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NLP & Semantic Computing Group
N L P
Semantic Perspectives forContemporary Question Answering Systems
Andre FreitasUniversity of Passau
JAIST, December 2016
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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
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NLP & Semantic Computing Group
Multiple Perspectives of Semantic Representation
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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.
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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
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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 …
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NLP & Semantic Computing Group
Lightweight Semantic Representation
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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
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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
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Factoid shape
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NLP & Semantic Computing Group
RDF as the basic data modelGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Instance
10
Instance
Instance
Class
Property
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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
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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
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NLP & Semantic Computing Group
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
points13
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NLP & Semantic Computing Group
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
points14
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NLP & Semantic Computing Group
Data Integration pointsGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Also abstract classes … Pivot
points15
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NLP & Semantic Computing Group
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
points16
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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
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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
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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
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NLP & Semantic Computing Group
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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NLP & Semantic Computing Group
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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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
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NLP & Semantic Computing Group
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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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?
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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
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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
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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
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NLP & Semantic Computing Group
Lightweight Semantic Representation
Representing Texts as Contextualized Entity-Centric Linked Data Graphs, WebS 2013
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NLP & Semantic Computing Group
Distributional Semantics
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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 model30
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NLP & Semantic Computing Group
Distributional Semantics as Commonsense Knowledge
Commonsense is here
θ
car
dog
cat
bark
run
leashSemantic Approximation is
here
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NLP & Semantic Computing Group
Distributional-Relational Networks
Distributional Relational Networks, AAAI Symposium, 2013
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NLP & Semantic Computing Group
The vector space is segmented33
Dimensional reduction mechanism!
A Distributional Structured Semantic Space for Querying RDF Graph Data, IJSC 2012
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NLP & Semantic Computing Group
Compositionality of Complex Nominals
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NLP & Semantic Computing Group
Compositional-distributional model for Categories
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NLP & Semantic Computing Group
Compositional-distributional model for paraphrases
A Compositional-Distributional Semantic Model for Searching Complex Entity Categories, *SEM (2016)
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NLP & Semantic Computing Group
Knowledge Graph Extraction from Text
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NLP & Semantic Computing Group
Graphene
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NLP & Semantic Computing Group
Graph Extraction Pipeline
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
ML-based
Rule-based
Rule-based
ML-based
39
Argumentation Classification
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NLP & Semantic Computing Group
Minimalistic Text Transformations
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
ML-based
Rule-based
Rule-based
ML-based
40
Argumentation Classification
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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
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NLP & Semantic Computing Group
Text Simplification
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
ML-based
Rule-based
Rule-based
ML-based
42
Argumentation Classification
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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
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NLP & Semantic Computing Group
Text Simplification for KG Extraction
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NLP & Semantic Computing Group
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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)
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NLP & Semantic Computing Group
N-ary Relation Extraction
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
Rule-based
Rule-based
ML-based
47
OpenIE, University of Washington
Argumentation Classification
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NLP & Semantic Computing Group
Taxonomy Extraction
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
Rule-based
Rule-based
ML-based
48
Representation and Extraction of Complex Category Descriptors, NLDB 2014
Argumentation Classification
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NLP & Semantic Computing Group
RST Classification
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
Argumentation Classification
Rule-based
Rule-based
ML-based
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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
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NLP & Semantic Computing Group
Argumentation Representation•Supports/Attack•Rhetorical Structure Theory (RSTs)
•Informal Logic•Argumentation Schemes (Walton et al.)•Pragmatic Categories
Retrieval
Reasoning
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NLP & Semantic Computing Group
QueryingKnowledge Graphs
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NLP & Semantic Computing Group
With DSMs our graph supports semantic approximations as a first-class operation
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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
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Corpus
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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
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NLP & Semantic Computing Group
•Now let’s answer the query
“Who is the daughter of Bill Clinton married to?”
Question
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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)
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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) (PROPERTY)
(PROPERTY)
(CLASS)
(PERSON)
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NLP & Semantic Computing Group
Query Pre-Processing (Question Analysis)
Bill Clinton
daughter married to
(INSTANCE)
Person
ANSWER TYPE
QUESTION FOCUS59
• Step 3: Put in a structured pseudo-logical form Rules based.• Remove stop words.• Merge words into entities.• Reorder structure from core entity position.
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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
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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
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NLP & Semantic Computing Group
Core Entity SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
KB:
Entity search
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NLP & Semantic Computing Group
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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NLP & Semantic Computing Group
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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NLP & Semantic Computing Group
Distributional Semantic SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
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NLP & Semantic Computing Group
Distributional Semantic SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child
(PIVOT ENTITY)
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NLP & Semantic Computing Group
Distributional Semantic SearchBill Clinton
daughter married to Person
:Bill_Clinton
Query:
:Chelsea_Clinton
:child:Mark_Mezvinsky
:spouse
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Note the lazy disambiguation
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NLP & Semantic Computing Group
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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
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NLP & Semantic Computing Group
Comparative Analysis
Better recall and query coverage compared to baselines with equivalent precision.
More comprehensive semantic matching.
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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.
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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).
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NLP & Semantic Computing Group
Reasoning for Text Entailment
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NLP & Semantic Computing Group
Beyond Word Vector Models
engineer degree
universityθ
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???
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NLP & Semantic Computing Group
Beyond Word Vector Models
engineer
degree???
engineer
degree???
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NLP & Semantic Computing Group
Beyond Word Vector Models: Intensional Reasoning
Representing structured intensional-level knowledge.
Creation of an intensional-level reasoning model.
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NLP & Semantic Computing Group
Commonsense Reasoning
Selective (focussed) reasoning Selecting the relevant facts in the context
of the inference
Reducing the search space.Scalability
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NLP & Semantic Computing Group
Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
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target
source answer
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NLP & Semantic Computing Group
Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
79
target
source answer
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NLP & Semantic Computing Group
Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
80
target
source answer
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NLP & Semantic Computing Group
John Smith
EngineerInstance-level
occupation
Does John Smith have a degree?
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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NLP & Semantic Computing Group
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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).
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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
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NLP & Semantic Computing Group
Annotating and Structuring WordNet Glosses• lake_poets:
• refinement:
• redundancy:
• slender_salamander:
• genus_Salix:
• unstaple:
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NLP & Semantic Computing Group
Semantic Roles for Lexical DefinitionsAristotle’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
char
acte
rized
by
has type
adds
non
-ess
entia
l inf
o to
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NLP & Semantic Computing Group
Bringing it into the Real World
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NLP & Semantic Computing Group
Semeval 2017
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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.
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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.
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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.
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NLP & Semantic Computing Group
http://www.slideshare.net/andrenfreitas
These slides: