Babel Street Semantic Search
Babel Street Semantic Search enables 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 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
Product Features
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.
