Aussenac semanticsnl pwebsem2017-v4

New convergences between
Natural language processing and
knowledge engineering –
An illustration with the extraction and
representation of semantic relations
Nathalie Aussenac-Gilles
(IRIT – CNRS, Toulouse, France)
aussenac@irit.fr

Outline of the talk
• Evolution of the Language and Knowledge
duality in AI
• Deep learning for NLP
• Semantic relations
• Finding semantic relations
2SEMANTICS - NLP and KE at the era of SemWeb: Semantic relations - Aussenac12/09/2017

The language / Knowledge duality
in early AI
Natural Language Processing
• Ambitious goal
• To produce systems able to fully
understand and represent the
meaning of language
• Target representation: logic
• … inspired by linguistic >>
computational linguistics
Knowledge Engineering
• Ambitious goal
• To Produce systems able to fully
solve problems that « classical »
algorithms are not likely to solve
• Target representation: logic
• … inspired by human problem
solving >> expert systems
12/09/2017 SEMANTICS - NLP and KE at the era of SemWeb: Semantic relations - Aussenac 3
Knowledge
acquisition
Natural
Language
Processing Logic-based
representation
KBSSyntactic
Parsing / checking
Spelling checking
….

in classical AI
• To produce systems able to
understand and build
representations from language in
order to build systems that perform
language intensive tasks
• Target applications
– Identifying opinions
– Providing abstracts
– Translating from one language to
another
– Extracting information
– Answering questions
– Managing dialog systems
Knowledge engineering
• Collect knowledge from various
sources to build representations
and knowledge bases in order to
build systems that perform or
support knowledge intensive tasks
• Knowledge based systems
– Fault diagnosis
– Classification
– Repair
– Task planning
– simple design, …
• Little focus on domain knowledge

in classical AI
• Layered approach to deal
with specific issues
Knowledge Engineering
• Layered models and
reusable components
• Cf CommonKADS
OCR/ tokenization
Morphological /
lexical analysis
Syntactic analysis
Semantic typing
Discourse analysis
Task model
Inference structure
Domain model
Task library
Problem solving
methods
Ontologies1995

at the era of the (semantic) web
What the semantic web provides
• Ambition
– To make web pages and web data « understable » by algorithms
– To give them a semantics by typing entities and concepts
• Standards for knowledge representation
– Improve interoperability
– Promote knowledge and data reusability
• An architecture to reach this goal
– Web application composition: web services
– Semantic annotation
– Linking semantic data and making it open (LOD)

at the era of the (semantic) web
• Larger corpora enable
– Statistics
– Probabilist language models
– Machine learning
• NLP benefits of new semantic
datasets
– Ontologies
– Large KB: DBPedia, Yago,
– Multilingual lexical KB: BabelNet
Knowledge engineering
• Text as knowledge sources
– Information extraction techniques
– Semantic typing
– Relation extraction
• Ontology engineering from text
• Produce semantic resources
– (domain) Ontologies
– Large general KB
• Ontology based applications
– Connect services
– make apointments
– adapt processes to context
– answer questions, search for
information …
More knowledge sources, more data and more digital text

Linguistic clues for knowledge
October 2014 From natural language to ontologies 8

Named Entity
Recognition
Relation
extraction
Entity typing

Name : Sofia Copola or Sofia
Carmina Coppola
Is-a : Person
Born on: 1971, May 14
Born in: New York (USA)
Job: Movie director, actor
Nationality: American
Name : Francis Ford Copola
Is-a : Person
Born on: …
Born in: …
Job: Movie director
Nationality: American
Has-child

Additional linguistic difficulties

Additional linguistic difficulties
reference /
anaphora
coordination distribution

Other difficult issues
• Short (context-free) text:
headlines or tweets cf
http://www.cs.cmu.edu/~ark/TweetNLP/
• Asserting the value of facts
• Parsing non standard
English; neologism, spelling
errors, syntax errors …
• Non figurative language,
humor, sarcasm …
• segmentation issues
• …
• The Pope’s baby steps on gays
• The Eiffel Tower is a 324 metres tall
(including the antenas) wrought iron lattice
tower … The Eiffel Tower is 312 metres tall
…
• Great job @jusInbieber! Were SOO PROUD
of what you’ve accomplished! U taught us 2
#neversaynever & you yourself should
never give up either ♥
• “Congratulation #lesbleus for your great
match!” is ironic if the French soccer team
has lost the match.
• The New York-‐New Haven Railroad
The New York-‐New Haven Railroad

