What are Intent Templates? 🤔

Definition:

Intent templates are patterns that represent common ways users ask questions. Each template has two parts:

• Non-variable Portion: Fixed text that remains the same.
• Variable Portion: A placeholder (often represented as $X) that stands in for the topic or specific detail in the query.

Why Use Them? 🚀

They help the system:

• Recognize clear-intent questions.
• Extract the topic from the query.
• Map the query to a specific question category (like treatment, recipe, etc.).

Example:

Template: "$X causes"

Matches: “diabetes causes”, “heart attack causes”

Template: "recipe for $X"

Matches: “recipe for lasagna”, “recipe for hummus”

Structure of an Intent Template 🏗️

Each intent template is structured into:

• Non-variable Portion: The fixed text (e.g., “recipe for”, “how do I treat”).
• Variable Portion: A placeholder like $X that represents any topic or subject.

Example Breakdown:

Template: "how do I treat $X"

Non-variable: “how do I treat”

Variable: $X (could be “diabetes”, “migraine”, etc.)

1. Obtaining Potential Intent Questions 🔍📚

The process starts by gathering potential intent questions from two main sources:

A. From Authoritative Sources (Step 405)

How It Works: The system scans trusted websites and documents to extract headings that look like questions.

Examples of Sources:

• Medical Subject Matter:
  • General Sources: webmd.com, mayoclinic.com, medicinenet.com
  • Focused Sources: cancer.org, heart.org
• Cooking Subject Matter:
  • General Sources: allrecipes.com, foodnetwork.com
  • Focused Sources: vegetariantimes.com

Why It Matters: These headings often naturally phrase clear questions (e.g., "How to treat diabetes" or "Best recipe for lasagna"), which can then be used as seeds for creating intent templates.

B. From Search Records (Step 410)

How It Works: The system reviews past search logs and aggregated query data.

Process Details:

• It identifies queries that frequently return results from the authoritative sources.
• Queries with top results from trusted sites are assumed to have clear intent.

Example: If many users type “heart disease treatment” (versus “how do I treat heart disease?”), both versions indicate a similar need. Gathering such variants broadens the template base.

2. Converting Potential Intent Questions to Templates

Once potential intent questions are gathered, the next step is to convert them into templates by replacing certain parts with placeholders.

A. Replacement with Placeholders (Step 415)

What Happens: The system scans each question and identifies consecutive terms that can be substituted by a variable placeholder (commonly $X).

Detailed Examples:

Example 1:

Original Question: “how diabetes is treated”

Potential Templates Generated:

• “how diabetes is treated”
• “how $X”
• “how diabetes $X”
• “how diabetes is $X”
• “$X treated”
• “$X is treated”
• “how $X treated”

Example 2:

Original Question: “How to make hummus”

Potential Templates Generated:

• “how to make hummus”
• “how $X”
• “how to $X”
• “how to make $X”
• “how $X make hummus”
• “how $X hummus”
• “$X hummus”
• “$X make hummus”

Note: The system might avoid substituting common question words (e.g., who, what, when) to maintain template clarity.

3. Frequency Analysis & Template Selection 📊✅

Not every potential template is used. The system refines its list by analyzing frequency.

A. Creating a Histogram (Step 420)

Process: All potential intent templates generated are tallied to see how often each occurs across documents and queries.

Why: Templates that appear frequently indicate common query patterns and are thus more valuable.

B. Selecting High-Frequency Templates (Step 425)

Selection Criteria:

• Predetermined Number: The system may select the top N templates.
• Threshold-Based: Only templates that occur more than a certain number of times are chosen.
• Combination: Sometimes a mix of both strategies is used.

Outcome: The resulting set represents the most common and useful intent templates, ready for further categorization.

4. Associating Templates with Question Categories 🗂️🤖

Once templates are finalized, they must be organized into clusters (or question categories) that group similar queries together.

A. Manual vs. Automatic Assignment (Step 430)

Manual Assignment: A user or administrator reviews the list and assigns each template to a category.
Example: Grouping “how do I treat $X” and “how is $X treated” under the Treatment category.

Automatic Assignment: The system can:

• Issue a query using the template (replacing $X with a sample topic).
• Compare search results across templates.
• Cluster templates with similar search result patterns into the same category.

Hybrid Approach: A combination of both methods may be used where:

• Templates that naturally cluster (high similarity in search outcomes) are automatically grouped.
• Outliers or ambiguous templates are then manually reviewed.

B. Examples of Categorization:

• Treatment Category:
  • Templates: “how do I treat $X”, “$X treatment”, “how is $X treated”, “how to cure $X”
• Recipe Category:
  • Templates: “recipe for $X”, “how to make $X”, “directions for making $X”

5. The Broader Role of Intent Templates in the System

When a user submits a query, the system uses intent templates to determine if it’s a clear-intent question. Here’s how:

Process 700: Determining Query Intent

Generate Potential Templates from the Query (Step 705): The system breaks down the query and forms potential templates.

Example: For “how is migraine treated?”, it might generate the template “how is $X treated”.

Match Against Known Intent Templates (Step 710): The system checks if any potential template matches an existing, established intent template.

Decision:

• If No: The query is not a clear-intent question.
• If Yes: Proceed to the next step.

Determine the Query Topic (Step 715): The portion of the query that fits the variable slot ($X) is identified as the topic (e.g., “migraine”).

The system then checks its Q&A data store to confirm if this topic and its corresponding question category exist.

Outcome:

• If the topic exists: The system retrieves natural language answers.
• If not: The query might be processed as a standard keyword search.

Converting Natural Language to Keyword Queries

Enhanced Snippet Results: Once a query is recognized as clear-intent, the system can also convert it to a keyword query (e.g., “migraine treatment”) to fetch supporting snippet-based search results.

Example in Action:

User Query: “how is migraine treated?”

Process:

• The system recognizes the query matches the template “how is $X treated” (where $X = “migraine”).
• It confirms that “migraine” exists under the “treatment” category in the Q&A data store.
• It retrieves and ranks the best natural language answer while also using the keyword “migraine treatment” to fetch relevant snippet-based results.

6. Handling Ambiguous or Undesired Queries 🚫

Not every query that seems to match a template should trigger a natural language answer. Some queries, even if they match a template, might have different intents. For example:

Ambiguous Case: Query: “center of disease control and prevention” might match a template like "$X prevention" but its intent is informational, not a clear medical question.

Blacklisting: Some undesired queries (e.g., “how to make money”) are added to a blacklist so the system doesn’t process them as clear-intent questions.

7. Benefits of Using Intent Templates 🌟

• Direct Answers: Users get immediate, natural language answers instead of only link-based snippets.
• Improved Relevance: The system accurately matches the query topic and question category, delivering highly relevant results.
• Enhanced User Experience: They help translate natural language queries into keyword queries. For example, after matching a query like “how is migraine treated?” with its template, the system might generate a keyword query “migraine treatment” to retrieve additional snippet-based results.
• Flexibility & Scalability: Templates can be updated or expanded over time, ensuring that the system adapts to evolving query patterns.
• Maintaining Consistency: Since intent templates do not change rapidly, they can be regenerated periodically (e.g., weekly or monthly) to adapt to new query patterns without affecting the existing Q&A data store.
• Matching Query Headings: When processing documents, headings that match an intent template (e.g., “Cancer symptoms” matching “$X symptoms”) are flagged and stored along with their corresponding answer text.