Efficient access to chemical information contained in scientific literature, patents, technical
reports, or the web is a pressing need shared by researchers and patent attorneys from …

Introduction e term “Named Entity”, now widely used in Natural Language Processing, was
coined for the Sixth Message Understanding Conference (MUC-6)(R. Grishman & Sundheim …

Named Entity Recognition (NER) is essential for some Natural Language Processing (NLP)
tasks. Previous researchers gave a survey of NER in statistical machine learning era …

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 …

Combining data content with visual embellishments, infographics can effectively deliver
messages in an engaging and memorable manner. Various authoring tools have been …

In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the
context of information retrieval (IR). Modern IR systems can benefit from information …

In the menagerie of tasks for information extraction, entity linking is a new beast that has
drawn a lot of attention from NLP practitioners and researchers recently. Entity Linking, also …

Eliciting user needs for individual components and features of a product or a service on a
large scale is a key requirement for innovative design. Synthesizing data as an initial …

Recently, neural models have shown superior performance over conventional models in
NER tasks. These models use CNN to extract sub-word information along with RNN to …

Named Entity Recognition (NER) aims to classify each word of a document into predefined
target named entity classes and is now-a-days considered to be fundamental for many …