> ## Documentation Index > Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt > Use this file to discover all available pages before exploring further. # Machine Learning Engine [Back to NLP Topics](/ai-for-service/automation/natural-language/nlp-topics) The ML engine maps user utterances to intents using example-based training. It supports supervised and unsupervised learning, with a range of network types for different use cases. *** ## How the ML Engine Works **Intent detection pipeline:** 1. User utterance is preprocessed (tokenization, stop word removal, lemmatization, n-grams). 2. The ML model classifies the utterance as a probable or definitive match against trained intents. 3. Results are passed to Ranking and Resolver. **Entity extraction:** After intent is identified, the engine extracts entity values from the utterance using NER (Named Entity Recognition) trained annotations. **Supervised vs. unsupervised learning:** * **Unsupervised** (auto-expand): The assistant learns from all successful interactions automatically. * **Supervised** (recommended): Developers monitor interaction logs, tune NLP settings for failures, and retrain. *** ## Add ML Utterances 1. Go to **Natural Language > Training > Intents**. 2. Select an intent and add sample utterances. 3. Click **Train** to update the model. **Utterance guidelines:** * Utterances do not need to be full sentences; phrases work too. * For distinct intents (e.g., "Find Flight" vs. "Book Hotel"): 200-300 utterances each. * For similar intents (e.g., "Change Seat" vs. "Change Flight"): 1000+ utterances each. * For Deep Neural Networks: larger datasets (data-hungry models) produce better results. *** ## ML Network Types Configure under **Natural Language > NLU Config > Engine Tuning > ML**. | Network Type | Description | | ---------------------------------------------- | ----------------------------------------------------------------------------------- | | **Standard** (default) | Bag-of-words classification. Suitable for most use cases. | | **MLP-BOW** | Multi-layer perceptron with bag-of-words features. | | **MLP-WordEmbeddings** | MLP with pre-trained word embeddings. | | **LSTM** | Recurrent neural network. Captures sequential context. | | **CNN** | Convolutional neural network. Captures local n-gram patterns. | | **Transformer** | Transformer-based model. High accuracy for English. | | **KAEN** | Kore.ai custom model. Improved accuracy for English. | | **Zero-shot Model** | No training required; uses semantic similarity. Requires NLP V3. | | **Few-shot Model (Kore.ai Hosted Embeddings)** | LLM-based semantic similarity. Reduces training data requirements. Requires NLP V3. | *** ## Thresholds and Configuration Go to **Natural Language > NLU Config > Engine Tuning > Machine Learning**. | Setting | Description | | ------------------ | ---------------------------------------------------------------------- | | **ML Threshold** | Minimum confidence score for a probable match (default: 0.3). | | **Auto Training** | Automatically retrain the model when utterances are added or modified. | | **Network Type** | Select the ML model architecture. | | **Stop Words** | Enable/disable stop word removal during training. | | **Synonyms in ML** | Include bot synonyms in ML training. | *** ## Synonyms and Concepts for ML **Bot Synonyms:** Go to **Training > Bot Synonyms**. Add synonyms for words in task and entity names. Synonyms are app-wide. * To add: click **New Synonym**, enter the keyword and its synonyms (press Enter after each). * Parallel editing is supported — multiple users can add/delete synonyms concurrently without data conflicts. **Entity Synonyms:** Go to **Training > Entities**. Add synonyms per entity node. **Concepts:** Custom concept groups prefixed with `~`. Define in **Training > Concepts**. Used in patterns and entity detection. *** ## App Substitutions Define domain-specific word replacements using the `~bot_substitutions` concept: ``` Chevy|Chevrolet → replaces "Chevy" with "Chevrolet" ?_km|kilometers → replaces "5 km" with "5 kilometers" ``` Syntax: `original_word|replacement` (case-insensitive match, case-sensitive replacement). *** ## App Word Aliases Define alternate spellings or STT transcription corrections using Bot Synonyms with a `+` prefix. Example: `+Alpha Romeo` as an alias for `Alfa Romeo` — corrects speech-to-text transcription errors. * Aliases do not change the original utterance. * They normalize values returned for Custom Concept entities. *** ## Homophones Handle words that sound the same but have different spellings. Define using a concept with `|` separator: ``` write|right|rite ``` The engine considers all homophones during POS tagging and replaces the word if a homophone is a better grammatical fit. **To define:** **Training > Concepts > New Concept** — enter homophones separated by `|`. *** ## Emoji Support Import default emoji mappings into system concepts to handle emoji inputs: 1. Go to **Natural Language > Training > Intents > ⋯ > Import Emojis into Concepts**. 2. Emojis are added to system concepts (e.g., `~emohappy`, `~emohello`). 3. Review and remove unwanted emojis after import. *** ## Patterns for Intents and Entities See [NLP Guidelines — Patterns](/ai-for-service/automation/natural-language/nlp-guidelines#patterns) for operators, syntax, and examples. **Add intent pattern:** 1. **Training > Intents** → click **Patterns / +Pattern** for an intent. 2. Enter patterns; press Enter between each. **Add entity pattern:** 1. **Training > Entities** → click the edit icon for an entity. 2. Select the **Patterns** tab → enter patterns. **Add intent rules:** 1. **Training > Intents** → click **Rules / +Rule** for an intent. 2. Enter Trait or context tag conditions using AND/OR operators. **Negative patterns:** 1. Enable at **NLU Config > Ranking and Resolver Engine > Negative Patterns**. 2. Per intent: **Training > Intents** → select intent → **Negative Patterns** tab → add patterns. *** ## NLU Config — FM Engine Tuning | Setting | Description | | ------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Intent Detection using Task Name Words** | When disabled, intent is detected only if input starts/ends with the exact task name. Useful for tasks with shared words (e.g., multiple "order" tasks). Enabled by default. | | **FM Threshold** | Max % above the top-scoring intent allowed for other intents to be considered (0-20%; default 2%). Supported for English, Spanish, French, German. |