A Machine Learning Approach to Information Extraction

Téllez-Valero, Alberto; Montes-y-Gómez, Manuel; Villaseñor-Pineda, Luis

doi:10.1007/978-3-540-30586-6_58

Cited by 14 publications

(9 citation statements)

References 5 publications

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“…Furthermore, the recent advancement of AI has enabled the exploration of machine learning techniques in improving disaster risk communication (Ogie et al , 2018). For example, the prediction and monitoring for early disaster warning (Danso-Amoako et al , 2012), and information extraction and classification for situational awareness: harnessing social media message (Fersini et al , 2017) and online disaster news report (Téllez-Valero et al , 2005). On the other hand, AI technology can also bring challenges in disaster risk communication – e.g.…”

Section: Resultsmentioning

confidence: 99%

Risk, crisis and disaster management in hospitality and tourism: a comparative review

Ritchie

Jiang

2021

IJCHM

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Purpose This paper aims to summarize the current state of research on risk, crisis and disaster management in the generic field, and in tourism and hospitality. It identifies key themes and compares the main topics studied in both the tourism and hospitality management and marketing literature. Design/methodology/approach A narrative (thematic) review and synthesis was completed based on articles published in the top 20 tourism and hospitality management journals from 2011 to March 2021. A review was conducted of the generic literature from 2016 to 2020. Findings From 210 papers reviewed, only 47 are in the hospitality field. The authors found that 80% of papers were empirical with slightly more quantitative papers produced. The majority of the papers focused on crises. Three key themes were found from the review and future research proposed to address gaps based on these findings and a review of 26 papers from the generic risk, crisis and disaster management field. Practical implications Research is required into planning and preparedness, not just response and recovery to crises and disasters. Future research should consider hospitality rather than tourism, particularly focusing attention outside of the accommodation sector. Hospitality studies also need to go beyond the micro-organizational level to include more meso- and macro-level studies. Originality/value The review provides a number of future research directions for tourism and hospitality research in the field. The paper provides a comprehensive multi-dimensional framework to synthesize studies and identifies research gaps. It also provides recommendations on methodologies required to progress these research directions. Research in this field is likely to grow because of the impact of COVID-19.

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Section: Resultsmentioning

confidence: 99%

Risk, crisis and disaster management in hospitality and tourism: a comparative review

Ritchie

Jiang

2021

IJCHM

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“…Previous studies that focus on the process of extracting content from papers can be broadly classified based on the data cutting levels and data extraction methods, which are representative methods for the cutting of data. There are phrase-level analysis methods, such as conditional random fields (CRFs) and support vector machines (SVMs), and sentence-level analysis methods that use machine learning algorithms, such as Bayesian classifiers and SVMs [26]. Although old-level analysis has not been studied due to the lack of datasets until recently, rule-based algorithms and CRF methods have been developed recently.…”

Section: Background a Structures Of Scientific Papers By Sectionmentioning

confidence: 99%

Development of an Embedding Framework for Clustering Scientific Papers

Kim

Lee²,

Yoon³

2022

IEEE Access

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In this era, research and development are becoming a continuous and accelerating process because technology changes rapidly with a short lifecycle. As a result, various methodologies are being developed to monitor these rapidly changing research trends; In particular, clustering method-related studies in science and technology documents are being developed with a variety of approaches. However, previous studies on document clustering methods focus on a specific field or language but do not take into consideration certain important pieces of information in science and technology documents. Therefore, this study proposes an embedding methodology that uses important content from scientific and technical documents. We took into consideration the importance of information containing core structures in science and technology documents and proposed a clustering methodology that analyzes structured and unstructured data, such as textual information, author information, and citation information. The proposed method combines both textual and structural data from the paper, using a method that focuses on screening important information by sections in science and technology documents. Then, Girvan-Newman clustering and Louvain clustering models are applied to generate embedding vectors and show evaluation results through the clustering indices. As a practical example, we applied the proposed methodology using paper data from the field of hydrogen cell vehicles. The results of this study will be effective in identifying gaps in technology for new technological development, identifying technology trends, and presenting directional information for future technology development.INDEX TERMS Clustering method, data mining, text mining, text analysis, scientific publishing, fuel cells.

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“…For this reason, IE is concerned with structuring all the relevant information from any given source. In other words, the goal of an IE system is to find and link the relevant information while ignoring the extraneous and irrelevant one [17]. Since a lot of today's information is available in natural language, an unstructured format, IE can help structuring the free-text information in a way that can be used by other tasks to mine knowledge out of it [18].…”

Section: Named-entity Recognitionmentioning

confidence: 99%

Recognition of genetic mutations in text using deep learning

Matos

2018

Proceedings of the First International Conference on Data Science, E-Learning and Information Systems

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Deep learning is a sub-area of automatic learning that attempts to model complex structures in the data through the application of different neural network architectures with multiple layers of processing. These methods have been successfully applied in areas ranging from image recognition and classification, natural language processing, and bioinformatics. In this work we intend to create methods for named-entity recognition (NER) in text using techniques of deep learning in order to identify genetic mutations.

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A Machine Learning Approach to Information Extraction

Cited by 14 publications

References 5 publications

Risk, crisis and disaster management in hospitality and tourism: a comparative review

Risk, crisis and disaster management in hospitality and tourism: a comparative review

Development of an Embedding Framework for Clustering Scientific Papers

Recognition of genetic mutations in text using deep learning

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