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Named Entity Recognition (NER) is the task of identifying named entities in a piece of text, such as people, organizations, and locations. NER is often used in natural language processing (NLP) applications, such as Question-Answering, Machine Translation, and Text Summarization
How is Named Entity Recognition done?
There are a number of different techniques that can be used for NER. These techniques include:
- Rule-based systems: These systems use a set of rules to identify named entities. For example, a rule-based system might look for the word "Mr." to indicate that a named entity is a person
- Machine Learning Algorithms: These systems use machine learning algorithms to learn how to identify named entities. Machine learning algorithms can be trained on a dataset of text that has already been labeled with the correct named entities.
- Hybrid Systems: These systems combine rule-based systems and machine learning algorithms. Hybrid systems can often achieve better accuracy than either rule-based systems or machine learning algorithms alone.
What are the challenges of Named Entity Recognition?
There are a number of challenges to NER, including:
Ambiguity
The meaning of text can be ambiguous. For example, the word “bank” can refer to a financial institution or to the side of a river.
Complexity
Text can be complex, containing multiple meanings and nuances. This can make it difficult to identify named entities correctly.
Bias
NER systems can be biased. For example, a NER system that is trained on a dataset of text from a particular country might be biased towards that country’s culture or language.
Ambiguity
The meaning of text can be ambiguous. For example, the word “bank” can refer to a financial institution or to the side of a river.
Complexity
Text can be complex, containing multiple meanings and nuances. This can make it difficult to identify named entities correctly.
Bias
NER systems can be biased. For example, a NER system that is trained on a dataset of text from a particular country might be biased towards that country’s culture or language.
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Our service is for anyone who is interested in learning or is aspiring for best grades in their assignments on Named Entity Recognition. We also conduct online tutoring for Data Science aspirants. No prior experience is necessary for learning Data Science, Machine Learning and Natural Langauage Processing (NLP)
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