Modelings and techniques in named entity recognition: an information extraction task

Kanya, N.; Ravi, T.

doi:10.1049/cp.2012.2199

Cited by 14 publications

(6 citation statements)

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“…The COVID-19 information from web pages does not include contents in paragraph tags (<p>). These contents could be included in the COVID-19 ontology schema and instances using Named Entity Recognition (NER) [19] and Hearst Patterns [20]. NER and Hearst Patterns are combined to define taxonomy or superclass/subclass relationship residing in the contents of paragraph tags.…”

Section: Discussionmentioning

confidence: 99%

Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

Wiangnak,

Trongratsameethong

2023

ECTI-CIT Transactions

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Gathering information from multiple data sources takes a long time to collect, analyze, and classify. Furthermore, if the data sources have different data structures, the merged data structure must support such heterogeneity. In addition, semantic of data must also be considered. This paper proposes automated knowledge integration from heterogeneous data sources using ontology engineering combined with text analytics. Text stemming is used to preprocess data. Part-of-speech (POS) tagging, Universal Dependencies (UD), and text similarity measurement called cosine similarity are used to analyze and integrate data. Our work focuses on five COVID- 19 knowledge scopes: COVID-19, coronaviruses, diseases, pandemics, and vaccines. For evaluation, six ontologies were constructed with six different cosine similarity values ranging from 0.5 to 1.0. Each constructed ontology has COVID-19 related and non-COVID-19 data in a ratio of 70 to 30. The six constructed ontologies were evaluated for consistency with the original data. Using cosine similarity with 0.6, precision, recall, and F1-score are 0.82, 0.71, and 0.76, respectively, and the constructed ontology is optimal, containing the highest amount of relevant COVID-19 information for this case study.

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Section: Discussionmentioning

confidence: 99%

Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

Wiangnak,

Trongratsameethong

2023

ECTI-CIT Transactions

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“…Time, accuracy, and performing quality are among the many criteria used to evaluate different NER tools. NER could make use of a selection of learning strategies, with but not limited to semi-supervised learning, supervised learning, and unsupervised learning [20]. In the various forms, these methods all have significant advantages.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

Named Entity Recognition of Kumauni Language using Machine Learning (ML)

Pant¹,

Sharma²,

Kundu

2023

Preprint

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Communication between humans is impossible without the use of language. Natural Language Processing (NLP) is a technique that is used so that computers can comprehend various natural languages. Named Entity Recognition (NER) is a subtask of information extraction that aims to discover and categorize the components in given text into pre-defined categories. NER is an abbreviation for the phrase ”named entity recognition”. Machine translation, question-answering systems, and automatic summarization are exam- ples of the types of NLP tasks that can benefit from NER. The purpose of this research is to investigate whether or if it is possible to create a chatbot that converses in the Kumaon language, as well as any poten- tial difficulties that could arise in the process. In addition to this, the authors provide an in-depth exami- nation of Kumaoni as well as a mapping of the language into other languages to make its use in industrial processing more accessible.

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“…and it entails a little amount of supervision, like a collection of seeds, to initiate the learning process. In [7], the authors have proposed a bootstrapping approach combined with CRF to accomplish NER.…”

Section: ) Supervised Learning Approachesmentioning

confidence: 99%

A Review on Automatic Person Attribute Information Extraction and Disambiguation from Unstructured text

et al. 2023

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Entity attribute extraction is the process of identifying and extracting attributes, or characteristics, of entities from a given text. The objective is to create a model that can automatically perform person-attribute information extraction from unstructured text. Entity attribute extraction's primary goal is to locate and extract attributes of entities from a supplied text. As a result, information from the unstructured text may now be represented in a structured way. By extracting attributes of entities, a computer program can gain a better understanding of the information contained in the text and can use this information for various purposes such as building a knowledge base or for information retrieval. In this way, entity attribute extraction can help to improve the ability of computer programs to process and understand natural language text. All the essential tools and algorithms are researched and discussed in this paper. This study is divided into two main sections that explore published works and modern tools and technologies working in the field of Entity attribute extraction. It also identifies critical research gaps in the literature under assessment. The gap analysis reveals potential for improved textual event prediction algorithms in the future.

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Modelings and techniques in named entity recognition: an information extraction task

Cited by 14 publications

References 0 publications

Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

Named Entity Recognition of Kumauni Language using Machine Learning (ML)

A Review on Automatic Person Attribute Information Extraction and Disambiguation from Unstructured text

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