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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Chart-based RRG parsingusing an automatically extracted
RRG grammar with features
David Arps & Tatiana Bladier & Laura Kallmeyer
International Conference on Role and Reference Grammar19 August 2019
University at Buffalo, The State University of New York
1 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Overview
Design of RRG Grammars
Automatic RRG Grammar Extraction
Parsing experiments
Issues
Summary & Outlook
2 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Introduction
1 RRG annotated treebank:RRGBank [2],
2 extract RRG elementarytrees automatically,
3 use these elementarytrees for RRG parsing.
SENTENCE
CLAUSE
CORE
NP
PRO
it
V
looks
NUC
PP
COREP
NUCP
P
like
DEF-OP
a
N
NUCN
COREN
NP
holiday
3 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Introduction
1 RRG annotated treebank:RRGBank [2],
2 extract RRG elementarytrees automatically,
3 use these elementarytrees for RRG parsing.
NP
PRO
it
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
looks
PP↓
PP
COREP
NUCP
P
like
NP↓
NP∗
DEF-OP
a
NP
COREN
NUCN
N
holiday
3 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Introduction
1 RRG annotated treebank:RRGBank [2],
2 extract RRG elementarytrees automatically,
3 use these elementarytrees for RRG parsing.
NP
PRO
it
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
looks
PP↓
PP
COREP
NUCP
P
like
NP↓
NP∗
DEF-OP
a
NP
COREN
NUCN
N
holiday
3 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Outline
Design of RRG Grammars
Automatic RRG Grammar Extraction
Parsing experiments
Issues
Summary & Outlook
4 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
RRGbank
? Corpus of RRG annotated sentences [2]→ automatically converted from PennTreebank,→ manually checked and validated;
? 395 gold sentences, 1090 silverannotated sentences;
? RRGbank and RRG annotation tool:rrgbank.phil.hhu.de.
SENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORE
CLM
to
NUC
AUX
be
NP
COREN
AP-PERI
A
heavy
NUCN
N
buyers
TNS-OP
were
V
NUC
CORE
CLAUSETNS
5 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Removing crossing branchesSENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORE
CLM
to
NUC
AUX
be
NP
COREN
AP-PERI
A
heavy
NUCN
N
buyers
TNS-OP
were
V
NUC
CORE
CLAUSETNS
→
SENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORE
CLM
to
NUC
AUX
be
NP
COREN
AP-PERI
A
heavy
NUCN
N
buyers
TNS-OP [OP=CL]
were
V
NUC
CORE
CLAUSETNS? we transform the RRG structures to remove crossing branches,
? we mark the original position of the node with [op=cl],? original tree structure is easily recovered.
6 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Removing crossing branchesSENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORE
CLM
to
NUC
AUX
be
NP
COREN
AP-PERI
A
heavy
NUCN
N
buyers
TNS-OP
were
V
NUC
CORE
CLAUSETNS
→
SENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORE
CLM
to
NUC
AUX
be
NP
COREN
AP-PERI
A
heavy
NUCN
N
buyers
TNS-OP [OP=CL]
were
V
NUC
CORE
CLAUSETNS? we transform the RRG structures to remove crossing branches,? we mark the original position of the node with [op=cl],
? original tree structure is easily recovered.
6 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Removing crossing branchesSENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORE
CLM
to
NUC
AUX
be
NP
COREN
AP-PERI
A
heavy
NUCN
N
buyers
TNS-OP
were
V
NUC
CORE
CLAUSETNS
→
SENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORE
CLM
to
NUC
AUX
be
NP
COREN
AP-PERI
A
heavy
NUCN
N
buyers
TNS-OP [OP=CL]
were
V
NUC
CORE
CLAUSETNS? we transform the RRG structures to remove crossing branches,? we mark the original position of the node with [op=cl],? original tree structure is easily recovered.
