A survey of textual emotion recognition and its challenges
Textual language is the most natural carrier of human emotion. In natural language
processing, textual emotion recognition (TER) has become an important topic due to its …
processing, textual emotion recognition (TER) has become an important topic due to its …
Review of graph neural network in text classification
Text classification is one of the fundamental problems in Natural Language Processing
(NLP). Several research studies have used deep learning approaches such as Convolution …
(NLP). Several research studies have used deep learning approaches such as Convolution …
Nyströmformer: A nyström-based algorithm for approximating self-attention
Transformers have emerged as a powerful tool for a broad range of natural language
processing tasks. A key component that drives the impressive performance of Transformers …
processing tasks. A key component that drives the impressive performance of Transformers …
[图书][B] Pretrained transformers for text ranking: Bert and beyond
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …
response to a query. Although the most common formulation of text ranking is search …
Clear: Contrastive learning for sentence representation
Pre-trained language models have proven their unique powers in capturing implicit
language features. However, most pre-training approaches focus on the word-level training …
language features. However, most pre-training approaches focus on the word-level training …
A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification
A Onan, MA Toçoğlu - Ieee Access, 2021 - ieeexplore.ieee.org
Sarcasm identification on text documents is one of the most challenging tasks in natural
language processing (NLP), has become an essential research direction, due to its …
language processing (NLP), has become an essential research direction, due to its …
Graph convolutional networks for text classification
Text classification is an important and classical problem in natural language processing.
There have been a number of studies that applied convolutional neural networks …
There have been a number of studies that applied convolutional neural networks …
Be more with less: Hypergraph attention networks for inductive text classification
Text classification is a critical research topic with broad applications in natural language
processing. Recently, graph neural networks (GNNs) have received increasing attention in …
processing. Recently, graph neural networks (GNNs) have received increasing attention in …
Every document owns its structure: Inductive text classification via graph neural networks
Text classification is fundamental in natural language processing (NLP), and Graph Neural
Networks (GNN) are recently applied in this task. However, the existing graph-based works …
Networks (GNN) are recently applied in this task. However, the existing graph-based works …
Estimating training data influence by tracing gradient descent
We introduce a method called TracIn that computes the influence of a training example on a
prediction made by the model. The idea is to trace how the loss on the test point changes …
prediction made by the model. The idea is to trace how the loss on the test point changes …