A sentence’s meaning is specified by the conditions under which it would be true; formally, a theory of meaning pairs sentences with truth conditions inside a model (entities, functions, relations). This model-theoretic view traces to Tarski’s work on defining truth ...

The principle of compositionality (often attributed to Frege) states that the meaning of a complex expression is determined by the meanings of its parts plus the rules for combining them. This is the default foundation for formal semantics, but it ...

A Sequential Query is any query that forms part of a series of related queries within a session or across sessions. Unlike one-off represented queries, sequential queries carry dependency: their meaning or scope often relies on earlier queries. For example: ...

The skip-gram model is a predictive approach for learning word embeddings. Given a center word, the model tries to predict its context words within a fixed window. If the center word is “SEO” and the context window includes words like ...

BERT (Bidirectional Encoder Representations from Transformers) is trained with a masked language model, enabling it to interpret words in full-sentence context. Unlike older models such as Word2Vec or Skip-Gram, which produce static vectors, BERT generates contextual embeddings, making it possible ...

A knowledge graph represents the world as nodes (entities) and edges (relations). KGEs map each node and relation to vectors (sometimes complex-valued) so that true triples score higher than false ones. In practice, this gives you a differentiable proxy for ...

Modern search is no longer about matching keywords—it’s about understanding unseen queries and aligning them with the right intent. This is where zero-shot and few-shot query understanding come into play, powered by large language models (LLMs). What is Zero-shot Query ...

Learning-to-Rank (LTR) is a machine learning approach used in information retrieval and search systems to order a set of documents, passages, or items by relevance to a given query. Instead of relying on static scoring functions (like BM25), LTR learns ...

DPR is a dual-encoder retriever: one encoder maps the query to a vector; another maps each passage to a vector. Retrieval becomes a fast vector similarity lookup rather than a sparse term match. This helps when users express ideas differently ...

What are Click Models? Click models are probabilistic frameworks that separate what users looked at from what they considered relevant. They estimate hidden variables like examination (did the user see a result?) and attractiveness (would they click if they saw ...