The increasing volume of user-generated content on the web has made sentiment analysis
an important tool for the extraction of information about the human emotional state. A current …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

We present simple BERT-based models for relation extraction and semantic role labeling. In
recent years, state-of-the-art performance has been achieved using neural models by …

Deep learning has improved performance on many natural language processing (NLP)
tasks individually. However, general NLP models cannot emerge within a paradigm that …

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a
better understanding of the human language for linguistic-based human-computer …

Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a
sentence. It is typically regarded as an important step in the standard NLP pipeline. As the …

Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no
explicit linguistic features. However, prior work has shown that gold syntax trees can …

We introduce a new deep learning model for semantic role labeling (SRL) that significantly
improves the state of the art, along with detailed analyses to reveal its strengths and …

Abstract Semantic Role Labeling (SRL) is believed to be a crucial step towards natural
language understanding and has been widely studied. Recent years, end-to-end SRL with …

In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity
and provide state-of-the-art performance in a wide variety of tasks, such as machine …