Semantics and Information Extraction

1
Semantics and Information Extraction

• Douglas E. Appelt
• Artificial Intelligence Center
• SRI International

2
What is Semantics?

• Theory of the relationship between formal aspects
  of language and objects and facts in the world.

3
Traditional Approach in NLP (and linguistics)

• Define a well-behaved logical language
• Intensional logic
• Dynamic predicate logic
• Discourse Representation Structures
• Define a semantics for the logical language
  (using model theory)
• Devise rules for translating natural language
  structures into the logical language that
  preserve truth conditions.
• Apply principles of compositionality to build
  larger structures from smaller ones.

4
Successes and Failures

• Success
• Data base query applications (e.g. ATIS systems)
• Dialog systems with narrow domain of application
  (e.g. TRAINS)
• Failures
• Extracting information from large corpora
• Real syntax too complex
• Coverage too weak for large corpora

5
Semantics and Information Extraction

• General requirements of a semantic theory for
  information extraction
• ACE as a specific approach to semantics for
  information extraction
• Examine specific issues
• Basic ontology
• Coreference
• Generic/Specific
• Metonymy
• Relations and Events

6
Information ExtractionA Pragmatic Approach

• Let application requirements drive semantic
  analysis
• Identify the types of entities that are relevant
  to a particular task
• Identify the range of facts that one is
  interested in for those entities
• Ignore everything else

7
MUC and Scenario Templates

• Define a set of interesting entities
• Persons, organizations, locations
• Define a complex scenario involving interesting
  events and relations over entities
• Example management succession persons,
  companies, positions, reasons for succession
• This collection of entities and relations is
  called a scenario template.

8
Problems with Scenario Template

• Encouraged development of highly domain specific
  ontologies, rule systems, heuristics, etc.
• Most of the effort expended on building a
  scenario template system was not directly
  applicable to a different scenario template.

9
Addressing the Problem

• Address a large number of smaller, more focused
  scenario templates (Event-99)
• Develop a more systematic ground-up approach to
  semantics by focusing on elementary entities,
  relations, and events (ACE)

10
The ACE Program

• Automated Content Extraction
• Develop core information extraction technology by
  focusing on extracting specific semantic entities
  and relations over a very wide range of texts.
• Corpora Newswire and broadcast transcripts, but
  broad range of topics and genres.
• Third person reports
• Interviews
• Editorials
• Topics foreign relations, significant events,
  human interest, sports, weather
• Discourage highly domain- and genre-dependent
  solutions

11
Components of a Semantic Model

• Entities - Individuals in the world that are
  mentioned in a text
• Simple entities singular objects
• Collective entities sets of objects of the same
  type where the set is explicitly mentioned in the
  text
• Attributes - Timeless unary properties of
  entities (e.g. Name)
• Temporal points and intervals
• Relations - Properties that hold of two entities
  over a time interval
• Events - A particular kind of relation among
  entities implying a change in relation state at
  the end of the time interval.

12
Semantic Analysis Relating Language to the Model

• Linguistic Mention
• A particular linguistic phrase
• Denotes a particular entity, relation, or event
• A noun phrase, name, or possessive pronoun
• A verb, nominalization, compound nominal, or
  other linguistic construct relating other
  linguistic mentions
• Linguistic Entity
• Equivalence class of mentions with same meaning
• Coreferring noun phrases
• Relations and events derived from different
  mentions, but conveying the same meaning

13
Language and World Model
Linguistic Mention
Denotes
Denotes
Linguistic Entity
14
NLP Tasks in an Extraction System
15
The Basic Semantic Tasks of an IE System

• Recognition of linguistic entities
• Classification of linguistic entities into
  semantic types
• Identification of coreference equivalence classes
  of linguistic entities
• Identifying the actual individuals that are
  mentioned in an article
• Associating linguistic entities with predefined
  individuals (e.g. a database, or knowledge base)
• Forming equivalence classes of linguistic
  entities from different documents.

16
Choosing an Ontology for IE Semantics

• Ordinary native speakers should be able to
  annotate text with minimal training.
• People should have well-developed intuitions
  about type classification
• Is a museum an organization or facility? (A
  FOG?)
• People should have well-developed intuitions
  about entity coreference
• Peace in the Middle East
• Entities should be extensional, not abstract,
  generic, counterfactual, or fictional

17
The ACE Ontology and Annotation Standards

• Documents available online
• http//www.ldc.upenn.edu/Projects/ACE/
• Entity standards
• Relations standards
• Proposed event standards still under development

18
The ACE Ontology

• Persons
• A natural kind, and hence self-evident
• Organizations
• Should have some persistent existence that
  transcends a mere set of individuals
• Locations
• Geographic places with no associated governments
• Facilities
• Objects from the domain of civil engineering
• Geopolitical Entities
• Geographic places with associated governments

19
Why GPEs

• An ontological problem certain entities have
  attributes of physical objects in some contexts,
  organizations in some contexts, and collections
  of people in others
• Sometimes it is difficult to impossible to
  determine which aspect is intentded
• It appears that in some contexts, the same phrase
  plays different roles in different clauses

20
Aspects of GPEs

• Physical
• San Francisco has a mild climate
• Organization
• The United States is seeking a solution to the
  North Korean problem.
• Population
• France makes a lot of good wine.