6 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Operator projection and periphery can be recovered
SENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORE
CLM
to
NUC
AUX
be
NP
COREN
AP-PERI
A
heavy
NUCN
N
buyers
TNS-OP
were
V
NUC
CORE
CLAUSETNS
→
SENTENCE
CLAUSE
CORE
NP
COREN
NUCN
N
Japanese
NUC
V
said
CORECLM
to
NUC
AUX
be
NP
COREN
AP
PERIPHERY
heavy
NUCN
N
buyerswere
V
NUC
CORE
CLAUSETNS
7 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Elementary trees in RRG Grammars
? We follow Kallmeyer et al. (2013) and Osswald & Kallmeyer(2018) [3, 4] in design of the elementary trees in our grammar.
? Three tree composition operations:→ substitution ( argument slot filling)→ wrapping substitution (displaced argument slot filling)→ sister adjunction ( adding operators and periphery elements);
? Such RRG grammars capture long-distance dependencies→ for example, WH-movement.
8 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Elementary trees in RRG Grammars
? We follow Kallmeyer et al. (2013) and Osswald & Kallmeyer(2018) [3, 4] in design of the elementary trees in our grammar.
? Three tree composition operations:
→ substitution ( argument slot filling)→ wrapping substitution (displaced argument slot filling)→ sister adjunction ( adding operators and periphery elements);
? Such RRG grammars capture long-distance dependencies→ for example, WH-movement.
8 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Elementary trees in RRG Grammars
? We follow Kallmeyer et al. (2013) and Osswald & Kallmeyer(2018) [3, 4] in design of the elementary trees in our grammar.
? Three tree composition operations:→ substitution ( argument slot filling)
→ wrapping substitution (displaced argument slot filling)→ sister adjunction ( adding operators and periphery elements);
? Such RRG grammars capture long-distance dependencies→ for example, WH-movement.
8 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Elementary trees in RRG Grammars
? We follow Kallmeyer et al. (2013) and Osswald & Kallmeyer(2018) [3, 4] in design of the elementary trees in our grammar.
? Three tree composition operations:→ substitution ( argument slot filling)→ wrapping substitution (displaced argument slot filling)
→ sister adjunction ( adding operators and periphery elements);
? Such RRG grammars capture long-distance dependencies→ for example, WH-movement.
8 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Elementary trees in RRG Grammars
? We follow Kallmeyer et al. (2013) and Osswald & Kallmeyer(2018) [3, 4] in design of the elementary trees in our grammar.
? Three tree composition operations:→ substitution ( argument slot filling)→ wrapping substitution (displaced argument slot filling)→ sister adjunction ( adding operators and periphery elements);
? Such RRG grammars capture long-distance dependencies→ for example, WH-movement.
8 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓
CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓
CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓
CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓
CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓
CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓
CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓
CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓ CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
NP
PRO
I
CORE∗
TNS-OP [OP=CL]
have
SENTENCE
CLAUSE
CORE
NP↓ NUC
V
felt
NP↓ CORE*N
AP-PERI
COREA
NUCA
A
many
NP
COREN
NUCN
N
aftershocks
Sentence: I have felt many aftershocks
9 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Substitution and sister adjunction
SENTENCE
CLAUSE
CORE
NP
PRO
I felt
V
NUC
NP
AP-PERI
COREA
NUCA
A
many
NUCN
N
aftershocks
TNS-OP [OP=CL]
have
COREN
Sentence: I have felt many aftershocks
10 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Wrapping substitution
CL
CL↓CO
NUC
V
think
RP
Asterix
TNS
does
CL
CL
CO
NUC
V
drank
RP
Obelix
PrCS
RP
What
CL
CL
CO
NUC
V
drank
RP
Obelix
CO
NUC
V
think
RP
Asterix
TNS
does
PrCS
RP
What
Sentence: What does Asterix think Obelix drank
11 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Wrapping substitution
