Different Semantic Perspectives for Question Answering Systems
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Transcript of Different Semantic Perspectives for Question Answering Systems
NLP & Semantic Computing Group
N L P
Different Semantic Perspectives forHybrid Question Answering Systems
Andre FreitasUniversity of Passau
OKBQA, Jeju, 2016
NLP & Semantic Computing Group
http://www.slideshare.net/andrenfreitas
These slides:
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Outline Multiple Perspectives of Semantic
Representation Lightweight Semantic Representation Knowledge Graph Extraction from Text Answering Queries with
Knowledge Graphs Reasoning Take-away Message
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Multiple Perspectives of Semantic Representation
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QA & Semantics
• Question Answering is about managing semantic representation, extraction, selection trade-offs.
• And it is about integrating multiple components in a complex approach.
•Semantic best-effort, systems tolerant to noisy, inconsistent, vague, data.
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“Most semantic models have dealt with particular types of constructions, and have been carried out under very simplifying assumptions, in true lab conditions.”
“If these idealizations are removed it is not clear at all that modern semantics can give a full account of all but the simplest models/statements.”
Formal World Real World
Baroni et al. 2013
Semantics for a Complex World
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Why Not RDF?•Follows a more “database-type” of
representation perspective.
•Gap towards representing text.
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Choices of Semantic Representation• Logical
• Frames: verbs | nouns
• Binary relations: binary | n-ary
• Named entities
• Language Models
• Syntactic structures
• Bag-of-words
Concept-level representation
Background knowledge
Extraction complexity
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Information Extraction• Logical
• Frames: verbs | nouns
• Binary relations: binary | n-ary
• Named entities
• Syntactic Structures & LMs
• Bag-of-words
• Semantic parsing
• Semantic role labeling
• Relation extraction: – closed/open
• Named entity recognition
• Syntactic/N-gram Parsing
• Indexing
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Information Extraction• Logical
• Frames: verbs | nouns
• Binary relations: binary | n-ary
• Named entities
• Syntactic Structures & LMs
• Bag-of-words
• Semantic parsing
• Semantic role labeling
• Relation extraction: – closed/open
• Named entity recognition
• Syntactic/N-gram Parsing
• Indexing
Use all of them!
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Representation focal points•Types of knowledge to focus at the
representation: Facts vs Definitions vs Opinions Temporality Spatiality Modality Polarity Rhetorical structures …
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Lightweight Semantic Representation
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Objective•Provide a lightweight knowledge representation model which: Can represent textual discourse
information.• Maximizes the capture of textual information.
Is convenient to extract from text. Is convenient to access (query and
browse).13
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Lightweight Semantic Representation
Representing Texts as Contextualized Entity-Centric Linked Data Graphs, WebS 2013
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Representation Assumptions• Data integration:
Named entities (instances) Abstract classes (unary predicates)
• Rich taxonomical structures.
• Context representation as a first class citizen.
• Open vocabulary.
• Word instead of sense/concept.15
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Representation Assumptions• Data integration:
Named entities (instances) Abstract classes (unary predicates)
• Rich taxonomical structures.
• Context representation as a first class citizen.
• Open vocabulary.
• Word instead of sense/concept.16
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Representation of Complex RelationsGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
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Data Integration pointsGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Named entities are lower entropy integration points Pivot
points18
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Data Integration pointsGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Named entities are also low entropy entry points for answering queries Pivot
points19
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Data Integration pointsGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Also abstract classes … Pivot
points20
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Data Integration pointsGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
They are also a very convenient way to represent. Pivot
points21
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Representation Assumptions• Data integration:
Named entities (instances) Abstract classes (unary predicates)
• Rich taxonomical structures.
• Context representation as a first class citizen.
• Open vocabulary.
• Word instead of sense/concept.22
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Taxonomy Extraction Are predicates with more complex compositional patterns
which describe sets.
Parsing complex nominals.
American multinational conglomerate corporation
On the Semantic Representation and Extraction of Complex Category Descriptors, NLDB 2014
multinational conglomerate corporation
corporation
conglomerate corporation
is a
is a
is a
Pivot points
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Representation Assumptions• Data integration:
Named entities (instances) Abstract classes (unary predicates)
• Rich taxonomical structures.
• Context representation as a first class citizen.
• Open vocabulary.
• Word instead of sense/concept.24
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Context RepresentationGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Reification as a first class representation element
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Context RepresentationGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Temporality, spatiality, modality, rhetorical relations …
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Rhetorical Structures using Reification• cause:
e.g. “because scraping the bottom with a metal utensil will scratch the surface.”
• circumstance e.g. “After completing your operating system reinstallation,”
• concession e.g. “Although the hotel is situated adjacent to a beach,”
• condition e.g. “If you can break the $ 1000 dollar investment range,”
• contrast e.g. “but you can do better with 2.4ghz or 900mhz phones.”
