> ## 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.

# Fundamental Meaning Engine

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The Fundamental Meaning (FM) engine is a deterministic, rule-based NLP model built on ChatScript. It analyzes word meaning, position, conjugation, and sentence structure to identify intent.

***

## How the FM Engine Works

### Processing Pipeline

| Step              | Description                                                                                                                                                              |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Tokenization**  | Splits text into tokens. Retains hyphenated words; splits digit-hyphen combos (e.g., `2-3` → `2 - 3`).                                                                   |
| **Substitution**  | Expands abbreviations, contractions, and misspellings using system concepts. Bot substitutions apply custom replacements (e.g., `Chevy` → `Chevrolet`).                  |
| **Merging**       | Combines sequences of single words, numbers, or dates into single tokens (e.g., `credit card`, `twenty-five`).                                                           |
| **Spell Check**   | Replaces unknown words with known matches using WordNet and app-defined terms.                                                                                           |
| **Lemmatization** | Converts words to their base form using WordNet.                                                                                                                         |
| **Gleaning**      | Identifies and normalizes special segments: polite phrases (treated as noise), multi-intent connectors, numbers, and system entities (dates, currencies, phone numbers). |
| **POS Tagging**   | Tags each word with its part of speech and role (subject/verb/object).                                                                                                   |
| **Marking**       | Assigns a concept/meaning to each word based on context. Example: `book` in "book a flight" is marked as a verb.                                                         |

### Scoring

The FM engine scores each intent match based on:

* **Position** of matched words in the sentence.
* Whether matched words are **nouns or verbs**.
* **Role**: main subject, main verb, or main object.
* **Exact match** vs. synonym match.
* **Tense**: present/future tense is preferred.

The engine prefers tasks where matching words are close together, toward the start of the sentence, and in the same order as the task label.

***

## Key Components

| Component    | Description                                                                                                       |
| ------------ | ----------------------------------------------------------------------------------------------------------------- |
| **Synonyms** | Alternative words for task/entity names. App-wide.                                                                |
| **Concepts** | Predefined or custom groups of related terms. Used in patterns (e.g., `~world_country`, `~fruit`).                |
| **Patterns** | Sentence structures for idiomatic or non-obvious expressions. Pattern matches take priority over synonym matches. |

***

## Manage Synonyms

**How synonyms work:** Synonyms for a word apply across all tasks and entities that contain that word.

**Add Bot Synonyms:**

1. Go to **Natural Language > Training > Bot Synonyms**.
2. Click **New Synonym**.
3. Enter the keyword and its synonyms (press Enter after each).

**Add Entity Synonyms:**

1. Go to **Natural Language > Training > Entities**.
2. Click the **Edit** icon for an entity.
3. Enter synonyms and press Enter.

**Parallel editing:** Multiple users can add or delete synonyms concurrently. The platform tracks add/delete operations individually—no data is overwritten.

***

## Manage Concepts

**System concepts:** Built-in groups (e.g., `~yes`, `~no`, `~emohello`). Extend instead of modifying: create a new concept and add words.

**Custom concepts:**

1. Go to **Natural Language > Training > Concepts > New Concept**.
2. Name must start with `~` followed by alphanumerics and underscores.
3. Add concept members (one per line).

Use concepts in patterns: `buy ~fruit` matches `buy orange`, `buy banana`, etc.

**Disable default help:** Add `~disable_help` concept and train.

***

## App Substitutions

Replaces words in user utterances before NLP processing.

Define using the `~bot_substitutions` concept:

* Syntax: `original_word|replacement`
* Case-insensitive match; case-sensitive replacement.
* When replacement is empty (no text after `|`), the word is removed.

**Special patterns:**

* `?_word|replacement` — matches a number followed by the word: `?_km|kilometers` → `5 km` becomes `5 kilometers`.
* `< word|replacement` — matches only when the word appears first in the sentence.

***

## App Word Aliases

Aliases normalize STT transcription errors using the `+` prefix on Bot Synonyms.

Example: `+Alpha Romeo` as an alias for `Alfa Romeo` — corrects voice transcription to the canonical form.

Aliases do not change the original utterance but:

* Return the normalized value for Custom Concept entities.
* Link concepts to new words (Marking).

***

## Homophones

Handle words that sound alike but are spelled differently (e.g., `write` / `right`).

Define using a Concept with members separated by `|`:

```
write|right|rite
```

The engine considers all homophones during POS tagging:

* If a homophone is a better grammatical fit, it replaces the original word.
* If equally valid, the original word is marked with the homophone.

***

## Manage Patterns and Rules

See [NLP Guidelines — Patterns](/ai-for-service/automation/natural-language/nlp-guidelines#patterns) for full pattern operator reference.

**Add intent patterns:**

1. **Training > Intents** → click **Patterns / +Pattern** for an intent.
2. Enter patterns; press Enter between each.

**Add intent rules** (trait or context tag conditions):

1. **Training > Intents** → click **Rules / +Rule** for an intent.
2. Enter conditions using AND/OR operators.

**Add entity patterns:**

1. **Training > Entities** → edit icon for an entity → **Patterns** tab.
2. Enter patterns.

**Negative patterns** (prevent incorrect intent matches):

1. Enable: **NLU Config > Ranking and Resolver Engine > Negative Patterns**.
2. Per intent: **Training > Intents** → select intent → **Negative Patterns** tab.

***

## NLU Config — FM Engine Tuning

Go to **Natural Language > NLU Config > Engine Tuning > Fundamental Meaning**.

| Setting                                    | Description                                                                                                                                          |
| ------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Intent Detection using Task Name Words** | Disabled: only match if input starts/ends with exact task name. Useful for tasks sharing words (e.g., multiple "order" intents). Enabled by default. |
| **FM Threshold**                           | Max % above the top-scoring intent for other intents to qualify. Range: 0-20% (default: 2%). Supported for English, Spanish, French, German.         |