CL
CL↓CO
NUC
V
think
RP
Asterix
TNS
does
CL
CL
CO
NUC
V
drank
RP
Obelix
PrCS
RP
What
CL
CL
CO
NUC
V
drank
RP
Obelix
CO
NUC
V
think
RP
Asterix
TNS
does
PrCS
RP
What
Sentence: What does Asterix think Obelix drank
11 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Wrapping substitution
CL
CL↓CO
NUC
V
think
RP
Asterix
TNS
does
CL
CL
CO
NUC
V
drank
RP
Obelix
PrCS
RP
What
CL
CL
CO
NUC
V
drank
RP
Obelix
CO
NUC
V
think
RP
Asterix
TNS
does
PrCS
RP
What
Sentence: What does Asterix think Obelix drank
11 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Combination operations: Wrapping substitution
CL
CL↓CO
NUC
V
think
RP
Asterix
TNS
does
CL
CL
CO
NUC
V
drank
RP
Obelix
PrCS
RP
What
CL
CL
CO
NUC
V
drank
RP
Obelix
CO
NUC
V
think
RP
Asterix
TNS
does
PrCS
RP
What
Sentence: What does Asterix think Obelix drank
11 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Elementary trees with features
RRGbank Extracted Elementary Trees
NP
DEF-OP
the
CORE-N
NUC-N
N
average placeholder
NP∗
DEF-OP
the
NP
CORE-N
NUC-N
N
average
[DEF -OPS [NP -]
][DEF +OPS [NP +]
][DEF +]
[OP=NP]
[NUC-O lOPS [NP -]
] [NUC-O rOPS [NP -]
]
? elementary trees are enhanced with features
→ edge features→ node features
12 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Elementary trees with features
RRGbank Extracted Elementary Trees
NP
DEF-OP
the
CORE-N
NUC-N
N
average placeholder
NP∗
DEF-OP
the
NP
CORE-N
NUC-N
N
average
[DEF -OPS [NP -]
][DEF +OPS [NP +]
][DEF +]
[OP=NP]
[NUC-O lOPS [NP -]
] [NUC-O rOPS [NP -]
]
? elementary trees are enhanced with features→ edge features
→ node features
12 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Elementary trees with features
RRGbank Extracted Elementary Trees
NP
DEF-OP
the
CORE-N
NUC-N
N
average placeholder
NP∗
DEF-OP
the
NP
CORE-N
NUC-N
N
average
[DEF -OPS [NP -]
][DEF +OPS [NP +]
][DEF +]
[OP=NP]
[NUC-O lOPS [NP -]
] [NUC-O rOPS [NP -]
]
? elementary trees are enhanced with features→ edge features→ node features
12 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Edge featuresRRGbank Extracted Elementary Trees
NP
DEF-OP
the
CORE-N
NUC-N
N
average placeholder
NP∗
DEF-OP
the
NP
CORE-N
NUC-N
N
average
[DEF -OPS [NP -]
][DEF +OPS [NP +]
][DEF +]
[OP=NP]
[NUC-O lOPS [NP -]
] [NUC-O rOPS [NP -]
]
Left and right edge feature structures:
? unify adjacent structures in the derived tree? model ordering constraints? percolate upwards until phrasal nodes
13 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Edge featuresRRGbank Extracted Elementary Trees
NP
DEF-OP
the
CORE-N
NUC-N
N
average placeholder
NP∗
DEF-OP
the
NP
CORE-N
NUC-N
N
average
[DEF -OPS [NP -]
][DEF +OPS [NP +]
][DEF +]
[OP=NP]
[NUC-O lOPS [NP -]
] [NUC-O rOPS [NP -]
]
Left and right edge feature structures:? unify adjacent structures in the derived tree
? model ordering constraints? percolate upwards until phrasal nodes
13 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Edge featuresRRGbank Extracted Elementary Trees
NP
DEF-OP
the
CORE-N
NUC-N
N
average placeholder
NP∗
DEF-OP
the
NP
CORE-N
NUC-N
N
average
[DEF -OPS [NP -]
][DEF +OPS [NP +]
][DEF +]
[OP=NP]
[NUC-O lOPS [NP -]
] [NUC-O rOPS [NP -]
]
Left and right edge feature structures:? unify adjacent structures in the derived tree? model ordering constraints
? percolate upwards until phrasal nodes
13 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Edge featuresRRGbank Extracted Elementary Trees
NP
DEF-OP
the
CORE-N
NUC-N
N
average placeholder
NP∗
DEF-OP
the
NP
CORE-N
NUC-N
N
average
[DEF -OPS [NP -]
][DEF +OPS [NP +]
][DEF +]
[OP=NP]
[NUC-O lOPS [NP -]
] [NUC-O rOPS [NP -]
]
Left and right edge feature structures:? unify adjacent structures in the derived tree? model ordering constraints? percolate upwards until phrasal nodes
13 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Node featuresRRGbank Extracted Elementary Trees
NP
DEF-OP
the
CORE-N
NUC-N
N
average placeholder
NP∗
DEF-OP
the
NP
CORE-N
NUC-N
N
average
[DEF -OPS [NP -]
][DEF +OPS [NP +]
][DEF +]
[OP=NP]
[NUC-O lOPS [NP -]
] [NUC-O rOPS [NP -]
]
? One feature structure per node:• unify during tree composition• store syntactic or syn-sem interface information.
