The document discusses semantic parsing and semantic role labeling to map natural language text to a formal representation of its meaning. It describes preprocessing text through tokenization and tagging, then analyzing syntax and semantics by identifying predicates and arguments. Finally, it represents the analysis in a structured RDF format linked to an ontology to define types and relations. This enables applications to search for and connect information about existing software solutions.

2. Semantic Parsing
Definition: process of mapping natural language
text into a formal representation of its meaning.
Ewan forgot the mozzarella in his car.
∃x0 named(x0, ewan, person) ∧
∃x1 mozzarella(x1) ∧
∃x2 car(x2) ∧ of(x2,x0) ∧ in(x1, x2) ∧
∃e event(e) ∧ forget(e) ∧ agent(e, x0) ∧
patient(e, x1)
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3. Semantic Parsing
Definition: process of mapping natural language
text into a formal representation of its meaning.
Ewan forgot the mozzarella in his car.
∃x0 named(x0, ewan, person) ∧
∃x1 mozzarella(x1) ∧
∃x2 car(x2) ∧ of(x2,x0) ∧ in(x1, x2) ∧
∃e event(e) ∧ forget(e) ∧ agent(e, x0) ∧
patient(e, x1)
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4. Semantic Role Labelling
Mapping to a shallow semantic representation of
predicates and associated semantic arguments
Ewan forgot the mozzarella in his car.
∃x0 named(x0, ewan, person) ∧
∃x1 mozzarella(x1) ∧
∃x2 car(x2) ∧ of(x2,x0) ∧ in(x1, x2) ∧
∃e event(e) ∧ forget(e) ∧ agent(e, x0) ∧
patient(e, x1)
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5. Semantic Role Labelling
Mapping to a shallow semantic representation of
predicates and associated semantic arguments
Ewan forgot the mozzarella in his car.
∃x0 named(x0, ewan, per) ∧ male(x0) ∧
∃x1 mozzarella(x1) ∧
∃x2 car(car←———x2) ∧ of(his x2,x0) ∧
∧
∃e event(e) ∧ forget(e) ∧ agent(e, x0) ∧
Ewan—actor→forget)
Ewan———↗ patient(e, x3) ∧ ↖ in(———e, x2)
mozzarella
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6. Semantic Role Labelling
Mapping to a shallow semantic representation of
predicates and associated semantic arguments
Ewan forgot the mozzarella in his car.
∃x0 named(x0, ewan, per) ∧ male(x0) ∧
∃x1 mozzarella(x1) ∧
∃x2 car←car(x2) owner—∧ of(x2,his x0) ∧
car—location ∧
∃e event(e) ∧ forget(e) ∧ agent(e, x0) ∧
Ewan—actor→forget) ↓
Ewan—patient(actor↗ e, x3) ∧ in(↖theme—e, x2)
mozzarella
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7. Semantic Role Labelling
Motivation: identify who did what to whom,
where, why and how, etc.
Ewan forgot the mozzarella in his car.
∃x0 named(x0, ewan, per) ∧ male(x0) ∧
∃x1 mozzarella(x1) ∧
∃x2 car(car←x2) owner—∧ of(x2,his x0) ∧
car—location ∧
∃e event(e) ∧ forget(e) ∧ agent(e, x0) ∧
Ewan—actor→forget) ↓
Ewan—patient(actor↗ e, x3) ∧ in(↖theme—e, x2)
mozzarella
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8. S-CASE Project
CASE: Computer Assisted Software Engineering
 Plethora of software solutions already available
 Could be (re)used for rapid prototyping
The role of UEDIN in this project:
 Analyse textual requirements of existing
solutions that describe their functionalities
 Provide a search interface for finding solutions
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9. Functional Requirements
Properties
 Discussed by developers and customers
 Basis for work plans, implementations, etc.
Examples
“The user must be able to login to his account.”
“The system should store all activities.”
…
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10. Pre-processing
Before mapping text to meaning representations:
 Tokenization
 Part-of-speech tagging and lemmatization
 Syntactic dependency parsing
“The user must be able to login to his account.”
