Natural Language Processing
Natural Language Processing (NLP) is the field of AI focused on enabling machines to understand, interpret, and generate human language. NLP powers applications from chatbots and translation to sentiment analysis and search.
Understanding Natural Language Processing
Natural Language Processing (NLP) is a broad field at the intersection of linguistics, computer science, and artificial intelligence that enables machines to understand, interpret, and generate human language. NLP powers everyday technologies including voice assistants, email spam filters, machine translation services like Google Translate, and sentiment analysis tools used in social media monitoring. The field has undergone a dramatic transformation with the rise of deep learning, moving from rule-based and statistical methods to neural network approaches built on transformer architectures. Key NLP tasks include tokenization, part-of-speech tagging, named entity recognition, text classification, and question answering. Modern large language models have unified many of these tasks under a single architecture, blurring the traditional boundaries between natural language understanding and natural language generation.
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.