? Unification successful → accept parse tree
14 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Outline
Design of RRG Grammars
Automatic RRG Grammar Extraction
Parsing experiments
Issues
Summary & Outlook
15 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
RRG Grammar extraction algorithm (1)
? Elementary tree extraction inspired by Xia [6] algorithmfor induction of Tree-Adjoining Grammars.
? Top-down extraction of elementary trees.? Heuristics from head-modifier percolation tables.? We use RRG structures from RRGbank for automatic grammarinduction→ rrgbank.phil.hhu.de.
16 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
RRG Grammar extraction algorithm (2)
ROOT
CLAUSE
CORE
NP
COREN
NUCN
DEF-OP
The
N
average
V
dropped
NUC NP
COREN
CD
27
NUCN
N
points
TNS-OP [OP=CL]
had
NP∗
DEF-OP[OP = NP,DEF = +]
The
NP
COREN
NUCN
N
average
CORE∗
TNS-OP[OP = CL,TNS = past]
had
ROOT
CLAUSE
CORE
NP↓ NUC
V
dropped
NP↓
COREN∗
CD
27
NP
COREN
NUCN
N
points
17 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Outline
Design of RRG Grammars
Automatic RRG Grammar Extraction
Parsing experiments
Issues
Summary & Outlook
18 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Chart-based RRG Parser TuLiPA
? input: set of elementary trees and sentences to parse;
? output: all derivations that can be derived by combining theelementary trees;
? standard CYK algorithm;? bottom-up, left-to-right traversion of the derived tree;? software: TuLiPA RRG parser [1]
(https://github.com/spetitjean/TuLiPA-frames)TuLiPA = Tübingen Linguistic Parsing Architecture;
? suitable for hand-crafted precision RRG grammars;? suitable for automatically extracted RRG grammars.
19 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Chart-based RRG Parser TuLiPA
? input: set of elementary trees and sentences to parse;? output: all derivations that can be derived by combining theelementary trees;
? standard CYK algorithm;? bottom-up, left-to-right traversion of the derived tree;? software: TuLiPA RRG parser [1]
(https://github.com/spetitjean/TuLiPA-frames)TuLiPA = Tübingen Linguistic Parsing Architecture;
? suitable for hand-crafted precision RRG grammars;? suitable for automatically extracted RRG grammars.
19 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Chart-based RRG Parser TuLiPA
? input: set of elementary trees and sentences to parse;? output: all derivations that can be derived by combining theelementary trees;
? standard CYK algorithm;
? bottom-up, left-to-right traversion of the derived tree;? software: TuLiPA RRG parser [1]
(https://github.com/spetitjean/TuLiPA-frames)TuLiPA = Tübingen Linguistic Parsing Architecture;
? suitable for hand-crafted precision RRG grammars;? suitable for automatically extracted RRG grammars.
