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NLP & Semantic Computing Group
N L P
Semantic Perspectives for
Contemporary Question Answering Systems
Andre Freitas
University of Passau
JAIST, December 2016
NLP & Semantic Computing Group
Outline
 Multiple Perspectives of Semantic Representation
 Lightweight Semantic Representation
 Knowledge Graph Extraction from Text
 Querying Knowledge Graphs
 Text Entailment Reasoning
 Take-away Message
NLP & Semantic Computing Group
Multiple Perspectives of
Semantic Representation
NLP & Semantic Computing Group
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.
4
NLP & Semantic Computing Group
 “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
5
NLP & Semantic Computing Group
Representation focal points
• Types of knowledge to focus at the
representation:
 Facts vs Definitions
 Temporality
 Spatiality
 Modality
 Polarity
 Rhetorical structures
 Pragmatic categories
 …
6
NLP & Semantic Computing Group
Lightweight Semantic
Representation
NLP & Semantic Computing Group
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).
8
NLP & Semantic Computing Group
Representation of Contextual Relations
(Facts)
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
9
Factoid shape
NLP & Semantic Computing Group
RDF as the basic data model
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
Instance
10
Instance Instance
Class
Property
NLP & Semantic Computing Group
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.
11
NLP & Semantic Computing Group
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.
12
NLP & Semantic Computing Group
Data Integration points
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
Named entities are lower
entropy integration points
Pivot
points13
NLP & Semantic Computing Group
Data Integration points
General 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
points14
NLP & Semantic Computing Group
Data Integration points
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
Also abstract classes …
Pivot
points15
NLP & Semantic Computing Group
Data Integration points
General 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
points16
NLP & Semantic Computing Group
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.
17
NLP & Semantic Computing Group
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
NLP & Semantic Computing Group
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.
19
NLP & Semantic Computing Group
Context Representation
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
Reification as a first class
representation element
20
NLP & Semantic Computing Group
Context Representation
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
Temporality, spatiality,
modality, rhetorical relations
…
21
NLP & Semantic Computing Group
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
NLP & Semantic Computing Group
Open Vocabulary
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
Temporality, spatiality,
modality, rhetorical relations
…
23
NLP & Semantic Computing Group
Open Vocabulary
• Easier to extract but more difficult to consume.
• We pay the price at query time.
• How to operate over a large-scale semantically
heterogeneous knowledge-graphs?
24
NLP & Semantic Computing Group
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.
25
NLP & Semantic Computing Group
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”.
26
NLP & Semantic Computing Group
Revisited RDF (for Representing Texts)
• Triple as the basic fact unit.
• Data Model Types: Instance, Class, Property…
• RDFS: Taxonomic representation.
• Reification for contextual relations (subordinations).
• Blank nodes for n-ary relations.
• Labels over URIs.
27
NLP & Semantic Computing Group
Lightweight Semantic Representation
Representing Texts as Contextualized Entity-Centric
Linked Data Graphs, WebS 2013
NLP & Semantic Computing Group
Distributional
Semantics
NLP & Semantic Computing Group
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 model
30
NLP & Semantic Computing Group
Distributional Semantics as Commonsense Knowledge
Commonsense is here
θ
car
dog
cat
bark
run
leash
Semantic Approximation is here
31
NLP & Semantic Computing Group
Distributional-Relational Networks
Distributional Relational Networks, AAAI
Symposium, 2013
NLP & Semantic Computing Group
The vector space is
segmented33
Dimensional reduction
mechanism!
A Distributional Structured Semantic Space for
Querying RDF Graph Data, IJSC 2012
NLP & Semantic Computing Group
Compositionality of Complex Nominals
NLP & Semantic Computing Group
Compositional-distributional model for
Categories
35
NLP & Semantic Computing Group
Compositional-distributional model for
paraphrases
A Compositional-Distributional Semantic Model for
Searching Complex Entity Categories, *SEM (2016)
NLP & Semantic Computing Group
Knowledge Graph Extraction from Text
NLP & Semantic Computing Group
Graphene
NLP & Semantic Computing Group
Graph Extraction Pipeline
Text
Transformation
N-ary
Relation
Extraction
Text
Simplification
Graph
Serialization
Taxonomy
Extraction
Storage
ML-based
Rule-based
Rule-based
ML-based
39
Argumentation
Classification
NLP & Semantic Computing Group
Minimalistic Text Transformations
Text
Transformation
N-ary
Relation
Extraction
Text
Simplification
Graph
Serialization
Taxonomy
Extraction
Storage
ML-based
Rule-based
Rule-based
ML-based
40
Argumentation
Classification
NLP & Semantic Computing Group
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. 41
NLP & Semantic Computing Group
Text Simplification
Text
Transformation
N-ary
Relation
Extraction
Text
Simplification
Graph
Serialization
Taxonomy
Extraction
Storage
ML-based
Rule-based
Rule-based
ML-based
42
Argumentation
Classification
NLP & Semantic Computing Group
Text Simplification for KG
Extraction
“A few hours later, Matthias Goerne, a German
baritone, offered an all-German program at the
Frick Collection.”
