Named Entity Recognition
Named Entity Recognition (NER) is an NLP task that identifies and classifies named entities like people, organizations, locations, and dates in text. NER is a fundamental building block for information extraction.
Understanding Named Entity Recognition
Named Entity Recognition (NER) is a core natural language processing task that identifies and classifies key entities in text into predefined categories such as person names, organizations, locations, dates, and monetary values. Modern NER systems leverage deep learning architectures like transformers and recurrent neural networks to achieve high accuracy, even on complex and ambiguous text. Applications span across industries: financial firms extract company names from earnings reports, healthcare systems identify drug names and dosages in clinical notes, and search engines use NER to build knowledge graphs. Pre-trained language models like BERT have dramatically improved NER performance through transfer learning, enabling fine-tuning on domain-specific datasets with relatively few labeled examples. NER often serves as a foundational step in larger natural language understanding pipelines.
Category
Natural Language Processing
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Abstractive Summarization
Abstractive summarization generates new text that captures the key points of a longer document, rather than simply extracting existing sentences. It requires deep language understanding and generation capabilities.
Beam Search
Beam search is a decoding algorithm that explores multiple candidate sequences simultaneously, keeping only the top-k most promising at each step. It balances between greedy decoding and exhaustive search in text generation.
BERT
BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that reads text in both directions simultaneously. BERT revolutionized NLP by enabling deep bidirectional pre-training for language understanding tasks.
Bigram
A bigram is a contiguous sequence of two items (typically words or characters) from a given text. Bigram models estimate the probability of a word based on the immediately preceding word.
Byte Pair Encoding
Byte Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of characters or character sequences. BPE is widely used in modern language models to handle rare words and multilingual text.
Corpus
A corpus is a large, structured collection of text documents used for training and evaluating natural language processing models. The quality and diversity of a training corpus significantly impacts model performance.
Extractive Summarization
Extractive summarization selects and combines the most important sentences directly from a source document to create a summary. It preserves the original wording but may lack the coherence of abstractive approaches.
Grounding
Grounding in AI refers to connecting a model's language understanding to real-world knowledge, data, or sensory experience. Grounded AI systems produce more factual and contextually relevant outputs.