority of medical investigators can only …
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial
neural networks (ANNs) have recently been shown to outperform existing NER systems …
neural networks (ANNs) have recently been shown to outperform existing NER systems …
Exploiting linguistic resources for neural machine translation using multi-task learning
Linguistic resources such as part-of-speech (POS) tags have been extensively used in
statistical machine translation (SMT) frameworks and have yielded better performances …
statistical machine translation (SMT) frameworks and have yielded better performances …
Recurrent neural network models for disease name recognition using domain invariant features
Hand-crafted features based on linguistic and domain-knowledge play crucial role in
determining the performance of disease name recognition systems. Such methods are …
determining the performance of disease name recognition systems. Such methods are …
Don't throw those morphological analyzers away just yet: Neural morphological disambiguation for Arabic
This paper presents a model for Arabic morphological disambiguation based on Recurrent
Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several …
Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several …
The role of" condition" a novel scientific knowledge graph representation and construction model
Conditions play an essential role in scientific observations, hypotheses, and statements.
Unfortunately, existing scientific knowledge graphs (SciKGs) represent factual knowledge as …
Unfortunately, existing scientific knowledge graphs (SciKGs) represent factual knowledge as …
A neural machine translation model for arabic dialects that utilises multitask learning (mtl)
In this research article, we study the problem of employing a neural machine translation
model to translate Arabic dialects to modern standard Arabic. The proposed solution of the …
model to translate Arabic dialects to modern standard Arabic. The proposed solution of the …
Recent trends in named entity recognition (ner)
A Roy - arXiv preprint arXiv:2101.11420, 2021 - arxiv.org
The availability of large amounts of computer-readable textual data and hardware that can
process the data has shifted the focus of knowledge projects towards deep learning …
process the data has shifted the focus of knowledge projects towards deep learning …