Named Entity Recognition over FBNER: A New Facebook Dataset in Turkish

Ooban, Onder; Özel, Selma Ayşe; İnan, Ali

doi:10.1109/asyu52992.2021.9598971

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, the feature-based NER model combines the rule engineering of the RBM with the learning capabilities of the LBM to identify specific features from the text. This NER model is designed to identify lexical, morphological, gazetteer lookup, orthographical, POS, contextual, conjunction, and word-embedding features [58], [62]. It results in a model that learns the specific features based on a predefined set of rules.…”

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges

et al. 2023

View full text Add to dashboard Cite

Information extraction (IE) is a challenging task, particularly when dealing with highly heterogeneous data. State-of-the-art data mining technologies struggle to process information from textual data. Therefore, various IE techniques have been developed to enable the use of IE for textual data. However, each technique differs from one another because it is designed for different data types and has different target information to be extracted. This study investigated and described the most contemporary methods for extracting information from textual data, emphasizing their benefits and shortcomings. To provide a holistic view of the domain, this comprehensive systematic literature review employed a systematic mapping process to summarize studies published in the last six years (from 2017 to 2022). It covers fundamental concepts, recent approaches, applications, and trends, in addition to challenges and future research prospects in this domain area. Based on an analysis of 161 selected studies, we found that the state-of-the-art models employ deep learning to extract information from textual data. Finally, this study aimed to guide novice and experienced researchers in future research and serve as a foundation for this research area.

show abstract

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%