QA4IE: A Question Answering Based System for Document-Level General Information Extraction

Qiu, Lin; Ru, Dongyu; Long, Quanyu; Zhang, Weinan; Yu, Yong

doi:10.1109/access.2020.2970119

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Assuming a factor F 1, thus, the approximation factor k → 1, and therefore, the BPA of the active rule R i will tend m Rj → 1; whereas, the BPA of the inactive rules will tend to zero. As a result, R i * w Ri + U = R i * w Ri + 1 − R i * w Ri , which equals to one, satisfying, thus, Equation (10).…”

Section: ) Uncertainty Quantification Of the Knowledge Rulesmentioning

confidence: 87%

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Piolo

Tahasanul

et al. 2022

IEEE Access

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Operational knowledge is one of the most valuable assets in a company, as it provides a strategic advantage over competitors and ensures steady and optimal operation in machines. An (interactive) assessment system on the shop floor can optimize the process and reduce stopovers because it can provide constant valuable information regarding the machine condition to the operators. However, formalizing operational (tacit) knowledge to explicit knowledge is not an easy task. This transformation considers modeling expert knowledge, quantification of knowledge uncertainty, and validation of the acquired knowledge. This study proposes a novel approach for production assessment using a knowledge transfer framework and evidence theory to address the aforementioned challenges. The main contribution of this paper is a methodology for the formalization of tacit knowledge based on an extended failure mode and effect analysis for knowledge extraction, as well as the use of evidence theory for the uncertainty definition of knowledge. Moreover, this approach uses primitive recursive functions for knowledge modeling and proposes a validation strategy of the knowledge using machine data. These elements are integrated into an interactive recommendation system hosted on a backend that uses HoloLens as a visual interface. We demonstrate this approach using an industrial setup: a laboratory bulk good system. The results yield interesting insights, including the knowledge validation, uncertainty behavior of knowledge, and interactive troubleshooting for the machine operator.

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Section: ) Uncertainty Quantification Of the Knowledge Rulesmentioning

confidence: 87%

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Piolo

Tahasanul

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To summarize, IE techniques have been successfully implemented in various tasks in the real world, including the extraction of cause-effect relationships [24], event detection and extraction [139], [144], corpus extraction from text [39], [40], [75], KG construction, question answering [36], [99], and mining social media data [148], [150], [157]. The mechanisms of IE from textual data are continually developing as more research is being conducted to address the existing problems within each technique.…”

Section: ) Challenges Based On Rq3: Issues Related To Future Applicat...mentioning

confidence: 99%

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

et al. 2023

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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.

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Generating Biomedical Question Answering Corpora From Q&A Forums

2020

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Question Answering (QA) is a natural language processing task that aims at obtaining relevant answers to user questions. While some progress has been made in this area, biomedical questions are still a challenge to most QA approaches, due to the complexity of the domain and limited availability of training sets. We present a method to automatically extract question-article pairs from Q&A web forums, which can be used for document retrieval, a crucial step of most QA systems. The proposed framework extracts from selected forums the questions and the respective answers that contain citations. This way, QA systems based on document retrieval can be developed and evaluated using the question-article pairs annotated by users of these forums. We generated the BiQA corpus by applying our framework to three forums, obtaining 7,453 questions and 14,239 question-article pairs. We evaluated how the number of articles associated with each question and the number of votes on each answer affects the performance of baseline document retrieval approaches. Also, we demonstrated that the articles given as answers are significantly similar to the questions and trained a state-of-the-art deep learning model that obtained similar performance to using a dataset manually annotated by experts. The proposed framework can be used to update the BiQA corpus from the same forums as new posts are made, and from other forums that support their answers with documents. The BiQA corpus and the framework used to generate it are available at https://github.com/lasigeBioTM/BiQA.

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QA4IE: A Question Answering Based System for Document-Level General Information Extraction

Cited by 4 publications

References 29 publications

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

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

Generating Biomedical Question Answering Corpora From Q&A Forums

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