 relations are spread across clauses
 relations are presented in non-canonical form
43
NLP & Semantic Computing Group
Text Simplification for KG
Extraction
44
NLP & Semantic Computing Group
NLP & Semantic Computing Group
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.
A Sentence Simplification System for Improving
Open Relation Extraction COLING (2016)
NLP & Semantic Computing Group
N-ary Relation Extraction
Text
Transformation
N-ary
Relation
Extraction
Text
Simplification
Graph
Serialization
Taxonomy
Extraction
Storage
Rule-based
Rule-based
ML-based
47
OpenIE, University of Washington
Argumentation
Classification
NLP & Semantic Computing Group
Taxonomy Extraction
Text
Transformation
N-ary
Relation
Extraction
Text
Simplification
Graph
Serialization
Taxonomy
Extraction
Storage
Rule-based
Rule-based
ML-based
48
Representation and Extraction of Complex
Category Descriptors, NLDB 2014
Argumentation
Classification
NLP & Semantic Computing Group
RST Classification
Text
Transformation
N-ary
Relation
Extraction
Text
Simplification
Graph
Serialization
Taxonomy
Extraction
Storage
Argumentation
Classification
Rule-based
Rule-based
ML-based
49
NLP & Semantic Computing Group
Rhetorical Structure Theory
• 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.”
• …
50
NLP & Semantic Computing Group
Argumentation Representation
• Supports/Attack
• Rhetorical Structure Theory (RSTs)
• Informal Logic
• Argumentation Schemes (Walton et al.)
• Pragmatic Categories
Retrieval
Reasoning
NLP & Semantic Computing Group
Querying
Knowledge Graphs
NLP & Semantic Computing Group
With DSMs our graph supports semantic
approximations as a first-class operation
NLP & Semantic Computing Group
Approach Overview
Query Planner
Ƭ-Space
(embedding graphs)
Commonsense
knowledge
RDF
Core semantic approximation &
composition operations
Query AnalysisQuery Query Features
Query Plan
54
Corpus
NLP & Semantic Computing Group
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.
55
NLP & Semantic Computing Group
• Now let’s answer the query
“Who is the daughter of Bill Clinton married to?”
Question
56
NLP & Semantic Computing Group
• Step 1: Determine answer type
Who is the daughter of Bill Clinton married to?
(PERSON)
• Using POS Tags
Query Pre-Processing
(Question Analysis)
57
NLP & Semantic Computing Group
• Step 2: Semantic role labeling.
Who is the daughter of Bill Clinton married to?
• NER, POS Tags
 Rules-based: POS Tag + IDF
Query Pre-Processing
(Question Analysis)
58
(INSTANCE) (PROPERT
Y)
(PROPERT
Y)
(CLASS)
(PERSON)
NLP & Semantic Computing Group
Query Pre-Processing
(Question Analysis)
Bill Clinton daughter married to
(INSTANCE)
Person
ANSWER
TYPE
QUESTION FOCUS
59
• Step 3: Put in a structured pseudo-logical form
 Rules based.
• Remove stop words.
• Merge words into entities.
• Reorder structure from core entity position.
NLP & Semantic Computing Group
• Step 3: Put in a structured pseudo-logical form
 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
60
NLP & Semantic Computing Group
• Map query features into a query plan.
• A query plan contains a sequence of:
 Search operations.
 Selection 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
61
NLP & Semantic Computing Group
Core Entity Search
Bill Clinton daughter married to Person
:Bill_Clinton
Query:
KB:
Entity search
62
NLP & Semantic Computing Group
Distributional Semantic Search
Bill Clinton daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
:Baptists
:religion
:Yale_Law_School
:almaMater
...
(PIVOT ENTITY)
(ASSOCIATED
TRIPLES)
63
KB:
NLP & Semantic Computing Group
Distributional Semantic Search
Bill 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’?
