Semantic Perspectives for Contemporary Question Answering Systems

NLP & Semantic Computing Group

Semantic Perspectives forContemporary Question Answering Systems

Andre FreitasUniversity of Passau

JAIST, December 2016

Outline Multiple Perspectives of Semantic

Representation Lightweight Semantic Representation Knowledge Graph Extraction from Text Querying Knowledge Graphs Text Entailment Reasoning Take-away Message

Multiple Perspectives of Semantic Representation

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.

“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

Representation focal points•Types of knowledge to focus at the

representation: Facts vs Definitions Temporality Spatiality Modality Polarity Rhetorical structures Pragmatic categories …

Lightweight Semantic Representation

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

Representation of Contextual Relations (Facts)General Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York

Factoid shape

RDF as the basic data modelGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York

Instance

Property

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

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

Named entities are also low entropy entry points for answering queries Pivot

points14

Also abstract classes … Pivot

points15

They are also a very convenient way to represent. Pivot

points16

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

Pivot points

Context RepresentationGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York

Reification as a first class representation element

Context RepresentationGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York

Temporality, spatiality, modality, rhetorical relations …

Open VocabularyGeneral Electric Company, or GE , is an American multinational conglomerate corporation incorporated in Schenectady , New York

Temporality, spatiality, modality, rhetorical relations …

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?

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

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

Lightweight Semantic Representation

Representing Texts as Contextualized Entity-Centric Linked Data Graphs, WebS 2013

Distributional Semantics

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

Distributional Semantics as Commonsense Knowledge

Commonsense is here

leashSemantic Approximation is

Distributional-Relational Networks

Distributional Relational Networks, AAAI Symposium, 2013

The vector space is segmented33

Dimensional reduction mechanism!

A Distributional Structured Semantic Space for Querying RDF Graph Data, IJSC 2012

Compositionality of Complex Nominals

Compositional-distributional model for Categories

Compositional-distributional model for paraphrases

A Compositional-Distributional Semantic Model for Searching Complex Entity Categories, *SEM (2016)

Knowledge Graph Extraction from Text

Graphene

Graph Extraction Pipeline

Text Transformati

N-ary Relation Extractio

nText Simplificatio

n GraphSerializatio

Taxonomy

Extraction

Storage

ML-based

Rule-based

ML-based

Argumentation Classification

Minimalistic Text Transformations

Text Transformati

nText Simplificatio

n GraphSerializatio

Taxonomy

Extraction

Storage

ML-based

Rule-based

ML-based

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

Text Simplification

Text Transformati

nText Simplificatio

n GraphSerializatio

Taxonomy

Extraction

Storage

ML-based

Rule-based

ML-based

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

Text Simplification for KG Extraction

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)

N-ary Relation Extraction

Text Transformati

nText Simplificatio

n GraphSerializatio

Taxonomy

Extraction

Storage

Rule-based

ML-based

OpenIE, University of Washington

Taxonomy Extraction

Text Transformati

nText Simplificatio

n GraphSerializatio

Taxonomy

Extraction

Storage

Rule-based

ML-based

Representation and Extraction of Complex Category Descriptors, NLDB 2014

RST Classification

Text Transformati

nText Simplificatio

n GraphSerializatio

Taxonomy

Extraction

Storage

Rule-based

ML-based

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

Argumentation Representation•Supports/Attack•Rhetorical Structure Theory (RSTs)

•Informal Logic•Argumentation Schemes (Walton et al.)•Pragmatic Categories

Retrieval

Reasoning

QueryingKnowledge Graphs

With DSMs our graph supports semantic approximations as a first-class operation

Approach Overview

Query Planner

Ƭ-Space(embedding

graphs)

Commonsense knowledge

Core semantic approximation &

composition operations

Query AnalysisQuery Query Features

Query Plan

Corpus

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

•Now let’s answer the query

“Who is the daughter of Bill Clinton married to?”

Question

•Step 1: Determine answer type

Who is the daughter of Bill Clinton married to? (PERSON)

•Using POS Tags

Query Pre-Processing (Question Analysis)

•Step 2: Semantic role labeling.

Who is the daughter of Bill Clinton married to?

•NER, POS Tags Rules-based: POS Tag + IDF

(INSTANCE) (PROPERTY)

(PROPERTY)

(CLASS)

(PERSON)

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.

Bill Clinton

daughter married to

(INSTANCE)

Person

(PREDICATE) (PREDICATE) Query Features

• 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

Core Entity SearchBill Clinton

daughter married to Person

:Bill_Clinton

Query:

Entity search

Distributional Semantic SearchBill Clinton

:Bill_Clinton

Query:

:Chelsea_Clinton

:child

:Baptists:religion

:Yale_Law_School:almaMater...

(PIVOT ENTITY)

(ASSOCIATED TRIPLES)

: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’?

:Bill_Clinton

Query:

:Chelsea_Clinton

:child

:Bill_Clinton

Query:

:Chelsea_Clinton

:child

(PIVOT ENTITY)

:Bill_Clinton

Query:

:Chelsea_Clinton

:child:Mark_Mezvinsky

:spouse

Note the lazy disambiguation

Relevance

Medium-high query expressivity / coverage

Accurate semantic matching for a

semantic best-effort scenario

Ranking in the second position in

average

Comparative Analysis

Better recall and query coverage compared to baselines with equivalent precision.

More comprehensive semantic matching.

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.

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).

Reasoning for Text Entailment

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???

Beyond Word Vector Models

engineer

degree???

engineer

degree???

Beyond Word Vector Models: Intensional Reasoning

Representing structured intensional-level knowledge.

Creation of an intensional-level reasoning model.

Commonsense Reasoning

Selective (focussed) reasoning Selecting the relevant facts in the context

of the inference

Reducing the search space.Scalability

Distributional semantic relatedness as a Selectivity Heuristics

Distributional heuristics

target

source answer

target

source answer

target

source answer

John Smith

EngineerInstance-level

occupation

Does John Smith have a degree?

A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases, NLDB (2015).

Intensional-level representation• Dictionary definitions

refinement: a highly developed state of perception

state perfection

differentia quality

developed highly

quality modifier

differentia quality

refinement

Annotating and Structuring WordNet Glosses• lake_poets:

• refinement:

• redundancy:

• slender_salamander:

• genus_Salix:

• unstaple:

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

has type

Bringing it into the Real World

Semeval 2017

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.

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.

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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