CARMA Constructional Analyzer using Recursively Multiple AVMs
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CARMA Constructional Analyzer using Recursively Multiple AVMs Ely Edison Matos ely.matos@ufjf.edu.br September 6, 2018 FrameNet Brasil Project - UFJF
Table of contents
1. Introduction
2. Premises
3. Computational Processing
4. Limitations and Outlook
1Introduction
Context
FNBr is working on NLU (Natural Language Understanding) projects.
FNBr approach to NLU comprises three main elements:
1. Linguistic Knowledge: Lexicon, Constructions, GF, POS, Roles,
Syntax, etc.
2. World Knowledge: Ontologies and external datasets
3. Situational Context: Frames and Frame Elements
NLU processes must use linguistic knowledge cognitively to get an
approximated shape of a world knowledge in a given situational
context.
2CARMA
CARMA1 is a constructional analyzer: given a raw sentence, it tries to
identify the constructions in the sentence.
If these constructions evoke a frame, it helps to identify the Situational
Context.
1 this is a new version of [4]
3Why constructional analysis?
obj
det nsubj det
o celular quebrou a tela
DET NOUN VERB DET NOUN
The cellphone break.PST the screen
’The screen in cellphone broke’/Cxn Split_object
obj
det nsubj det
o menino quebrou a cadeira
DET NOUN VERB DET NOUN
The boy break.PST the chair
’The boy broke the chair’/Cxn Transitive_action
4Resources
CARMA is using 4 different resources:
1. FNBr framenet
• the network of frames and LUs, including all lexicon stuff (words,
lexemes, lemmas,..)
2. FNBr constructicon
• the network of constructions
3. FNBr ontology
• a Generative Lexicon based ontology defining extended qualia
relations between LUs (based on SIMPLE ontology[1])
4. UD parser
• to get the syntactic structure of sentence using UD POS and
relations.
5Premises
AVM
Figure 1: AVM structure
6AVM
Figure 2: Everything as AVM
! Recursive AVMs: the value can be another AVM
7Constraints
CARMA is a constraint-based system: the AVM attributes must be
restricted by a (set of) possible/acceptable value(s)
Constructions are defined by constraining construction elements to
dependency relations, as proposed by Property Grammar [2]
8Construction definition
cxn_split_object:
type: cxn
class: cxn_split_object
region: cxn_split_object
attributes:
nsubj:
features: {optional: false, head: false}
value: [ud_nsubj]
verb:
features: {optional: false, head: true}
value: [pos_verb]
obj:
features: {optional: false, head: false}
value: [ud_obj]
x_part:
type: xe
value: [rel_is_part_of]
x_frame:
type: xe
value: [frm_undergoing]
constraints:
- {arg1: verb, constraint: dominance, arg2: nsubj}
- {arg1: verb, constraint: dominance, arg2: obj}
- {arg1: nsubj, arg2: x_part, constraint: hasword}
- {arg1: obj, arg2: x_part, constraint: hasword}
9Computational Processing
Topology
CARMA
is a recursive hierarchical network
and an elaborated pattern-matching system
So, it is amenable to some Machine Learning techniques
10RCN
RCN: Recursive Cortical Network[3]
Figure 3: Overview of RCN (source:[3]) 11RCN
? RCN resembles AVM
Figure 4: Detail of of RCN (source:[3])
12RCN Processing
RCN can be used for generation and inference (parsing)
Inference
• Belief propagation
• Forward-pass
• Backward-pass
13CARMA processing
In CARMA we are interested in the parsing process
Resources are stored in some persistent medium
• Lexicon on FNBr database (MySQL)
• Frames, Constructions, Ontology exported to Neo4j graph database
14CARMA processing
1. User inputs a sentence.
2. The sentence is parsed for UD (currently using UDPipe parser)
3. FNBr database is queried for wordforms, lexemes and lemmas
4. A type network is built with lexical stuff
5. Graph database is queried to complete the type network
6. Type network is traversed to create a token network
7. Word nodes are activated, constraints are calculated and the
activation spreads in token network until a root node
8. Activated constructions nodes correspond to constructions detected
in the sentence
9. Conflicts (more than one construction activated) are resolved based
on MAP (maximum a posteriori)
15CARMA processing
Figure 5: Partial view of activated network
16Limitations and Outlook
Limitations
• Current version is at very beginning
• UD parsing for Brazilian Portuguese is very limited and error prone
• Some basic linguistic phenomenons are not handled yet (e.g. Null
Instantiation)
• and many others...
17Outlook
• How to implement a learning process?
• How to use the analysis in the context of construction alignment
• How many constraint types?
• and many others...
18Thank you!
18References i
N. Bel, F. Busa, N. Calzolari, E. Gola, A. Lenci, M. Monachini,
A. Ogonowski, I. Peters, W. Peters, N. Ruimy, M. Villegas, and
A. Zampolli.
SIMPLE: A General Framework for the Development of
Multilingual Lexicons.
Proceedings of the 2nd International Conference on Language
Resources and Evaluation, 2000.
P. Blache.
Property Grammars: A Fully Constraint-Based Theory.
In Christiansen H. et al. (eds.), Constraint Solving and Language
Processing, Sptinger-Verlag, Berlin Heidelberg, pages 1–16, 2005.
19References ii
D. George, W. Lehrach, K. Kansky, M. Lazaro-Gredilla, C. Laan,
B. Marthi, X. Lou, Z. Meng, and Y. Liu.
A generative vision model that trains with high data efficiency
and breaks text-based CAPTCHAs.
Science, 10(October):1–19, 1126.
E. Matos, T. Torrent, V. Almeida, A. Laviola, L. Lage, N. Marção,
and T. Tavares.
Constructional Analysis Using Constrained Spreading
Activation in a FrameNet-Based Structured Connectionist
Model.
The AAAI 2017 Spring Symposium on Computational Construction
Grammar and Natural Language Understanding, Technical Report
SS-17-02, pages 222–229, 2017.
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