• purpose e.g.“in order for the rear passengers to get in the vehicle.”
• …27
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Representation Assumptions• Data integration:
Named entities (instances) Abstract classes (unary predicates)
• Rich taxonomical structures.
• Context representation as a first class citizen.
• Open vocabulary.
• Word instead of sense/concept.28
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Open VocabularyGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Temporality, spatiality, modality, rhetorical relations …
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Open Vocabulary
•Easier to extract but difficult to consume.
•We pay the price at query time.
•How to operate over a large-scale semantically heterogeneous knowledge-graphs?
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Representation Assumptions• Data integration:
Named entities (instances) Abstract classes (unary predicates)
• Rich taxonomical structures.
• Context representation as a first class citizen.
• Open vocabulary.
• Word instead of sense/concept.31
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Words instead of Senses•Motivation: Disambiguation is a tough
problem.
•Sense granularity can be, at many situations, arbitrary (too context dependent).
•We treat a word as a superposition of senses, almost in a “quantum mechanical sense”. 32
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Sens
e Su
perp
ositi
on
Coecke et al. (2010): Category theory and Lambek calculus.
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Revisited RDF (for Representing Texts)• Data Model Types: Instance, Class, Property…
• RDFS: Taxonomic representation.
• Reification for contextual relations (subordinations).
• Blank nodes for n-ary relations.
• Triple.
• Labels over URIs.34
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Abstract Meaning Representations – AMR,Maximal Use of PropBank Frame Files
Alternative Representations
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Distributional Semantics
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Distributional Semantic Models Semantic Model with low acquisition effort
(automatically built from text)
Simplification of the representation
Enables the construction of comprehensive commonsense/semantic KBs
What is the cost?
Some level of noise(semantic best-effort)
Limited semantic model37
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Distributional Semantics as Commonsense Knowledge
Commonsense is here
θ
car
dog
cat
bark
run
leashSemantic Approximation is
here
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I find it rather odd that people are already trying to tie the Commission's hands in relation to the proposal for a directive, while at the same calling on it to present a Green Paper on the current situation with regard to optional and supplementary health insurance schemes.
I find it a little strange to now obliging the Commission to a motion for a resolution and to ask him at the same time to draw up a Green Paper on the current state of voluntary insurance and supplementary sickness insurance.
=?
Beyond Single Word Vector Models: Compositionality
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Compositional Semantics Can we extend DS to account for the
meaning of phrases and sentences? Compositionality: The meaning of a
complex expression is a function of the meaning of its constituent parts.
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Compositional Semantics
Words in which the meaning is directly determined by their distributional behaviour (e.g., nouns).
Words that act as functions transforming the distributional profile of other words (e.g., verbs, adjectives, …).
dogs
old
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Compositional-Distributional Semantics
NLP & Semantic Computing Group Recursive Neural Networks for Structure Prediction
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New Model: Recursive Neural Tensor Network•Goal: Function that composes two vectors.•More expressive than any other RNN so far.
44 Socher et al.
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Socher et al.
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Compositional-distributional model for Categories
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Embedding Knowledge Graphs
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The vector space is segmented48
Dimensional reduction mechanism!
A Distributional Structured Semantic Space for Querying RDF Graph Data, IJSC 2012
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Compositional-distributional model for paraphrases
A Compositional-Distributional Semantic Model for Searching Complex Entity Categories, *SEM (2016)
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Knowledge Graph Extraction from Text
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Graphene
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Graph Extraction Pipeline
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
RST Classificati
on
ML-based
Rule-based
Rule-based
ML-based
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Minimalistic Text Transformations
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
RST Classificati
on
ML-based
Rule-based
Rule-based
ML-based
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Minimalistic Text Transformations
•Co-reference Resolution Pronominal co-references.
•Passive We have been approached by the investment
banker. The investment banker approached us.
•Genitive modifier Malaysia's crude palm oil output is estimated
to have risen. The crude palm oil output of Malasia is
estimated to have risen.54
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Text Simplification
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
RST Classificati
on
ML-based
Rule-based
Rule-based
ML-based
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Text Simplification for KG Extraction“Defeating Republican nominee Mitt Romney, Obama, who was the first African American to hold the office, was reelected president in November 2012.”
relations are spread across clauses relations are presented in non-canonical form
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Text Simplification for KG Extraction
•Insertion of a text simplification step
Obama was reelected president in November 2012.
Obama was the first African American to hold the office.
Obama was defeating Mitt Romney. Mitt Romney was Republican nominee.
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Syntax-driven sentence simplification approachTask:
• Reduce the linguistic complexity of a text while retaining the original information/meaning using a set of syntax-based rewrite operations (deletion, insertion, reordering, sentence splitting).