19 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Chart-based RRG Parser TuLiPA
? input: set of elementary trees and sentences to parse;? output: all derivations that can be derived by combining theelementary trees;
? standard CYK algorithm;? bottom-up, left-to-right traversion of the derived tree;
? software: TuLiPA RRG parser [1](https://github.com/spetitjean/TuLiPA-frames)TuLiPA = Tübingen Linguistic Parsing Architecture;
? suitable for hand-crafted precision RRG grammars;? suitable for automatically extracted RRG grammars.
19 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Chart-based RRG Parser TuLiPA
? input: set of elementary trees and sentences to parse;? output: all derivations that can be derived by combining theelementary trees;
? standard CYK algorithm;? bottom-up, left-to-right traversion of the derived tree;? software: TuLiPA RRG parser [1](https://github.com/spetitjean/TuLiPA-frames)TuLiPA = Tübingen Linguistic Parsing Architecture;
? suitable for hand-crafted precision RRG grammars;? suitable for automatically extracted RRG grammars.
19 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Chart-based RRG Parser TuLiPA
? input: set of elementary trees and sentences to parse;? output: all derivations that can be derived by combining theelementary trees;
? standard CYK algorithm;? bottom-up, left-to-right traversion of the derived tree;? software: TuLiPA RRG parser [1](https://github.com/spetitjean/TuLiPA-frames)TuLiPA = Tübingen Linguistic Parsing Architecture;
? suitable for hand-crafted precision RRG grammars;
? suitable for automatically extracted RRG grammars.
19 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Chart-based RRG Parser TuLiPA
? input: set of elementary trees and sentences to parse;? output: all derivations that can be derived by combining theelementary trees;
? standard CYK algorithm;? bottom-up, left-to-right traversion of the derived tree;? software: TuLiPA RRG parser [1](https://github.com/spetitjean/TuLiPA-frames)TuLiPA = Tübingen Linguistic Parsing Architecture;
? suitable for hand-crafted precision RRG grammars;? suitable for automatically extracted RRG grammars.
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Extracted RRG grammar
• removed punctuation• exhaustive parsing (i.e. not probabilistic, overgenerating a lot)
• 2 versions:1 no features2 edge features for operators model adjunction constraints
• do feature structures eliminate parse trees that contradictlinguistic intuitions?
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Parsing experiments
Gold Grammar Silver Grammarw feats w/o feats w feats w/o feats
Sentences 395 1480avg. sentence length 6.1 8.0token-supertag pairs 1526 1497 6288 6044avg. number of parses 6.9 12.7 1166 2939
savings 45.1% 39.7%
features decrease number of results by ≈ 45%
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Features restrict adjunction of operators and periphery (1)
SENTENCE
PP-PERI
COREP
NUCP
By
NP[NUC-O rOPS [NP -]
][NUC-O lOPS [NP -]
]COREN
CD
9:45
CLAUSE
CORE
NP [NUC-O rOPS [NP -]
][NUC-O lOPS [NP -]
][NUC-O lOPS [NP -]
]AP-PERI
COREA
NUCA
A
industrial
COREN
NUCN
N
average
. . .
NP∗
DEF-OP
the
[NUC-O lOPS [NP -]
][NUC-O lOPS [NP +]
]
Sentence: By 9:45, the industrial average had dropped 27 points.
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Features restrict adjunction of operators and periphery (2)
SENTENCE
PP-PERI
COREP
NUCP
By
NP
COREN
CD
9:45
CLAUSE
CORE
NP
AP-PERI
COREA
NUCA
A
industrial
COREN
NUCN
N
average
. . .
NP
DEF-OP
the
Sentence: By 9:45, the industrial average had dropped 27 points.
