Neural NER Algorithm
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A Neural NER Algorithm is an NER algorithm that is a neural NLP algorithms and which can be implemented by a neural NER system (designed to solve neural NER tasks).
- Context:
- It can (typically) utilize various types of neural architectures, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformer Models, to process and analyze text data for entity recognition.
- It can (often) involve training on large annotated datasets to learn the contextual representations and dependencies necessary for accurate entity identification and classification.
- It can be enhanced by Pre-trained Language Models like BERT, ELMo, and GPT, which provide a deep understanding of language context and semantics, improving the algorithm's accuracy.
- It can benefit from techniques like Transfer Learning and Fine-Tuning to adapt pre-trained models to specific domains or languages with limited labeled data.
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- Example(s):
- Bidirectional LSTM-CRF-based NER, which combines Long Short-Term Memory (LSTM) networks with a Conditional Random Field (CRF) layer for sequence tagging.
- a Bidirectional Transformer Encoder-based NERs, such as:
- BERT-MRC (Li, Feng, et al., 2019), which applies a Bidirectional Transformer for question-answering frameworks to the NER task.
- GLiNER (Zaratiana et al., 2023), a compact model that leverages a Bidirectional Transformer Encoder for efficient and effective named entity recognition across various domains and languages.
- a Cross-domain and Cross-lingual NERs, such as T-NER.
- an LLM-based NERs, such as GPT-NER (Wang, Sun et al., 2023).
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- Counter-Example(s):
- Rule-based Named Entity Recognition Algorithm, which relies on hand-crafted rules and dictionaries rather than learning from data.
- Traditional Machine Learning NER Algorithm, which uses features engineered from the text without the capacity for deep contextual understanding.
- See: Named Entity Recognition System, Contextual Embedding, Transfer Learning, Fine-Tuning.