Text Classification
Text classification is the NLP task of assigning predefined categories to text documents. Applications include spam filtering, topic labeling, and content moderation.
Understanding Text Classification
Text classification is a natural language processing task that assigns predefined categories or labels to text documents based on their content. It encompasses a wide range of applications including sentiment analysis, spam detection, topic categorization, intent recognition for chatbots, and content moderation. Traditional approaches used feature engineering with bag-of-words representations and support vector machines, while modern systems leverage transformer-based models like BERT that capture deep contextual relationships between words. Fine-tuning a pre-trained language model on domain-specific labeled data has become the standard approach, often achieving high accuracy with relatively small datasets thanks to transfer learning. Zero-shot classification using large language models can even categorize text without any task-specific training data, opening new possibilities for rapid deployment.
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.