Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

Automatic text summarization is one of the most promising solutions to the ever-growing
challenges of textual data as it produces a shorter version of the original document with …

Abstract Named Entity Recognition (NER) is a natural language processing (NLP) task that
involves assigning labels like Person, Location, and Organization to words in text. While …

Named entities recognition (NER) has become, over time, a potentially helpful pre-
processing for several NLP tasks. It mainly identifies and classifies entities in the text into …

As stated in the no-free-lunch (NFL) theory, there is not any optimizer suitable for all
problems. This circumstance becomes the motivation of introducing a new swarm-based …

The large volume of court decision documents in Indonesia poses a challenge for
researchers to assist legal practitioners in extracting useful information from the documents …

Stop-words are words having the highest frequencies in a document without any significant
information. They are characterized by having common relations within a cluster. They are …

Named entity recognition is an essential task for various applications related to natural
language processing (NLP). It aims to retrieve a variety of named entities (NEs) from text and …

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is
proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the …

Abstract Named Entity Recognition (NER) is a vitally important task of Natural Language
Processing (NLP), which aims at finding named entities in natural language text and …