64
KB:
NLP & Semantic Computing Group
Distributional Semantic Search
Bill Clinton daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
65
KB:
NLP & Semantic Computing Group
Distributional Semantic Search
Bill Clinton daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
(PIVOT ENTITY)
66
KB:
NLP & Semantic Computing Group
Distributional Semantic Search
Bill Clinton daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
:Mark_Mezvinsky
:spouse
67
KB:
Note the lazy
disambiguation
NLP & Semantic Computing Group
68
NLP & Semantic Computing Group
Relevance
Medium-high query
expressivity / coverage
69
Accurate semantic
matching for a semantic
best-effort scenario
Ranking in the second
position in average
NLP & Semantic Computing Group
Comparative Analysis
 Better recall and query coverage compared to baselines with
equivalent precision.
 More comprehensive semantic matching.
70
NLP & Semantic Computing Group
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.
71
NLP & Semantic Computing Group
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).
72
NLP & Semantic Computing Group
Reasoning for Text Entailment
NLP & Semantic Computing Group
Beyond Word Vector Models
engineer
degree
university
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
engineer
degree
???
NLP & Semantic Computing Group
Beyond Word Vector Models
engineer
degree
???
engineer
degree
???
NLP & Semantic Computing Group
Beyond Word Vector Models:
Intensional Reasoning
 Representing structured intensional-level
knowledge.
 Creation of an intensional-level reasoning
model.
76
NLP & Semantic Computing Group
Commonsense Reasoning
 Selective (focussed) reasoning
 Selecting the relevant facts in the context of the
inference
 Reducing the search space.
Scalability
NLP & Semantic Computing Group
Distributional semantic relatedness as a
Selectivity Heuristics
Distributional
heuristics
78
target
source
answer
NLP & Semantic Computing Group
Distributional semantic relatedness as a
Selectivity Heuristics
Distributional
heuristics
79
target
source
answer
NLP & Semantic Computing Group
Distributional semantic relatedness as a
Selectivity Heuristics
Distributional
heuristics
80
target
source
answer
NLP & Semantic Computing Group
John Smith EngineerInstance-level
occupation
Does John Smith have a degree?
NLP & Semantic Computing Group
NLP & Semantic Computing Group
NLP & Semantic Computing Group
NLP & Semantic Computing Group
NLP & Semantic Computing Group
NLP & Semantic Computing Group
NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
A Distributional Semantics Approach for
Selective Reasoning on Commonsense Graph
Knowledge Bases, NLDB (2015).
NLP & Semantic Computing Group
Intensional-level representation
• Dictionary definitions
 refinement: a highly developed state of perception
state perfection
differentia
quality
developed highly
quality modifier
differentia
quality
refinement
is a
NLP & Semantic Computing Group
Annotating and Structuring WordNet
Glosses
• lake_poets:
• refinement:
• redundancy:
• slender_salamander:
• genus_Salix:
• unstaple:
NLP & Semantic Computing Group
Semantic Roles for Lexical Definitions
Aristotle’s classic theory of definition introduced important
aspects such as the genus-differentia definition pattern and
the essential/non-essential property differentiation. Taking
those principles as starting point and analyzing a sample of randomly chosen
WordNet’s definitions, we derived the following semantic roles for definitions:
origin
location
[role]
particle
accessory
determiner
accessory
quality
associated
fact
purpose
quality
modifier
event
location
event time
differentia
event
differentia
quality
supertype
definiendum
has particle
modified by
has component
characterizedby
has type
addsnon-essentialinfoto
NLP & Semantic Computing Group
Bringing it into the Real World
NLP & Semantic Computing Group
Semeval 2017
NLP & Semantic Computing Group
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.
 Representation needs to be convenient for information
extraction and data consumers.
NLP & Semantic Computing Group
Take-away Message
• Distributional semantics:
 Robust, language-agnostic semantic matching.
 Semantic pivoting strategy.
 Selective reasoning over commonsense KBs.
• Need to move to more fine-grained
models:
 Robust intensional-level reasoning.
NLP & Semantic Computing Group
Take-away Message
• Role of Machine Learning:
 Fundamental to cope with the long tail of
linguistic phenomena.
 More explicit interplay with convenient semantic
representation models.
 Interpretability/explanation over accuracy.
