Showing 1 - 13 results of 13 for search '(("seventh cognition") OR ("entity recognition"))', query time: 0.10s Refine Results
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    Keyword and named entity recognition on emergency call texts by Hu, Wanyu

    Published 2020
    “…This report utilize one of the methods of Artificial Intelligence (AI), specifically the Named Entity Recognition (NER) technique on Emergency Call Texts. …”
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    Final Year Project (FYP)
  3. 3

    Keyword and named entity recognition on air traffic control text by Tay, Nikole Qiwei

    Published 2020
    “…This report will discuss the application of Named Entity Recognition (NER), a Natural Language Processing (NLP) method on Air Traffic Control (ATC) conversations to improve and attain higher precision in the communication between pilots and air traffic controllers. …”
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    Final Year Project (FYP)
  4. 4

    Generalized AutoNLP model for name entity recognition task by Wong, Yung Shen

    Published 2022
    “…Experiments are conducted to study the performance of our proposed architecture with BERT word embeddings on AutoNLP name entity recognition tasks.…”
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    Final Year Project (FYP)
  5. 5

    Deep learning-based text augmentation for named entity recognition by Surana, Tanmay

    Published 2023
    “…This thesis is focused on the development of an effective text augmentation method for Named Entity Recognition (NER) in the low-resource setting. …”
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    Thesis-Master by Research
  6. 6

    Keyword and named entity recognition on air traffic control (ATC) data by Thia, Jeremy Ming Xuan

    Published 2019
    “…This project will explore several NLP tasks to perform named entity recognition on Air Traffic Control data, to be specific Air Traffic conversations. …”
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    Final Year Project (FYP)
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    Active learning for ontological event extraction incorporating named entity recognition and unknown word handling by Han, Xu, Kim, Jung-jae, Kwoh, Chee Keong

    Published 2016
    “…Previous works of active learning, however, focused on the tasks of entity recognition and protein-protein interactions, but not on event extraction tasks for multiple event types. …”
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    Journal Article
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    Machine learning for NTU canteen review analysis and recommendation by Nguyen, Duy Khanh

    Published 2022
    “…Therefore, this report experiments with Deep Neural Networks, utilizing 2 pretrained models namely T5 and Bart to handle the Food Named Entity Recognition task.…”
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    Final Year Project (FYP)
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    Active learning with applications in biomedical document annotation by Han, Xu

    Published 2017
    “…In addi- tion, the adaptation of the active learning method into named entity recognition tasks also improves the document selection for manual annotation of named entities. …”
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    Thesis
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    Converting a text mining system into a UIMA framework by Choo, Zhen Ying.

    Published 2013
    “…These parts include converting a Named Entity Recognition (NER) module and parsing the input sentences to obtain their semantic structures, as well as a Pattern Matching module to identify the relations between the named entities. …”
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    Final Year Project (FYP)
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    Development of a system to analyze fake news by Sankpal, Shibani Prashant

    Published 2024
    “…The analysis of the fake news articles in the web application has also expanded with the inclusion of sentiment analysis, text summarization and Named Entity Recognition. The inclusion of sentiment analysis would help the user discern whether the article conveys positivity, negativity, or neutrality, providing insight into the potential intent of the article.…”
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    Final Year Project (FYP)
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    Lightweight transformers for clinical natural language processing by Rohanian, O, Nouriborji, M, Jauncey, H, Kouchaki, S, Nooralahzadeh, F, Clifton, L, Merson, L, Clifton, DA

    Published 2024
    “…Our extensive evaluation was done across several standard datasets and covered a wide range of clinical text-mining tasks, including natural language inference, relation extraction, named entity recognition and sequence classification. To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks. …”
    Journal article
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