21
Metonymy

• Metonymy is when a speaker uses a mention to
  refer in a systematic way to an entity with a
  different name or type than that mentioned.
• Metonymy is a property of mentions.
• A literal mention is where the mention uses the
  name or type of the referential entity.
• A metonymic mention violates that in some way.
• A single entity can have both literal and
  metonymic mentions.

22
Examples

• Name metonymy
• Beijing announced a new policy toward North
  Korea.
• Baltimore hit a home run in the ninth inning
• SRI was severely damaged in the 1989 earthquake
• Type metonymy
• John works for the restaurant on the corner

23
Problem Cases literal and metonymic mentions
both not types of interest
John bought a Picasso.
It set him back 1 million.
He is his favorite artist.
24
Role AmbiguityWhy isnt it just metonymy?

• Iraq attacked Kuwait
• Was the attack on the physical territory?
• Was the attack on the government?
• Was the attack on the people of Kuwait?
• The answer is yes.

25
Multiple Roles

• Iraq disputed its border with Kuwait
• Governments dispute things
• Physical real estate has borders

26
Role Classification andSparse Data Problem

• Role determination through predicate-argument
  constraints
• China announced a new policy regarding North
  Korea.
• ACE Corpus About 20K words in training corpus
• GPE-PER 84 configurations
• GPE-LOC 432 configurations
• GPE-ORG 504 configurations
• GPE-GPE 789 configurations
• Only 131 configurations have more than 2
  instances in the corpus (about 7)
• Many of those involve weakly constrained
  predicates (have, be, of, etc.)

27
Generic vs Specific

• The assumed application is building a database
  using extracted information
• Databases typically represent concrete entities
• Specificity is a critical attribute of linguistic
  entities.
• Specificity is a property of the entity, not the
  mention
• John is looking for a Java programmer.
• He must have three years of experience.
• Problem assessment of specificity is a nuanced
  distinction subject to substantial
  inter-annotater disagreement

28
Types of Linguistic Mentions

• Name mentions
• The mention uses a proper name to refer to the
  entity
• Nominal mentions
• The mention is a noun phrase whose head is a
  common noun
• Pronominal mentions
• The mention is a headless noun phrase, or a noun
  phrase whose head is a pronoun, or a possessive
  pronoun

29
Entity and Mention Example
COLOGNE, Germany (AP) _ A Chilean exile
has filed a complaint against former Chilean
dictator Gen. Augusto Pinochet accusing him of
responsibility for her arrest and torture in
Chile in 1973, prosecutors said Tuesday. The
woman, a Chilean who has since gained German
citizenship, accused Pinochet of depriving
her of personal liberty and causing bodily harm
during her arrest and torture.
Person Organization Geopolitical Entity
30
Relations

• Relations hold between two entities over a time
  interval.
• Relations may be timeless or temporal interval
  is not specified
• Relations have inertia, I.e. they dont change
  unless a relevant event happens.

31
Explicit and Implicit Relations

• Many relations are true in the world. Reasonable
  knoweldge bases used by extraction systems will
  include many of these relations. Semantic
  analysis requires focusing on certain ones that
  are directly motivated by the text.
• Example
• Baltimore is in Maryland is in United States.
• Baltimore, MD
• Text mentions Baltimore and United States. Is
  there a relation between Baltimore and United
  States?

32
Another Example

• Prime Minister Tony Blair attempted to convince
  the British Parliament of the necessity of
  intervening in Iraq .
• Is there a role relation specifying Tony Blair as
  prime minister of Britain?
• A test a relation is implicit in the text if the
  text provides convincing evidence that the
  relation actually holds.

33
Explicit Relations

• Explicit relations are expressed by certain
  surface linguistic forms
• Copular predication - Clinton was the president.
• Prepositional Phrase - The CEO of Microsoft
• Prenominal modification - The American envoy
• Possessive - Microsofts chief scientist
• SVO relations - Clinton arrived in Tel Aviv
• Nominalizations - Anans visit to Baghdad
• Apposition - Tony Blair, Britains prime minister

34
Types of ACE Relations

• ROLE - relates a person to an organization or a
  geopolitical entity
• Subtypes member, owner, affiliate, client,
  citizen
• PART - generalized containment
• Subtypes subsidiary, physical part-of, set
  membership
• AT - permanent and transient locations
• Subtypes located, based-in, residence
• SOC - social relations among persons
• Subtypes parent, sibling, spouse, grandparent,
  associate

35
Event Types (preliminary)

• Movement
• Travel, visit, move, arrive, depart
• Transfer
• Give, take, steal, buy, sell
• Creation/Discovery
• Birth, make, discover, learn, invent
• Destruction
• die, destroy, wound, kill, damage

36
Problem Collective and Distributive Reference
John. .Bill
. they.
There are at least three distinct entities in
this text. Need a way to relate John and Bill
entities to the collective mention, they.
37
Solution Relations
John. .Bill
. they.
PartOf.part(e(John), e(they)) PartOf.part(e(Bill),
e(they))
Three of the men
PartOf.part(e(three), e(the men))
38
Summary

• Motivation for a semantic theory is a practical
  one driven by database filling needs
• Pick a limited ontology of core concepts, and
  build out, motivated by application needs
• Address a broad spectrum of semantic problems,
  but from a limited ontology that simplifies data
  annotation issues.