NLP & Semantic Computing Group
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Semantic Perspectives for Contemporary Question Answering Systems

  • 1. NLP & Semantic Computing Group N L P Semantic Perspectives for Contemporary Question Answering Systems Andre Freitas University of Passau JAIST, December 2016
  • 2. NLP & Semantic Computing Group Outline  Multiple Perspectives of Semantic Representation  Lightweight Semantic Representation  Knowledge Graph Extraction from Text  Querying Knowledge Graphs  Text Entailment Reasoning  Take-away Message
  • 3. NLP & Semantic Computing Group Multiple Perspectives of Semantic Representation
  • 4. NLP & Semantic Computing Group 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. 4
  • 5. NLP & Semantic Computing Group  “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 5
  • 6. NLP & Semantic Computing Group Representation focal points • Types of knowledge to focus at the representation:  Facts vs Definitions  Temporality  Spatiality  Modality  Polarity  Rhetorical structures  Pragmatic categories  … 6
  • 7. NLP & Semantic Computing Group Lightweight Semantic Representation
  • 8. NLP & Semantic Computing Group 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). 8
  • 9. NLP & Semantic Computing Group Representation of Contextual Relations (Facts) General Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York 9 Factoid shape
  • 10. NLP & Semantic Computing Group RDF as the basic data model General Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York Instance 10 Instance Instance Class Property
  • 11. NLP & Semantic Computing Group 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. 11
  • 12. NLP & Semantic Computing Group 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. 12
  • 13. NLP & Semantic Computing Group Data Integration points General Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York Named entities are lower entropy integration points Pivot points13
  • 14. NLP & Semantic Computing Group Data Integration points General 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 points14
  • 15. NLP & Semantic Computing Group Data Integration points General Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York Also abstract classes … Pivot points15
  • 16. NLP & Semantic Computing Group Data Integration points General 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 points16
  • 17. NLP & Semantic Computing Group 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. 17
  • 18. NLP & Semantic Computing Group 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
  • 19. NLP & Semantic Computing Group 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. 19
  • 20. NLP & Semantic Computing Group Context Representation General Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York Reification as a first class representation element 20
  • 21. NLP & Semantic Computing Group Context Representation General Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York Temporality, spatiality, modality, rhetorical relations … 21
  • 22. NLP & Semantic Computing Group 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
  • 23. NLP & Semantic Computing Group Open Vocabulary General Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York Temporality, spatiality, modality, rhetorical relations … 23
  • 24. NLP & Semantic Computing Group Open Vocabulary • Easier to extract but more difficult to consume. • We pay the price at query time. • How to operate over a large-scale semantically heterogeneous knowledge-graphs? 24
  • 25. NLP & Semantic Computing Group 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. 25
  • 26. NLP & Semantic Computing Group 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”. 26
  • 27. NLP & Semantic Computing Group Revisited RDF (for Representing Texts) • Triple as the basic fact unit. • Data Model Types: Instance, Class, Property… • RDFS: Taxonomic representation. • Reification for contextual relations (subordinations). • Blank nodes for n-ary relations. • Labels over URIs. 27
  • 28. NLP & Semantic Computing Group Lightweight Semantic Representation Representing Texts as Contextualized Entity-Centric Linked Data Graphs, WebS 2013
  • 29. NLP & Semantic Computing Group Distributional Semantics
  • 30. NLP & Semantic Computing Group 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 model 30
  • 31. NLP & Semantic Computing Group Distributional Semantics as Commonsense Knowledge Commonsense is here θ car dog cat bark run leash Semantic Approximation is here 31
  • 32. NLP & Semantic Computing Group Distributional-Relational Networks Distributional Relational Networks, AAAI Symposium, 2013