The user must be able to login to his account .
the
user
must
be
able
to
login
to
his
account
.
DT
NN
MD
VB
JJ
TO
VB
TO
PRP
NN
.
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11. Semantic Analysis
Several steps of analysis are required
 Find “predicates” in a sentence
 Identify potential arguments
 Classify arguments of each predicate
“The user must be able to login to his account.”
The user must be able to login to his account .
the
user
must
be
able
to
login
to
his
account
.
DT
NN
MD
VB
JJ
TO
VB
TO
PRP
NN
.
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12. Semantic Analysis—Detailed View
Several steps of analysis are required
 Find “predicates” in a sentence
 Identify potential arguments
 Classify arguments of each predicate
“The user must be able to login to his account.”
 Assigned part-of-speech
 Number of children
 Parent word form
The user must be able to login to his account .
the
user
must
be
able
to
login
to
his
account
.
DT
NN
MD
VB
JJ
TO
VB
TO
PRP
NN
.
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13. Semantic Analysis—Detailed View
Several steps of analysis are required
 Find “predicates” in a sentence
 Identify potential arguments
 Classify arguments of each predicate
“The user must be able to login to his account.”
 Assigned part-of-speech
 Labelled path to predicate
 (Other) children of pred
The user must be able to login to his account .
the
user
must
be
able
to
login
to
his
account
.
DT
NN
MD
VB
JJ
TO
VB
TO
PRP
NN
.
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14. Semantic Analysis—Detailed View
Several steps of analysis are required
 Find “predicates” in a sentence
 Identify potential arguments
 Classify arguments of each predicate
“The user must be able to login to his account.”
 Head word of argument
 Relative position
 Labelled dependency
The user must be able to login to his account .
the
user
must
be
able
to
login
to
his
account
.
DT
NN
MD
VB
JJ
TO
VB
TO
PRP
NN
.
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15. What about Linked Data?
Once we identified all predicates and arguments
 We can map them into a structured format
 Link with other information and share online
 Store in a database for downstream applications
“The user must be able to login to his account.”
The user must be able to login to his account .
the
user
must
be
able
to
login
to
his
account
.
DT
NN
MD
VB
JJ
TO
VB
TO
PRP
NN
.
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16. The user must be able to login to his account .
the
:x0 user
a :must
user; be
able
:e0 to
login
a :to
login;
his
account
.
DT
:actor_NN
of :e0. MD
VB
JJ
TO
:has_actor VB
:TO
x0;
PRP
NN
.
RDF Representation
Storing SRL predicates and arguments
 Define one entity per relevant word token
(predicates and arguments can coincide)
 Use RDF triples to describe relations
“The user must be able to login to his account.”
user login
:acts_on :x1.
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17. The user must be able to login to his account .
the
:x0 user
a :must
user; be
able
:e0 to
login
a :to
login;
his
account
.
DT
:actor_NN
of :e0. MD
VB
JJ
TO
:has_actor VB
:TO
x0;
PRP
NN
.
RDF Representation (cont.)
The bigger scheme: what are users, logins, etc.?
 Ontology defines classes, relations, restrictions
 user is-a actor is-a thingtype is-a concept
 ACTOR_OF(x, y) ↔ HAS_ACTOR(y, x)
“The user must be able to login to his account.”
_:user _:login
:acts_on :x1.
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18. Advantages for Applications
 Ontology defines concept types and relations
 Finite set of pre-defined symbols
 user is-a actor is-a thingtype is-a concept
 subclasses can be exploited for search
 ACTOR_OF(x, y) ↔ HAS_ACTOR(y, x)
 Axioms for detecting inconsistencies
and inferring missing relations
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19. Putting the Pieces Together
OWL
Ontology
Applications
Applications
Requirements
Document
Implemented
Software
Component
RDF
Triples
DB
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20. Conclusions
 Semantic parsing is an important prerequisite for
computational natural language understanding
 Results of shallow semantic analysis can be
represented in a structured format for
downstream applications
 Linked data helps us to connect and share
information on existing software solutions and
makes efficient search possible
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21. 21
Thank you!
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22. 22
Questions?
10 September 2014