Partners / Elasticsearch
Fix search relevance at the source
Bitext adds a multilingual linguistic layer before Elasticsearch, helping search, semantic search and RAG teams work with cleaner normalized text before indexing.
Instead of relying only on stemming, dictionaries or analyzer tuning, Bitext prepares text with linguistic signals such as lemmatization, decompounding, language-aware segmentation and entity extraction before it reaches the search layer.
Search relevance
Semantic search
RAG preparation
Multilingual indexing
Decompounding
Partner ecosystem
Bitext + Elasticsearch
Bitext complements Elasticsearch by preparing multilingual text before indexing. The result is cleaner linguistic evidence for lexical search, semantic search, hybrid retrieval and RAG workflows.
What we offer
A linguistic normalization layer for Elasticsearch
Bitext improves relevance by enhancing linguistic normalization before indexing — especially lemmatization, decompounding and multilingual consistency. The goal is to reduce query complexity, improve matching and strengthen retrieval quality.
Before indexing
normalize language
After retrieval
reduce noise
Bitext plugin for Elasticsearch
Designed for teams building search, semantic search and RAG
Bitext is designed for teams that need better normalization, more reliable matching and stronger multilingual consistency before content reaches Elasticsearch.
Better normalization
Normalize words with linguistic analysis rather than relying only on approximate stemming behavior.
Reliable matching
Reduce false positives and false negatives caused by weak or inconsistent linguistic analysis.
Multilingual consistency
Improve behavior across English, German, French, Spanish and other multilingual search collections.
Stronger RAG input
Prepare cleaner evidence before chunking, indexing, retrieval and grounding.
Stemming vs lemmatization
Elastic’s default English analyzer can collapse terms that should remain distinct
By default, Elastic’s English analyzer uses the Porter stemmer. Because it is rule-based, it can collapse terms too aggressively, causing false positives and loss of meaning. Bitext lemmatization preserves more linguistic meaning.
| Input | Default Porter stemmer | Bitext lemmatization |
|---|
| Apple / apples | Apple → appl
apples → appl | Apple → Apple
apples → apple |
| United / unit | United → unit
united → unit
unit → unit | United → United
united → unite
unit → unit |
| useful / used | useful → us
used → us | useful → useful
used → use |
In practice, this can lead to false matches and loss of precision, while proper lemmatization preserves meaning and improves search relevance.
The problem today
Lucene-based stemming and decompounding are useful, but not always enough
Elasticsearch works extremely well for general-purpose search, but multilingual and semantic use cases often expose limitations in stemming, tokenization and compound-word handling.
Stemming is used instead of true lemmatization
Compound-heavy languages require extra dictionary work and tuning
Tokenization and splitting behavior is not always consistent across languages and domains
These limitations affect search relevance, semantic retrieval and RAG grounding
Elastic compound-word article
Examples
These issues are not theoretical
Real Elasticsearch users report stemming and decompounding issues in production-like multilingual environments.
Example 1 / French stemming
“Servier” matching “Service”
A user reports that stemming creates false positives in French, including the company name “Servier” matching “Service”.
View discussion →
Example 2 / German decompounding
Inconsistent decompounding behavior
Users also report inconsistent decompounding behavior even when using Elastic’s recommended filters and configuration options.
View issue →
Example 3 / Compound words
Meaning hidden inside compounds
Elastic itself highlights that compound words create search challenges because meaningful components can be hidden inside longer terms.
Read Elastic article →
In other words: stemming and decompounding are useful, but for multilingual search and RAG they can still leave precision, normalization and consistency gaps.
How Bitext solves it
Documents arrive normalized and ready for higher-quality retrieval
Bitext adds a linguistic layer before indexing, so Elasticsearch receives cleaner normalized text and stronger multilingual metadata.
True lemmatization
Use linguistic lemmatization instead of stemming approximations.
Robust decompounding
Improve compound handling, especially for German and other compound-heavy languages.
Multilingual consistency
Improve consistency across English, German, French, Spanish and other languages.
Higher-quality RAG input
Give semantic search and RAG pipelines cleaner evidence before retrieval and grounding.
How it fits
Bitext enriches text before Elasticsearch indexes it
Raw documents go through Bitext linguistic enrichment first. Elasticsearch then indexes cleaner normalized text, language-aware metadata and structured linguistic fields.
01 / Input
Raw content
Documents, records, product data, support tickets, knowledge base content and multilingual text collections.
02 / Linguistic layer
Bitext NLP
Language ID, segmentation, lemmatization, decompounding, POS, entities, phrases and other multilingual linguistic signals.
03 / Search layer
Elasticsearch
Indexes normalized text and linguistic fields for search, hybrid retrieval, semantic search, filtering and ranking.
04 / Applications
AI systems
Enterprise search, AI search, RAG, knowledge workflows, document intelligence and customer-facing applications.
Seamless integration
Normalized indexing and querying without changing application logic
Bitext integrates advanced word segmentation, lemmatization and decompounding into Elasticsearch-style indexing workflows through custom analyzers and token filters.
This enables normalized indexing and querying while preserving the application experience on top of Elasticsearch.
Higher relevance across multilingual collections
Improved recall for compound-heavy languages
No changes required in application logic
Bitext multilingual NLP platform
Beyond Elasticsearch: a full multilingual NLP platform
Bitext provides a full multilingual NLP SDK to power search, AI and structured data extraction across large document collections.
640k
words / sec
High-speed processing on an 8-core CPU.
50MB
disk footprint
Lightweight per-language footprint.
200MB
RAM
Designed for local and enterprise deployment scenarios.
70+
languages
Broad multilingual NLP coverage.
Core capabilities
Language ID, sentence segmentation, lemmatization, word segmentation, decompounding, agglutination, POS tagging, phrase structure tagging, entity extraction and concept extraction.
Supported environments
Linux, macOS, Windows, ARM and x64 environments, with local/on-premises or cloud deployment depending on customer architecture.
AI and search workflows
Semantic search, RAG, entity and concept extraction, vector search, graph creation, compliance workflows and document intelligence.
Use cases
Where Bitext improves Elasticsearch workflows
Bitext is useful when search quality depends on multilingual normalization, better lexical evidence and more reliable retrieval input.
Semantic search and RAG
More grounding and precision, less noise and fewer hallucinations from poor retrieval evidence.
Entity and concept extraction
Fast multilingual enrichment for vector search, graph workflows and compliance-oriented search systems.
Graph RAG
Structured linguistic signals to accelerate knowledge graph creation from unstructured text.
Get started
Evaluate Bitext for your Elasticsearch pipeline
If you are building multilingual search, semantic retrieval or RAG pipelines on Elasticsearch, Bitext can help improve relevance, reduce noise and strengthen linguistic consistency.