Idea:• Simplify a sentence by separating out components
that supply only secondary information into simpler stand-alone context sentences, thus yielding one or more reduced core sentences.
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Approach• Linguistic analysis of sentences from the English Wikipedia to identify constructs which provide only secondary information:
• non-restrictive relative clauses• non-restrictive and restrictive appositive phrases• participial phrases offset by commas• adjective and adverb phrases delimited by punctuation• particular prepositional phrases• lead noun phrases• intra-sentential attributions• parentheticals• conjoined clauses with specific features• particular punctuation
•Rule-based simplification rules.
Improving Relation Extraction by Syntax-based Sentence Simplification (2016)
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N-ary Relation Extraction
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
RST Classificati
on
Rule-based
Rule-based
ML-based
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OpenIE, University of Washington
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Taxonomy Extraction
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
RST Classificati
on
Rule-based
Rule-based
ML-based
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Representation and Extraction of Complex Category Descriptors, NLDB 2014
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RST Classification
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
RST Classificati
on
Rule-based
Rule-based
ML-based
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Rhetorical Structure Extraction
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TEXT-LEVEL RST-STYLE DISCOURSE PARSER (Feng and Hirst, 2012)
Structure classification
Relation classification
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Answering Queries with Knowledge Graphs
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Now our graph supports semantic approximations as a first-class operation
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Approach Overview
Query Planner
Ƭ-Space(embedding
graphs)
WikipediaCommonsense
knowledge
RDF
Explicit Semantic Analysis
Core semantic approximation &
composition operations
Query AnalysisQuery Query Features
Query Plan
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Core Principles Minimize the impact of Ambiguity, Vagueness,
Synonymy. Address the simplest matchings first (semantic
pivoting).
Semantic Relatedness as a primitive operation.
Distributional semantics models as commonsense knowledge representation.
Lightweight syntactic constraints.67
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•Step 2: Query NER Rules-based: POS Tag + IDF
Who is the daughter of Bill Clinton married to?(PROBABLY AN INSTANCE)
Query Pre-Processing (Question Analysis)
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•Step 3: Determine answer type Rules-based.
Who is the daughter of Bill Clinton married to? (PERSON)
Query Pre-Processing (Question Analysis)
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•Transform natural language queries into a pseudo-logical form.
“Who is the daughter of Bill Clinton married to?”
Query Pre-Processing (Question Analysis)
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Query Pre-Processing (Question Analysis)
Bill Clinton
daughter married to
(INSTANCE)
Person
ANSWER TYPE
QUESTION FOCUS71
• Step 5: Determine the query pattern Rules based.• Remove stop words.• Merge words into entities.• Reorder structure from core entity position.
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• Step 5: Determine the query pattern Rules based.• Remove stop words.• Merge words into entities.• Reorder structure from core entity position.
Query Pre-Processing (Question Analysis)
Bill Clinton
daughter married to
(INSTANCE)
Person
(PREDICATE) (PREDICATE) Query Features
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• Map query features into a query plan.• A query plan contains a sequence of:
Search operations. Navigation operations.
Query Planning
(INSTANCE) (PREDICATE) (PREDICATE) Query Features
(1) INSTANCE SEARCH (Bill Clinton) (2) DISAMBIGUATE ENTITY TYPE (3) GENERATE ENTITY FACETS (4) p1 <- SEARCH RELATED PREDICATE (Bill Clintion, daughter) (5) e1 <- GET ASSOCIATED ENTITIES (Bill Clintion, p1) (6) p2 <- SEARCH RELATED PREDICATE (e1, married to) (7) e2 <- GET ASSOCIATED ENTITIES (e1, p2) (8) POST PROCESS (Bill Clintion, e1, p1, e2, p2)
Query Plan
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Core Entity SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
KB:
Entity search
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Distributional Semantic SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
:Baptists:religion
:Yale_Law_School:almaMater...
(PIVOT ENTITY)
(ASSOCIATED TRIPLES)
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KB:
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Distributional Semantic SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
:Baptists:religion
:Yale_Law_School:almaMater...
sem_rel(daughter,child)=0.054
sem_rel(daughter,child)=0.004
sem_rel(daughter,alma mater)=0.001
Which properties are semantically related to ‘daughter’?
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KB:
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Distributional Semantic SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
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KB:
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Distributional Semantic SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
(PIVOT ENTITY)
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KB:
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Distributional Semantic SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child:Mark_Mezvinsky
:spouse
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KB:
Note the lazy disambiguation
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What is the highest mountain?Second Query Example
(CLASS) (OPERATOR) Query Features
mountain - highest PODS
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Entity SearchMountain highest
:Mountain
Query:
:typeOf
(PIVOT ENTITY)
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KB:
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Extensional ExpansionMountain highest
:Mountain
Query:
:Everest:typeOf
(PIVOT ENTITY)
:K2:typeOf
...
