## Question Answering
- Special field in AI/NLP
- Goal: Answer questions in natural language
- Classification of QA systems:
	- Answer Domain: Open/Closed
	- Supported Question Types: Factoids/Definitions/Yes-No/...
	- Technologies: Shallow QA/Deep QA
- Dissociation to IR:
	- IR return relevant documents for keyword queries
	- QA should answer complicated questions

### Answer Domain
- **Closed-Domain**
	- QA within a single domain
	- e.g., medicine, animals, etc.
	- Opportunities:
		- Exploit domain-specific language
		- Use domain-specific ontologies (e.g., MeSH)
- **Open-Domain**
	- QA answering any topic
	- Any type of question may be asked (unknown topic and question type)
	- Requires different data sources
		- Aggregation and selection could become hard

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### Question Types
- **Question Types:**
	- Factoid
	- List
	- Definition
	- Relationship
	- Superlative
	- Yes-No
	- Opinion
	- Cause & Effect
- **Answer types:**
	- Person
	- Place
	- Date
	- Number
	- Explanation
	- Method
- **Question focus:**
	- Entity being asked by the question
- **Question topic:**
	- Object or event the question is about
- e.g. What is the height of Mt. Everest?
	- *Focus*: Height
	- *Topic*: Mt. Everest

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### Technology
#### Shallow QA
- Uses simple IR methods to find relevant answer documents
- Find a passage directly answering the question
- Keyword-based search (strip filler words)
- Answer: Use templates
- Simple case: Question already answered in the web

![](images/14-shallow.png)
#### Deep QA
- Goal: Answer more complex questions
- Used techniques:
	- Part-of-Speech Tagging (POS)
	- Named Entity Recognition (NER)
	- Ontologies (linked data, Folksonomy, ...)
	- Reasoning (translate to logical query)

##### Part-of-Speech Tagging
- Detect grammatical type of words (*lexical classification*)
- Detect relationship to other words
- Problems:
	- Language is ambiguous
	- Semantics are hidden/implicit
	- Using heuristics
- Classifications (around 150):
	- Noun
	- Verb
	- Article
	- Adjective
	- Adverb
	- Preposition
	- Pronoun
	- Conjunction
	- Interjection
- Training:
	- Algorithms are trained on a corpus

##### Named Entity Recognition
- Identify words representing named entities (e.g., from a domain)
- Usually, entities are tagged with their type

##### Common-sense Knowledge Base / Ontology
- Related to upper ontologies
- Usually, automatically generated from encyclopedias
- e.g., DBpedia, YAGO
- Contents:
	- Ontology of classes + individuals
	- Parts, materials, properties, functions of objects
	- Locations, duration, preconditions, effects of actions and events
- Use commonsense ontologies to answer queries directly

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### Showcase: Semi-Shallow Factoid QA
- Semi-Shallow Factoid System:
	- Input knowledge: Text documents
	- Factoid questions (e.g., when, who, how big)
	- Use WWW data sources
	- Use IR techniques
	- Use some NLP techniques
- Steps:
	- Query formulation
	- Query classification
	- Document retrieval
	- Passage retrieval
	- Answer processing

![](images/14-showcase.png)
#### Query Formulation
- Take the query and create a list of keywords
- Use the *bag of words* for a normal IR system
- Steps:
	- Remove stop words
	- Expand query using a thesaurus/ontology (e.g., WordNet)

#### Query Classification 
- Find required *answer type*
- Use question ontologies to get the questioned property
	- e.g., *How big* relates to *size*
- Use supervised learning to learn question and answer types
- Detect *question topic* (e.g., NER)
- Can be used for query formulation
	- e.g., combine queried property and entity

#### Document Retrieval
- Retrieve candidate documents containing the query terms
- Result: Candidate documents

#### Passage Retrieval
- Find relevant passages
	- Remove passages not containing the answer
- Rank using different characteristics:
	- Number of named entities of the correct answer   type (e.g., for *size*: meter, feet)
	- Number of question keywords/topics
	- Sentence proximity between passage and query

#### Answer Extraction
- From the selected passage, extract the actual answer
- Approaches:
	- Answer pattern extraction (designed patters from the answer ontology)
	- N-gram tiling (statistical approach)
- Problem: Multiple different answers
	- Which to chose?

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### Watson (D)
- Deep QA project
- Watson beat all in Jeopardy
	- Loose money if wrong answer chosen
	- Open domain
	- Complex and obfuscated questions
- Architecture:
	- Parallelized probabilistic evidence-based approach
	- Combination of shallow IR and Deep Ontology
	- Generate multiple hypotheses
	- Find evidence
	- Model probabilistic confidences
- Data:
	- Seed corpus (encyclopedias, dictionaries, ...)
	- Create expanded corpus (query internet for related documents)
	- Structured corpus (databases, ontologies, taxonomies)
- Question classification:
	- Type of question
	- Focus and Lexical Answer Type Detection
	- Relation detection
	- Question decomposition
- Hypothesis scoring (deep QA):
	- IR-based scorer
	- Ontology-based reasoning
	- Geo-spatial reasoning
	- Temporal reasoning