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NLP & Semantic Computing Group
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Semantic Perspectives forContemporary Question Answering Systems
Andre FreitasUniversity of Passau
JAIST, December 2016
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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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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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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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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).8
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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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RDF as the basic data modelGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York
Instance
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Instance
Instance
Class
Property
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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.11
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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.12
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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
points13
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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
points14
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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
points15
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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
points16
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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.17
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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.19
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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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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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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 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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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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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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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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Lightweight Semantic Representation
Representing Texts as Contextualized Entity-Centric Linked Data Graphs, WebS 2013
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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 model30
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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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Distributional-Relational Networks
Distributional Relational Networks, AAAI Symposium, 2013
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The vector space is segmented33
Dimensional reduction mechanism!
A Distributional Structured Semantic Space for Querying RDF Graph Data, IJSC 2012
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Compositionality of Complex Nominals
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Compositional-distributional model for Categories
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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
ML-based
Rule-based
Rule-based
ML-based
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Argumentation Classification
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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
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Argumentation Classification
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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.41
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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
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Argumentation Classification
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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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Text Simplification for KG Extraction
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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.
A Sentence Simplification System for Improving Open Relation Extraction COLING (2016)
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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
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OpenIE, University of Washington
Argumentation Classification
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Taxonomy Extraction
Text Transformati
on
N-ary Relation Extractio
nText Simplificatio
n GraphSerializatio
n
Taxonomy
Extraction
Storage
Rule-based
Rule-based
ML-based
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Representation and Extraction of Complex Category Descriptors, NLDB 2014
Argumentation Classification
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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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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.”
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Argumentation Representation•Supports/Attack•Rhetorical Structure Theory (RSTs)
•Informal Logic•Argumentation Schemes (Walton et al.)•Pragmatic Categories
Retrieval
Reasoning
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QueryingKnowledge Graphs
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With DSMs our graph supports semantic approximations as a first-class operation
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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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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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•Now let’s answer the query
“Who is the daughter of Bill Clinton married to?”
Question
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•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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•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)
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(INSTANCE) (PROPERTY)
(PROPERTY)
(CLASS)
(PERSON)
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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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• 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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• 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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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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Relevance
Medium-high query expressivity / coverage
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Accurate semantic matching for a
semantic best-effort scenario
Ranking in the second position in
average
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Comparative Analysis
Better recall and query coverage compared to baselines with equivalent precision.
More comprehensive semantic matching.
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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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Reasoning for Text Entailment
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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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Beyond Word Vector Models
engineer
degree???
engineer
degree???
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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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Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
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target
source answer
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Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
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target
source answer
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Distributional semantic relatedness as a Selectivity Heuristics
Distributional heuristics
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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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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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Annotating and Structuring WordNet Glosses• lake_poets:
• refinement:
• redundancy:
• slender_salamander:
• genus_Salix:
• unstaple:
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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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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.
Representation needs to be convenient for information extraction and data consumers.
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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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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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http://www.slideshare.net/andrenfreitas
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