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KB:
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Distributional Semantic MatchingMountain highest
:Mountain
Query:
:Everest:typeOf
(PIVOT ENTITY)
:K2:typeOf
...
:elevation
:location...:deathPlaceOf
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KB:
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Get all numerical valuesMountain highest
:Mountain
Query:
:Everest:typeOf
(PIVOT ENTITY)
:K2:typeOf
...
:elevation
:elevation
8848 m
8611 m
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KB:
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Apply operator functional definitionMountain highest
:Mountain
Query:
:Everest:typeOf
(PIVOT ENTITY)
:K2:typeOf
...
:elevation
:elevation
8848 m
8611 m
SORTTOP_MOST
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KB:
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Results
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StarGraph•Open source NoSQL platform for building
and interacting with large and sparse knowledge graphs.
•Semantic approximation as a built-in operation.
•Scalable query execution performance.
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Heuristics for the selection of the semantic pivot is critical!•Discussed here just superficially:
Information-theoretical justification.
How hard is the Query? Measuring the Semantic Complexity of Schema-Agnostic Queries, IWCS (2015).
Schema-agnositc queries over large-schema databases: a distributional semantics approach, PhD Thesis (2015).
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study, NLIWoD (2015).
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Indra Multilingual platform for experimentation
with different word vector models.
"Indra's net" is the net of the Vedic god Indra, whose net hangs over his palace on Mount Meru, the axis mundi of Hindu cosmology and Hindu mythology. Indra's net has a multifaceted jewel at each vertex, and each jewel is reflected in all of the other jewels.
In the Avatamsaka Sutra, the image of "Indra's net" is used to describe the interconnectedness of the universe.
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Indra
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Bridging Structured & Unstructured Data•NER + Text + Passage Retrieval Ranking
Simple and powerful QA basis.
•Lazy disambiguation.
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Treo Answers Jeopardy Queries (Video)
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Ranking Candidate Answers•But what if there are multiple candidate answers!
Q: Who was Queen Victoria’s second son?•Answer Type: Person
• Passage:The Marie biscuit is named after Marie Alexandrovna, the daughter of Czar Alexander II of Russia and wife of Alfred, the second son of Queen Victoria and Prince Albert
96Dan Jurafky’s slides
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Ranking Candidate Answers•But what if there are multiple candidate answers!
Q: Who was Queen Victoria’s second son?•Answer Type: Person
• Passage:The Marie biscuit is named after Marie Alexandrovna, the daughter of Czar Alexander II of Russia and wife of Alfred, the second son of Queen Victoria and Prince Albert
97Dan Jurafky’s slides
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Feature Engineering
The Marie biscuit is named after Marie Alexandrovna, the daughter of Czar Alexander II of Russia and wife of Alfred, the second son of Queen Victoria and Prince Albert
followed by a ‘,’ followed by an apposition
Who was Queen Victoria’s second son?
contains an entity in the query
has a four-word overlap
type = PERSON
matches AnswerType
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Propositionalisation
e0 followedBy(,) followedByAppositionContainingQueryEntities() answer …
Alfred true true true… … …
passage
entity (e0)
entity (en)
…
The Marie biscuit is named after Marie Alexandrovna, the daughter of Czar Alexander II of Russia and wife of Alfred, the second son of Queen Victoria and Prince Albert
answer
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Reasoning for Text Entailment
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Beyond Word Vector Models
give birth mother
car
θ
Distributional semantics can give us a hint about the concepts’ semantic proximity...
...but it still can’t tell us what exactly the relationship between them is
give birth
mother???
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Beyond Word Vector Models
give birth
mother???
give birth
mother???
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Beyond Word Vector Models: Intensional Reasoning
Representing structured intensional-level knowledge.
Creation of an intensional-level reasoning model.
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Commonsense Reasoning
Selective (focussed) reasoning - Selecting the relevant facts in the
context of the inference
Reducing the search space.Scalability
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Extended WordNet (XWN)
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http://conceptnet5.media.mit.edu/
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Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
108
target
source answer
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Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
109
target
source answer
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Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
110
target
source answer
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John Smith
EngineerInstance-level
occupation
Does John Smith have a degree?
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A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases, NLDB (2015).
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Bringing it into the Real World
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Semeval 2017
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Take-away Message• Choosing the sweet-spot in terms of semantic
representation is critical for the construction of robust QA systems.
Work at a word-based representation instead of a sense representation.
Text simplification/clausal disembedding critical for relation extraction.
Need for a standardized semantic representation for relations extracted from texts.
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Take-away Message•Text entailment:
Intensional-level reasoning. Natural logic. Distributional semantics.
•Distributional semantics: Robust, language-agnostic semantic
matching. Selective reasoning over commonsense
KBs.