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Outline
Design of RRG Grammars
Automatic RRG Grammar Extraction
Parsing experiments
Issues
Summary & Outlook
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Attachment ambiguitiesCORE*
PP-PERI
CORE_P
NUC_P
P
in
CORE↓
NP*
PP-PERI
CORE_P
NUC_P
P
in
NP↓
CORE_N*
PP-PERI
CORE_P
NUC_P
P
in
NP↓
CORE*
PP-PERI
CORE_P
NUC_P
P
in
CORE↓
CORE
NP
CORE-N
NUC-N
N
bank
X∗
PP-PERI
CORE_P
X↓NUC_P
P
in
NP∗
NP
CORE-N
NUC-N
N
Texas
syntactic information needed that might not be in RRGBank25 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Size of the grammar
Gold Grammar Silver Grammarw feats w/o feats w feats w/o feats
Sentences 395 1480avg. sentence length 6.1 8.0token-supertag pairs 1526 1497 6288 6044avg. number of parses 6.9 12.7 1166 2939
the number of parses per sentence increases with the size of the grammar
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Extraction of wrapping substitution treesCL
CL↓CO
NUC
V
think
RP
Asterix
TNS
does
CL
CL
CO
NUC
V
drank
RP
Obelix
PrCS
RP
What
CL
CL
CO
NUC
V
drank
RP
Obelix
CO
NUC
V
think
RP
Asterix
TNS
does
PrCS
RP
What
? discontinuous constituents are marked with traces in PTB;
? no special marking in RRGBank;
? transfer traces from PTB to RRG trees in RRGbank?27 / 32
Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Outline
Design of RRG Grammars
Automatic RRG Grammar Extraction
Parsing experiments
Issues
Summary & Outlook
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Summary
? automatically extracted elementary trees from RRGBank;? experiments with exhaustive parsing of 395/1480 sentences;? parsing w/o edge features → too many results;? some edge features already rule out bad results.
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Future Work
? adding more edge features = better results;? extract wrapping elementary trees;? 100s of results → not satisfying;? ambiguity and annotation/extraction mistakes have badconsequences;
? use unlexicalized elementary trees (= supertags);? probabilistic grammar and parsing→ A* parsing algorithm ParTAGe by Waszczuk (2017) [5];
? Web GUI.
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Design of RRG Grammars Automatic RRG Grammar Extraction Parsing experiments Issues Summary & Outlook
Thank you!
THANK YOU VERY MUCH FOR YOUR ATTENTION!
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References
References I
Arps, D. and Petitjean, S. (2018). A Parser for LTAG and Frame Semantics. In chair), N. C. C., Choukri, K., Cieri, C.,Declerck, T., Goggi, S., Hasida, K., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J.,Piperidis, S., and Tokunaga, T., editors, Proceedings of the Eleventh International Conference on LanguageResources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
Bladier, T., van Cranenburgh, A., Evang, K., Kallmeyer, L., Möllemann, R., and Osswald, R. (2018). RRGbank: a Roleand Reference Grammar Corpus of Syntactic Structures Extracted from the Penn Treebank. In Proceedings ofthe 17th International Workshop on Treebanks and Linguistic Theories (TLT 2018), December 13–14, 2018,Oslo University, Norway, number 155, pages 5–16. Linköping University Electronic Press.
Kallmeyer, L., Osswald, R., and Van Valin, Jr., R. D. (2013). Tree Wrapping for Role and Reference Grammar. In Morrill,G. and Nederhof, M.-J., editors, Formal Grammar 2012/2013, volume 8036 of LNCS, pages 175–190. Springer.
Osswald, R. and Kallmeyer, L. (2018). Towards a formalization of role and reference grammar. In Kailuweit, R., Künkel,L., and Staudinger, E., editors, Applying and Expanding Role and Reference Grammar., pages 355–378.Albert-Ludwigs-Universität, Universitätsbibliothek. [NIHIN studies], Freiburg.
Waszczuk, J. (2017). Leveraging MWEs in practical TAG parsing: towards the best of the two worlds. PhD thesis.
Xia, F. (1999). Extracting tree adjoining grammars from bracketed corpora. In Proceedings of the 5th Natural LanguageProcessing Pacific Rim Symposium (NLPRS-99), pages 398–403.
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