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Found 25 papers, 8 papers with code
ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora
We introduce a generic, human-out-of-the-loop pipeline, ERLKG, to perform rapid association analysis of any biomedical entity with other existing entities from a corpora of the same domain.
AmalREC: A Dataset for Relation Extraction and Classification Leveraging Amalgamation of Large Language Models
This study has focused on the following major questions: (i) how to generate sentences from relation tuples, (ii) how to compare and rank them, (iii) can we combine strengths of individual methods and amalgamate them to generate an even bette quality of sentences, and (iv) how to evaluate the final dataset?
Enhancing Event Extraction from Short Stories through Contextualized Prompts
Our objective is to clarify the intricate idea of events in the context of short stories.
A Generative Marker Enhanced End-to-End Framework for Argument Mining
This framework jointly extracts both ACs and ARs from a given argumentative text.
Noise in Relation Classification Dataset TACRED: Characterization and Reduction
Towards the first objective, we analyze predictions and performance of state-of-the-art (SOTA) models to identify the root cause of noise in the dataset.
VQA with Cascade of Self- and Co-Attention Blocks
The use of complex attention modules has improved the performance of the Visual Question Answering (VQA) task.
Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy
The immediate follow-up problem is: Given a specific reannotation budget, which subset of the data should we reannotate?
CL-NERIL: A Cross-Lingual Model for NER in Indian Languages
We employ a variant of the Teacher-Student model and optimize it jointly on the pseudo labels of the Teacher model and predictions on the generated weakly labeled data.
IQ-VQA: Intelligent Visual Question Answering
As a part of the cyclic framework, we propose a novel implication generator which can generate implied questions from any question-answer pair.
CQ-VQA: Visual Question Answering on Categorized Questions
The second level, referred to as answer predictor (AP), comprises of a set of distinct classifiers corresponding to each question category.
Taxonomical hierarchy of canonicalized relations from multiple Knowledge Bases
Further, to address the second question, we canonicalize, filter, and combine the identified relations from the three resources to construct a taxonomical hierarchy.
Unsupervised Representation Learning of DNA Sequences
In this work, we use a sequence-to-sequence autoencoder model to learn a latent representation of a fixed dimension for long and variable length DNA sequences in an unsupervised manner.
Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage
Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports.
Collective Learning From Diverse Datasets for Entity Typing in the Wild
The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets.
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.
Representation learning of drug and disease terms for drug repositioning
Drug repositioning (DR) refers to identification of novel indications for the approved drugs.
Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types.
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.
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.
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.
