Recent Progress of Named Entity Recognition over the Most Popular Datasets

Albared, Mohammed; Ocaña, Marc Gallofré; Ghareb, Abdullah Saeed; Al-Moslmi, Tareq

doi:10.1109/icoice48418.2019.9035170

Cited by 5 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, studies [6] and [11] have concentrated on summarizing studies related to IE techniques for diverse data types (i.e., text, images, audio, and video). Studies [21], [22], and [26] focused on NER techniques only, whereas Akkasi and Moens [24] focused on relation extraction (RE) techniques, and Xiang and Wang [23] focused on event extraction (EE) techniques. Based on the literature, only a few survey studies [23], [24], [25] have implemented DL-based methods for extracting useful information from text.…”

Section: Literature Reviewmentioning

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: Literature Reviewmentioning

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

show abstract

“…Finally, feature extraction has been approached using Named Entity Recognition (NER) [41][42][43]. Although NER is not strictly a topic modeling method, it allows extracting crucial features-named entities.…”

mentioning

confidence: 99%

Applying Named Entity Recognition and Graph Networks to Extract Common Interests from Thematic Subfora on Reddit

Sawicki,

Ganzha,

Paprzycki

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph networks representing online social networks. However, large-scale studies that take into account Reddit’s unique structure are scarce. In this contribution, similarity between subreddits is explored. Specifically, subreddit posts (from 3189 subreddits, spanning the year 2022) are processed using NER to build graph networks which are further mined for relations between subreddits. The evaluation of obtained results follows the state-of-the-art approaches used for a similar problem, i.e., recommender system metrics, and applies recall and AUC. Overall, the use of Reddit crossposts discloses previously unknown relations between subreddits. Interestingly, the proposed approach may allow for researchers to better connect their study topics with particular subreddits and shows promise for subreddit similarity mining.

show abstract