CDS-TNMT-Slide v3.pptx

30 May 2023
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
CDS-TNMT-Slide v3.pptx
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CDS-TNMT-Slide v3.pptx

Notes de l'éditeur

  1. Good afternoon. I am Nguyen Ngoc Vu, from Vietnam National University in Hanoi. Today, I am here to present our paper with title “A Character-level Deep Lifelong Learning Model for Named Entity Recognition in Vietnamese Text”
  2. Two next slides, I show detail about NER problems for Vietnamese text. Named entities (NEs) are phrases and they contain: person (PER), organization (ORG), location (LOC), times and quantities, monetary values, percentages, etc. NER is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction (since 1990s). Vietnamese Language and Speech Processing (VLSP) community has organized an evaluation campaign to systematically compare NER systems for Vietnamese language (2016, 2018) based on the ability to recognize NEs types: PER, ORG, LOC, and MISC. The NER systems are using some state-of-art methods to solve NER problems for Vietnamese text with high accurate. Some neural network models proposed for NER tasks: J. P. C. Chiu and E. Nichols [3], Z. Huang et al. [6], G. Lample et al. [8], X. Ma and E. H. Hovy [10], and these models are competitive with traditional models. However, there is a little of studies on LML based on deep neural networks for Named Entity Recognition (NER), especial in Vietnamese.
  3. Two next slides, I show detail about NER problems for Vietnamese text. Named entities (NEs) are phrases and they contain: person (PER), organization (ORG), location (LOC), times and quantities, monetary values, percentages, etc. NER is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction (since 1990s). Vietnamese Language and Speech Processing (VLSP) community has organized an evaluation campaign to systematically compare NER systems for Vietnamese language (2016, 2018) based on the ability to recognize NEs types: PER, ORG, LOC, and MISC. The NER systems are using some state-of-art methods to solve NER problems for Vietnamese text with high accurate. Some neural network models proposed for NER tasks: J. P. C. Chiu and E. Nichols [3], Z. Huang et al. [6], G. Lample et al. [8], X. Ma and E. H. Hovy [10], and these models are competitive with traditional models. However, there is a little of studies on LML based on deep neural networks for Named Entity Recognition (NER), especial in Vietnamese.
  4. Two next slides, I show detail about NER problems for Vietnamese text. Named entities (NEs) are phrases and they contain: person (PER), organization (ORG), location (LOC), times and quantities, monetary values, percentages, etc. NER is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction (since 1990s). Vietnamese Language and Speech Processing (VLSP) community has organized an evaluation campaign to systematically compare NER systems for Vietnamese language (2016, 2018) based on the ability to recognize NEs types: PER, ORG, LOC, and MISC. The NER systems are using some state-of-art methods to solve NER problems for Vietnamese text with high accurate. Some neural network models proposed for NER tasks: J. P. C. Chiu and E. Nichols [3], Z. Huang et al. [6], G. Lample et al. [8], X. Ma and E. H. Hovy [10], and these models are competitive with traditional models. However, there is a little of studies on LML based on deep neural networks for Named Entity Recognition (NER), especial in Vietnamese.
  5. Two next slides, I show detail about NER problems for Vietnamese text. Named entities (NEs) are phrases and they contain: person (PER), organization (ORG), location (LOC), times and quantities, monetary values, percentages, etc. NER is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction (since 1990s). Vietnamese Language and Speech Processing (VLSP) community has organized an evaluation campaign to systematically compare NER systems for Vietnamese language (2016, 2018) based on the ability to recognize NEs types: PER, ORG, LOC, and MISC. The NER systems are using some state-of-art methods to solve NER problems for Vietnamese text with high accurate. Some neural network models proposed for NER tasks: J. P. C. Chiu and E. Nichols [3], Z. Huang et al. [6], G. Lample et al. [8], X. Ma and E. H. Hovy [10], and these models are competitive with traditional models. However, there is a little of studies on LML based on deep neural networks for Named Entity Recognition (NER), especial in Vietnamese.
  6. Two next slides, I show detail about NER problems for Vietnamese text. Named entities (NEs) are phrases and they contain: person (PER), organization (ORG), location (LOC), times and quantities, monetary values, percentages, etc. NER is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction (since 1990s). Vietnamese Language and Speech Processing (VLSP) community has organized an evaluation campaign to systematically compare NER systems for Vietnamese language (2016, 2018) based on the ability to recognize NEs types: PER, ORG, LOC, and MISC. The NER systems are using some state-of-art methods to solve NER problems for Vietnamese text with high accurate. Some neural network models proposed for NER tasks: J. P. C. Chiu and E. Nichols [3], Z. Huang et al. [6], G. Lample et al. [8], X. Ma and E. H. Hovy [10], and these models are competitive with traditional models. However, there is a little of studies on LML based on deep neural networks for Named Entity Recognition (NER), especial in Vietnamese.