  • 33. NLP & Semantic Computing Group The vector space is segmented33 Dimensional reduction mechanism! A Distributional Structured Semantic Space for Querying RDF Graph Data, IJSC 2012
  • 34. NLP & Semantic Computing Group Compositionality of Complex Nominals
  • 35. NLP & Semantic Computing Group Compositional-distributional model for Categories 35
  • 36. NLP & Semantic Computing Group Compositional-distributional model for paraphrases A Compositional-Distributional Semantic Model for Searching Complex Entity Categories, *SEM (2016)
  • 37. NLP & Semantic Computing Group Knowledge Graph Extraction from Text
  • 38. NLP & Semantic Computing Group Graphene
  • 39. NLP & Semantic Computing Group Graph Extraction Pipeline Text Transformation N-ary Relation Extraction Text Simplification Graph Serialization Taxonomy Extraction Storage ML-based Rule-based Rule-based ML-based 39 Argumentation Classification
  • 40. NLP & Semantic Computing Group Minimalistic Text Transformations Text Transformation N-ary Relation Extraction Text Simplification Graph Serialization Taxonomy Extraction Storage ML-based Rule-based Rule-based ML-based 40 Argumentation Classification
  • 41. NLP & Semantic Computing Group 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. 41
  • 42. NLP & Semantic Computing Group Text Simplification Text Transformation N-ary Relation Extraction Text Simplification Graph Serialization Taxonomy Extraction Storage ML-based Rule-based Rule-based ML-based 42 Argumentation Classification
  • 43. NLP & Semantic Computing Group Text Simplification for KG Extraction “A few hours later, Matthias Goerne, a German baritone, offered an all-German program at the Frick Collection.”  relations are spread across clauses  relations are presented in non-canonical form 43
  • 44. NLP & Semantic Computing Group Text Simplification for KG Extraction 44
  • 45. NLP & Semantic Computing Group
  • 46. NLP & Semantic Computing Group 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. A Sentence Simplification System for Improving Open Relation Extraction COLING (2016)
  • 47. NLP & Semantic Computing Group N-ary Relation Extraction Text Transformation N-ary Relation Extraction Text Simplification Graph Serialization Taxonomy Extraction Storage Rule-based Rule-based ML-based 47 OpenIE, University of Washington Argumentation Classification
  • 48. NLP & Semantic Computing Group Taxonomy Extraction Text Transformation N-ary Relation Extraction Text Simplification Graph Serialization Taxonomy Extraction Storage Rule-based Rule-based ML-based 48 Representation and Extraction of Complex Category Descriptors, NLDB 2014 Argumentation Classification
  • 49. NLP & Semantic Computing Group RST Classification Text Transformation N-ary Relation Extraction Text Simplification Graph Serialization Taxonomy Extraction Storage Argumentation Classification Rule-based Rule-based ML-based 49
  • 50. NLP & Semantic Computing Group Rhetorical Structure Theory • 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.” • … 50
  • 51. NLP & Semantic Computing Group Argumentation Representation • Supports/Attack • Rhetorical Structure Theory (RSTs) • Informal Logic • Argumentation Schemes (Walton et al.) • Pragmatic Categories Retrieval Reasoning
  • 52. NLP & Semantic Computing Group Querying Knowledge Graphs
  • 53. NLP & Semantic Computing Group With DSMs our graph supports semantic approximations as a first-class operation
  • 54. NLP & Semantic Computing Group Approach Overview Query Planner Ƭ-Space (embedding graphs) Commonsense knowledge RDF Core semantic approximation & composition operations Query AnalysisQuery Query Features Query Plan 54 Corpus
  • 55. NLP & Semantic Computing Group 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. 55
  • 56. NLP & Semantic Computing Group • Now let’s answer the query “Who is the daughter of Bill Clinton married to?” Question 56
  • 57. NLP & Semantic Computing Group • Step 1: Determine answer type Who is the daughter of Bill Clinton married to? (PERSON) • Using POS Tags Query Pre-Processing (Question Analysis) 57
  • 58. NLP & Semantic Computing Group • Step 2: Semantic role labeling. Who is the daughter of Bill Clinton married to? • NER, POS Tags  Rules-based: POS Tag + IDF Query Pre-Processing (Question Analysis) 58 (INSTANCE) (PROPERT Y) (PROPERT Y) (CLASS) (PERSON)
  • 59. NLP & Semantic Computing Group Query Pre-Processing (Question Analysis) Bill Clinton daughter married to (INSTANCE) Person ANSWER TYPE QUESTION FOCUS 59 • Step 3: Put in a structured pseudo-logical form  Rules based. • Remove stop words. • Merge words into entities. • Reorder structure from core entity position.
