Semantic Similarity
Semantic similarity is a measure of how closely two pieces of text convey the same meaning. AI computes semantic similarity using vector embeddings, enabling applications like duplicate detection and recommendation.
Understanding Semantic Similarity
Semantic similarity quantifies how close two pieces of text are in meaning, regardless of whether they share the same words. It is computed by converting text into dense vector representations using models like BERT or sentence transformers, then measuring the distance between vectors using metrics such as cosine similarity. This capability underpins numerous AI applications including semantic search, duplicate detection, plagiarism checking, and recommendation systems. Semantic similarity enables chatbots to match user queries with the most relevant FAQ entries and powers clustering algorithms that group topically related documents. Fine-tuning embedding models on domain-specific data can dramatically improve similarity measurements for specialized fields like legal text, medical literature, or technical documentation.
Category
Natural Language Processing
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Natural Language Processing Terms
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.