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Found 12 papers, 3 papers with code
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs.
Relation Extraction with Self-determined Graph Convolutional Network
The state-of-the-art methods use linguistic tools to build a graph for the text in which the entities appear and then a Graph Convolutional Network (GCN) is employed to encode the pre-built graphs.
Autoencoding Keyword Correlation Graph for Document Clustering
The graph is constructed with topical keywords as nodes and multiple local and global features as edges.
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies.
Siamese Neural Networks with Random Forest for detecting duplicate question pairs
We got the best result by using the Siamese adaptation of a Bidirectional GRU with a Random Forest classifier, which landed us among the top 24% in the competition Quora Question Pairs hosted on Kaggle.
Investigating how well contextual features are captured by bi-directional recurrent neural network models
Our experiments focus on important contextual words as features, which can easily be extended to analyze various other feature types.
What matters in a transferable neural network model for relation classification in the biomedical domain?
We systematically investigate effectiveness of the proposed frameworks in transferring the knowledge under multiple aspects related to source and target tasks, such as, similarity or relatedness between source and target tasks, and size of training data for source task.
Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition
Three important characteristics of the framework are as follows - (1) model learns contextual as well as morphological features using two different BLSTM in hierarchy, (2) model uses first order linear conditional random field (CRF) in its output layer in cascade of BLSTM to infer label or tag sequence, (3) model does not use any domain specific features or dictionary, i. e., in another words, same set of features are used in the three NER tasks, namely, disease name recognition (Disease NER), drug name recognition (Drug NER) and clinical entity recognition (Clinical NER).
Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
Event trigger identification is an important first step in all event extraction methods.
Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
The two models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the output of Bi-LSTM layer to assign weights to features.
Relation extraction from clinical texts using domain invariant convolutional neural network
We evaluate performance of the proposed model on i2b2-2010 clinical relation extraction challenge dataset.
Recurrent neural network models for disease name recognition using domain invariant features
In particular, we propose various end-to-end recurrent neural network (RNN) models for the tasks of disease name recognition and their classification into four pre-defined categories.