  • 60. NLP & Semantic Computing Group • Step 3: Put in a structured pseudo-logical form  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 60
  • 61. NLP & Semantic Computing Group • Map query features into a query plan. • A query plan contains a sequence of:  Search operations.  Selection 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 61
  • 62. NLP & Semantic Computing Group Core Entity Search Bill Clinton daughter married to Person :Bill_Clinton Query: KB: Entity search 62
  • 63. NLP & Semantic Computing Group Distributional Semantic Search Bill Clinton daughter married to Person :Bill_Clinton Query: :Chelsea_Clinton :child :Baptists :religion :Yale_Law_School :almaMater ... (PIVOT ENTITY) (ASSOCIATED TRIPLES) 63 KB:
  • 64. NLP & Semantic Computing Group Distributional Semantic Search Bill 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’? 64 KB:
  • 65. NLP & Semantic Computing Group Distributional Semantic Search Bill Clinton daughter married to Person :Bill_Clinton Query: :Chelsea_Clinton :child 65 KB:
  • 66. NLP & Semantic Computing Group Distributional Semantic Search Bill Clinton daughter married to Person :Bill_Clinton Query: :Chelsea_Clinton :child (PIVOT ENTITY) 66 KB:
  • 67. NLP & Semantic Computing Group Distributional Semantic Search Bill Clinton daughter married to Person :Bill_Clinton Query: :Chelsea_Clinton :child :Mark_Mezvinsky :spouse 67 KB: Note the lazy disambiguation
  • 68. NLP & Semantic Computing Group 68
  • 69. NLP & Semantic Computing Group Relevance Medium-high query expressivity / coverage 69 Accurate semantic matching for a semantic best-effort scenario Ranking in the second position in average
  • 70. NLP & Semantic Computing Group Comparative Analysis  Better recall and query coverage compared to baselines with equivalent precision.  More comprehensive semantic matching. 70
  • 71. NLP & Semantic Computing Group 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. 71
  • 72. NLP & Semantic Computing Group 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). 72
  • 73. NLP & Semantic Computing Group Reasoning for Text Entailment
  • 74. NLP & Semantic Computing Group Beyond Word Vector Models engineer degree university 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 engineer degree ???
  • 75. NLP & Semantic Computing Group Beyond Word Vector Models engineer degree ??? engineer degree ???
  • 76. NLP & Semantic Computing Group Beyond Word Vector Models: Intensional Reasoning  Representing structured intensional-level knowledge.  Creation of an intensional-level reasoning model. 76
  • 77. NLP & Semantic Computing Group Commonsense Reasoning  Selective (focussed) reasoning  Selecting the relevant facts in the context of the inference  Reducing the search space. Scalability
  • 78. NLP & Semantic Computing Group Distributional semantic relatedness as a Selectivity Heuristics Distributional heuristics 78 target source answer
  • 79. NLP & Semantic Computing Group Distributional semantic relatedness as a Selectivity Heuristics Distributional heuristics 79 target source answer
  • 80. NLP & Semantic Computing Group Distributional semantic relatedness as a Selectivity Heuristics Distributional heuristics 80 target source answer
  • 81. NLP & Semantic Computing Group John Smith EngineerInstance-level occupation Does John Smith have a degree?
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  • 97. NLP & Semantic Computing Group A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases, NLDB (2015).
  • 98. NLP & Semantic Computing Group Intensional-level representation • Dictionary definitions  refinement: a highly developed state of perception state perfection differentia quality developed highly quality modifier differentia quality refinement is a
  • 99. NLP & Semantic Computing Group Annotating and Structuring WordNet Glosses • lake_poets: • refinement: • redundancy: • slender_salamander: • genus_Salix: • unstaple:
  • 100. NLP & Semantic Computing Group Semantic Roles for Lexical Definitions Aristotle’s classic theory of definition introduced important aspects such as the genus-differentia definition pattern and the essential/non-essential property differentiation. Taking those principles as starting point and analyzing a sample of randomly chosen WordNet’s definitions, we derived the following semantic roles for definitions: origin location [role] particle accessory determiner accessory quality associated fact purpose quality modifier event location event time differentia event differentia quality supertype definiendum has particle modified by has component characterizedby has type addsnon-essentialinfoto
  • 101. NLP & Semantic Computing Group Bringing it into the Real World
  • 102. NLP & Semantic Computing Group Semeval 2017
  • 103. NLP & Semantic Computing Group 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.  Representation needs to be convenient for information extraction and data consumers.
  • 104. NLP & Semantic Computing Group Take-away Message • Distributional semantics:  Robust, language-agnostic semantic matching.  Semantic pivoting strategy.  Selective reasoning over commonsense KBs. • Need to move to more fine-grained models:  Robust intensional-level reasoning.
  • 105. NLP & Semantic Computing Group Take-away Message • Role of Machine Learning:  Fundamental to cope with the long tail of linguistic phenomena.  More explicit interplay with convenient semantic representation models.  Interpretability/explanation over accuracy.
  • 106. NLP & Semantic Computing Group http://www.slideshare.net/andrenfreitas These slides: