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

Abdullah, Mohd Sohaimi; Aziz, Norshakirah; Abdulkadir, Said Jadid; Alhussian, Hitham; Talpur, Noureen

doi:10.1109/access.2023.3240898

Cited by 11 publications

(4 citation statements)

References 178 publications

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“…In [33,34], they focus mainly on methods, applications, trends and challenges in extracting information from textual data. The research discusses the applications of information extraction in different domains and the challenges faced.…”

Section: Application Of Knowledge Graphsmentioning

confidence: 99%

“…The traditional edit distance cannot determine the degree of similarity at the semantic level, which is particularly important for the semantics of domain KGs, as it can only determine the degree of match of string literal d by measuring the distance of the string. For this reason, we have introduced Word2vec [34] for unsupervised learning in the traditional editing distance, in which word vectors are trained for specialized domains.…”

Section: Calculation Of Similarity Based On Improved Edit Distancementioning

confidence: 99%

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Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance

Lou,

Yu,

Jiang

et al. 2023

Mathematics

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Under the background of intelligent manufacturing, industrial systems are developing in a more complex and intelligent direction. Equipment maintenance management is facing significant challenges in terms of maintenance workload, system reliability and stability requirements and the overall skill requirements of maintenance personnel. Equipment maintenance management is also developing in the direction of intellectualization. It is important to have a method to construct a domain knowledge graph and to organize and utilize it. As is well known, traditional equipment maintenance is mainly dependent on technicians, and they are required to be very familiar with the maintenance manuals. But it is very difficult to manage and exploit a large quantity of knowledge for technicians in a short time. Hence a method to construct a knowledge graph (KG) for equipment maintenance is proposed to extract knowledge from manuals, and an effective maintenance scheme is obtained with this knowledge graph. Firstly, a joint model based on an enhanced BERT-Bi-LSTM-CRF is put forward to extract knowledge automatically, and a Cosine and Inverse Document Frequency (IDF) based on semantic similarity a presented to eliminate redundancy in the process of the knowledge fusion. Finally, a Decision Support System (DSS) for equipment maintenance is developed and implemented, in which knowledge can be extracted automatically and provide an equipment maintenance scheme according to the requirements. The experimental results show that the joint model used in this paper performs well on Chinese text related to equipment maintenance, with an F1 score of 0.847. The quality of the knowledge graph constructed after eliminating redundancy is also significantly improved.

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Section: Application Of Knowledge Graphsmentioning

confidence: 99%

Section: Calculation Of Similarity Based On Improved Edit Distancementioning

confidence: 99%

Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance

Lou,

Yu,

Jiang

et al. 2023

Mathematics

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“…These include languages with small speaking populations and minimal written data, a language widely used but rarely discussed in NLP research, and domains with limited training data [14]. VOLUME XX, 2017 Previous systematic studies [1], [3], [7], [15] explained NER, RE, and EE for information extraction on text data but did not include SRL. We also found only a few systematic studies that addressed SRL for IE processes, where Wang et al [16] described SRL specifically for Chinese, and Ariyanto et al [2] discussed SRL specifically for Indonesian.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Ariyanto,

Purwitasari,

Fatichah

2024

IEEE Access

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Challenges in the big data phenomenon arise due to the existence of unstructured text data, which is very large, comes from various sources, has various formats, and contains much noise. The complexity of unstructured text data makes it difficult to extract useful information. Therefore, a process is needed to transform it into structured data to be processed further. The information Extraction (IE) process helps to extract relationships, entities, semantic roles, and events from unstructured text data by converting them into structured output. One of IE's tasks is Semantic Role Labeling (SRL), which has a crucial function in identifying semantic roles in a sentence so that it can enrich the understanding of the text. However, much of SRL development focuses on high-resource data, especially in English. The limited development of SRL in specific low-resource languages or domains is a complex challenge. This research aims to conduct a systematic study on the development of SRL for low-resource data, both in low-resource language or domainspecific contexts. The review process was carried out systematically using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, and 54 quality papers were obtained from the filtering process (from 2018 to 2023). We review several essential points, including (1) datasets that are often used for SRL tasks and their labeling strategies for low-resource data, (2) methods that have currently been developed for SRL tasks and learning scenarios when dealing with low-resource data, (4) evaluation metrics, (5) application of SRL tasks. This review is complemented by a discussion of issues and potential solutions for developing SRL on low-resource data to help researchers develop SRL more effectively in dealing with the challenges faced with low-resource data.

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“…By harnessing advanced analytical techniques and machine learning algorithms [2], predictive modeling can tap into this wealth of information to foresee future trends, consumer behaviors, and emerging patterns [3]. Businesses can adapt their strategies based on predictive insights gleaned from social media data, enabling them to make informed decisions [4], anticipate market shifts, and tailor offerings to customer preferences [5]. Navigating the data-rich realm of social media, its significance in predictive modeling goes beyond its sheer volume.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review on Social Media Slang Analytics in Contemporary Discourse

Sundaram,

Subramaniam,

Hamid

et al. 2023

IEEE Access

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Social media slang, encompassing informal language, words, phrases, and acronyms on digital platforms, reflects the dynamic nature of online communication. Analyzing social media slang offers valuable insights for organizations and researchers, enabling a deeper understanding of communication trends, sentiment analysis, and user behavior in the digital sphere. It plays a pivotal role in shaping effective marketing strategies and enhancing communication, ultimately facilitating informed decision-making in the digital age. In our study, we conducted a systematic review of research articles from the Web of Science and Scopus databases, spanning the years 2016 to 2023. Our rigorous selection process, based on quality assessments as per PRISMA guidelines, revealed several following key findings. Social media slang exhibits a remarkable adaptability to different platforms, mirroring the communication styles and user cultures found on each. Notably, it influences user behavior, impacting interactions, content engagement, and decisionmaking, particularly in marketing and communication strategies. Furthermore, our research highlights the value of social media slang in sentiment analysis, providing insights into public sentiment and supporting well-informed decision-making. Our study underscores the versatile applications of slang analytics across various industries and research domains, emphasizing its pivotal role in providing specialized insights and enhancing communication strategies. In conclusion, our research offers a comprehensive understanding of the dynamic landscape of informal language in the context of contemporary digital communication, furnishing valuable insights that inform decision-making, refine marketing strategies, and enhance communication.INDEX TERMS informal language, internet slang, language cognition, slang analytics and social media. I.

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

Cited by 11 publications

References 178 publications

Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance

Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

A Systematic Literature Review on Social Media Slang Analytics in Contemporary Discourse

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