# Semantic Search > Babel Street Semantic Search uncovers hidden insights by bridging language and context gaps, helping users find nuanced connections across data. Talk to an expert now! --- *​​​Babel Street ​​Semantic Search* # Powerful, Multilingual Semantic Search Babel Street ​S​emantic Search enable​s​ users to move beyond basic keyword matching and tap into true contextual understanding across multiple languages. Semantic Search bridges linguistic and conceptual gaps, surfacing insights that would otherwise remain hidden so users can rapidly uncover nuanced connections and trends across languages and domains. --- ## Go beyond keywords to search by meaning - **Expanded Search by Meaning**: As users search by key phrase, Babel Street automatically expands search terms and retrieves semantically relevant results - **AI-assisted Search Refinement**: Quickly filter search results by people, organization, location, sentiment, and disambiguated entities - **Cross-lingual and Multilingual Search**: Multilingual natural language processing (NLP) and contextual language models return accurate results regardless of the language or script used in the source content - **Fine-Grained Sentiment Detection**: Sentiment analysis is provided at both the phrase ​or document ​level with full integration into the semantic pipeline - **Verifiability and Trust**: Every data point returned in a search — whether it’s a name, location, or behavioral signal — is linked to its original source, allowing users to verify origin and context ## Unleashing the power of advanced semantic intelligence ### Foundational NLP - Accurate language identification — Automatically detects the language of each document or query with high reliability even in multilingual or code-switched data. - Tokenization — Splits unstructured text into words, phrases, or other meaningful units, respecting linguistic nuances. - Morphological analysis — Breaks down words into roots, stems, prefixes, and suffixes to interpret grammatical structure, inflection, and derivation. - Lemmatization — Reduces words to their base or dictionary form (lemma), allowing semantically similar words to be grouped for more effective matching. - Part-of-speech tagging — Assigns grammatical categories, enabling syntactic parsing and allowing for more accurateinterpretation of query intent and document meaning. - Named entity recognition (NER) — Automatically detects and classifies proper names to enrich search results and support entity-based retrieval. ### State-of-the-art text analytics - Entity linking and disambiguation — Connects detected entities to knowledge bases, resolving ambiguity and supporting knowledge graph integration. - Key phrase extraction — Identifies significant phrases or concepts within documents, enabling faceted navigation, summarization, and context-aware ranking. - Sentiment and attribute analysis — Determines sentiment present in the text, supporting more nuanced filtering and relevance ranking. - Relationship extraction — Discovers and categorizes relationships between entities, enabling graph-based search and deeper insights. ### Unmatched language understanding - Multilingual and cross-lingual support — Handles queries and documents in multiple languages, offering seamless [cross-lingual search](https://www.babelstreet.com/landing/cross-lingual-search-based-on-concepts-and-meaning) by mapping concepts and entities across linguistic boundaries. - Semantic text embeddings — Converts words, sentences, or documents into vector representations that capture meaning and context. ### Industry-leading​​ contextual intelligence - Contextual query expansion — Enriches user queries with synonyms, related terms, or semantic variations, improving recall while maintaining relevant results. - Domain adaptation and customization — Incorporates domain-specific vocabularies, ontologies, and customer taxonomies to optimize search effectiveness for specialized content. ## Speak with an expert [Schedule a Meeting](https://www.babelstreet.com/contact) --- ## Frequently Asked Questions **How does ​S​emantic ​S​earch differ from traditional keyword search?** Traditional keyword search matches exact ​​words or phrases as they appear in documents, while semantic search understands the intent and contextual meaning behind queries, enabling it to retrieve relevant results even if the exact search terms are not present. **How does ​S​emantic ​S​earch work?** Semantic ​S​earch relies on word embeddings to transform words into vectors. These are numerical representations that approximate the conceptual distance of one word’s meaning from another. Then, it becomes a mathematical function to calculate the similarity between word vectors to determine if the words are related by meaning. Terms, synonyms, and concepts are aligned across languages so words can be semantically compared in multiple languages. **What types of data can ​S​emantic ​S​earch be applied to?** Semantic ​S​earch can be used with various types of unstructured data such as documents, emails, web pages, social media posts, and even multimedia content when combined with natural language processing techniques. **How is the accuracy of ​S​emantic ​S​earch evaluated?** The effectiveness of ​S​emantic ​S​earch is typically measured using metrics like precision, recall, F1-score, and user satisfaction. Human evaluation and relevance judgments are also used to ensure the system meets user expectations. **What are common applications of ​S​emantic ​S​earch?** Semantic ​S​earch powers advanced information retrieval in customer support, e-commerce, enterprise knowledge management, legal document review, academic research, and more, helping users find pertinent information more efficiently. **What is the role of machine learning in ​S​emantic ​S​earch?** Machine learning models, especially those in natural language understanding (NLU) and deep learning, underpin semantic search by enabling systems to learn from large corpora, recognize patterns, and continuously improve their retrieval accuracy. **How can organizations implement ​S​emantic ​S​earch in their workflows?** Organizations can deploy ​S​emantic ​S​earch solutions using pre-built APIs, open-source libraries, or cloud platforms. Integration often involves configuring the system to process domain-specific language and optimize results for their unique use cases. **What is the role of foundational NLP in ​S​emantic ​S​earch?** Foundational NLP describes the high-quality multilingual text analytics and natural language processing needed to clean and prepare unstructured text for searching and advanced analysis.