Answer Seeking Query

What Is an Answer-Seeking Query?

Definition: An answer-seeking query is one where the user expects a short, direct answer rather than a list of links.

Example:
Query: “When was George Washington born?”
Expected Answer: “Feb. 22, 1732” 📆

Contrast with Non-Answer-Seeking Queries: Not every query expects a single, concise answer. Some queries are used to locate a set of relevant documents.

Example:
Query: “restaurants in New York”
Reason: No single concise answer exists—users expect many results (a list of restaurant options) 🍽️.

Process

Process: User Query → Search Results Retrieval → Query Classification (Question Types) → Answer Extraction (Answer Types) → Scoring & Selection → Display in Answer Box.

Real-Time Answer Generation: From Query to Displayed Answer

Receive Query and Get Search Results (Step 410): The search engine retrieves documents relevant to the query. 🔍
Determine If the Query Matches a Question Type (Step 420): If no matching question type is found, the query is treated as non-answer-seeking, and only regular search results are displayed.
Example: “restaurants in New York” would typically not match a question type expecting a concise answer.
Classify the Query as Answer-Seeking (Step 430): If a matching question type is found (e.g., “how to cook lasagna”), the system classifies it as answer-seeking.
Retrieve Associated Answer Types (Step 440): The system uses an index to quickly find all answer types (including those with skip gram elements) associated with the matching question type.
Score Passages Against Answer Types (Step 450): The system scans passages in the retrieved documents to identify candidates that match the answer types.
Scoring Details: Each passage is scored based on the number and quality of matching answer elements (including matches via skip grams).
Select the Best Answer (Steps 460 & 470): If the highest-scoring passage meets the quality threshold, it is chosen as the answer.
This answer is then displayed in the answer box on the search results page.
If Not: The system may show just the list of standard search results (Step 480).

A. Creating Question Types from Queries 📝

These are the parts of the query that help identify what is being asked. They include:

🏷 N-grams: Definition: Specific words or phrases (e.g., “how”, “when”).
Example: In “how to cook lasagna”, the word “how” is an n-gram.
🏛 Entity Instance: Definition: Specific names or entities.
Example: “Abraham Lincoln” in “When was Abraham Lincoln born?”
🏷 Entity Class: Definition: The category to which an entity belongs.
Example: “lasagna” can be recognized as belonging to the class “dishes” 🍝.
🏆 Part-of-Speech Class: Definition: Identifies roles like verbs, nouns, etc.
Example: “run” is categorized as a verb.
🌱 Root Word: Definition: The main action or focus in a question.
Example: In “how to cook lasagna”, “cook” is the root word.
❓ Question Triggering Words: Definition: Keywords that indicate a question is being asked (e.g., “what,” “how,” “why”).
Example: "how" in "How do I make hummus?"

B. Answer Elements 📝

These define what the answer should include and help the system score potential answers. They include:

📏 Measurement: Definition: Numbers, dates, durations, etc.
Example: “Feb. 22, 1732” is a date measurement.
🔤 N-grams: Definition: Specific phrases or terms from the answer text.
🔗 Verb & Preposition: Definition: Identifies action words (verbs) and connecting words (prepositions).
Example: In “Obama was born in Honolulu”:
“born” is a verb 🏃‍♂️
“in” is a preposition 📍
🏛 Entity Instance: Definition: Recognizes a specific named entity in the answer.
Example: “Obama” in “Obama was born in Honolulu.”
📍 Proximity-Based Elements:
- 🔎 N-gram Near Entity: The system checks if certain n-grams appear near a recognized entity.
  Example: In “Obama was born in Honolulu”, “Honolulu” is near “Obama.”
- 🔄 Verb/Preposition Near Entity: Ensures verbs or prepositions are close to an entity for context accuracy.
Skip Grams: Definition: Skip grams capture bigrams (pairs of words) with a defined number of words that can be skipped in between.
How They Work: For instance, if the skip value is 1, the skip gram “where * the” will match phrases like:
- “where is the”
- “where was the”
- “where does the”
- “where has the”
The asterisk (*) represents one skipped word.
Usage in Answer Types: The system includes a skip gram as an answer element to recognize flexible phrase structures.
Example: For the question “Where is the tallest building?” a skip gram like “where * the” might help capture variations in the answer phrase even if an extra word is present.

C. Triggering the Answer Scoring Engine ⚙️

How It Works:

When an answer-seeking query is identified, it triggers an Answer Scoring Engine.
This engine evaluates the relevance of potential answers based on the above question and answer elements.
It scores candidate answers to determine which one best fits the query.
Outcome: The highest-scoring answer is then provided to the user in a clear and concise format.
Example in Action:
Query: "What is the boiling point of water?"
Answer Elements: Measurement ("100°C"), verb ("boils"), and supporting context from authoritative sources.
Result: The search engine quickly displays "100°C" as the answer, without extra text.

How Does It Work? 🔍📊

Receiving Inputs
- Documents & Passages: After the search engine fetches documents relevant to your query, the Answer Scoring Engine looks at these documents to find candidate passages.
  Example: For the query “When was George Washington born?”, it might scan passages like:
  “George Washington was born on Feb. 22, 1732 in Westmoreland County, Virginia.”
- Pre-Computed Data: It uses question type/answer type pairs provided by the Training Engine. These pairs tell the system what features (like specific words, measurements, or even skip grams) are expected for certain types of questions and answers.
Matching Against Expected Answer Types
- Identifying Answer Elements: The engine checks candidate passages for key answer elements. These include:
  - Measurements: Numbers, dates, durations (e.g., “Feb. 22, 1732” is a date measurement) 📆.
  - Entity Instances: Specific names or entities (e.g., “George Washington”).
  - Skip Grams: Flexible phrase structures that capture variations.
  - Skip Gram Example: A skip gram like “where * the” might match phrases such as “where is the”, “where was the”, etc.
- Scoring Process:
  - Number of Matching Elements: More matches generally mean a better candidate.
  - Quality of Matches: How well the passage’s elements align with the expected answer type. This is often quantified using statistical measures like PMI (Point-wise Mutual Information) or NPMI (Normalized PMI).
  - Proximity & Order: For example, if an expected element (like a date) is near a recognized entity (e.g., “George Washington”), that passage might score higher.
Aggregating Scores and Selecting the Best Answer
- Aggregation: The engine may combine scores from multiple answer elements within a passage. For instance, if a passage matches both a measurement and a skip gram element, it gets a combined (or aggregated) score.
- Threshold Check: Only passages with scores above a certain threshold are considered good enough to be shown as the final answer.
- Final Selection: The highest-scoring candidate that meets the quality criteria is selected and displayed in the answer box.
  Example: If the passage containing “Feb. 22, 1732” scores the highest, that date will appear as the direct answer to “When was George Washington born?”

Real-World Example 🌟

Imagine you search: “When was George Washington born?”

Candidate Passages:
- Passage 1: “George Washington was born on Feb. 22, 1732 in Westmoreland County, Virginia.”
- Passage 2: “Washington, known for his leadership, was born in 1732.”
Matching Elements:
- Passage 1 contains a clear measurement element (“Feb. 22, 1732”) plus contextual details.
- Passage 2 provides a year but lacks the precise date.
Scoring: Passage 1 scores higher due to its precise measurement and potential match with expected skip gram patterns (if the system anticipates a full date format).
Final Answer: The Answer Scoring Engine selects Passage 1, and “Feb. 22, 1732” appears in the answer box.