Machine learning for NLP
• Machine learning requires
– Annotating examples (time
consuming)
– Selecting (and evaluating)
appropriate features to
describe the data in a
processable way (complex,
requires linguistic expertise and
resources)
– Selecting the appropriate
learning algorithm
– The ML algo pptimizes the
weights on features
The #NSA wiretapped a whole country. No worries
for #Belgium: it is not a whole country.
 positive example
The Eiffel Tower is 324 metres tall.
 negative example
… #irony or #humor
 positive example

Machine learning for NLP
• Machine learning requires
– Annotating examples (time
consuming)
– Selecting (and evaluating)
appropriate features to
describe the data in a
processable way (complex,
requires linguistic expertise and
resources)
– Selecting the appropriate
learning algorithm
– The ML algo optimizes the
weights on features
Features for humor detection in Tweets (Karaoui et al.
2016)
Surface features
Number of words in tweet
Ponctuation mark y/n
Question mark y/n
Word in capital letter y/n
Interjection y/n
Emoticon y/n
Slang word y/n
Sequence of exclamation or question marks
Discourse connective y/n
Sentiment features
Nb of positive (négative) opinion words
Words of surprise, neutral opinion
Semantic shifter
Intensifier y/n
Negation word y/n
Reporting speach verb y/n
Opposition features
Explicit sentiment opposition y/n
(tested with patterns)
Lexicons
Word lists

Challenging industrial
applications
… that require NLP AND semantic resources
• Search engines (written and spoken)
• Online advertisement matching
• Automated/assisted translation
• Sentiment analysis for marketing or finance
• Speech recognition
• Chatbots / Dialog (virtual) agents
– Customer support
– Controlling devices
– Technical support to diagnose and repair

Current challenges
according to D. Jurasky in 2012

Outline of the talk
• The Language and Knowledge duality in AI

Recent shift:
Deep Learning (DL) for NLP
from C. Manning and R. Socher, Course about NLP with DL
http://web.stanford.edu/class/cs224n/lectures/cs224n-2017-lecture1.pdf
• Representation learning attempts to automatically
learn good features or representations
– Ex: vectors represent word distribution in corpus
• Deep learning algorithms attempt to learn
(multiple levels of) representation and an output
• From “raw” inputs x (e.g., sound, characters, or
words)
• … not that “raw”: WORD VECTORS are the input

Recent shift:
(Manning 2017 tutorial)
• Deep NLP = deep learning + NLP
• Reach the goals of NLP using some NLP works,
representation learning and deep learning methods
• Several big improvements in recent years in NLP
– Levels: speech, words, syntax, semantics
– Tools: parts-of-speech, entities, parsing
– Applications: machine translation, sentiment analysis,
dialogue agents, question answering

Recent shift:
Distributional semantics
• Semantic similarity is based on the distributional
hypothesis [Harris 1954]
• Take a word and its contexts:
– tasty sooluceps
– sweet sooluceps
– stale sooluceps
– freshly baked sooluceps
• By looking at a word’s context, one can infer its meaning
food

Recent shift:
Distributional semantics
• Vectors to capture word meaning = frequency of co-occurring words
• and matrix to capture word similarities
Red Tasty Rapid Second-
hand
Sweet
Cherry 2 3 0 0 1
Strawberry 3 1 0 0 2
Car 2 0 3 2 0
Truck 1 0 3 1 0
Red Tasty Rapid Second-
hand
Sweet
Cherry 52 104 0 0 75
Strawberry 68 85 0 0 42
Car 27 0 65 35 0
Truck 12 0 43 72 0
Red Tasty Fast Second-
hand
Sweet
Cherry 752 604 0 1 575
Strawberry 868 584 2 0 642
Car 274 0 465 358 0
Truck 126 0 343 172 0
red
Fast
Strawberry
Cherry
Car
Truck

Deep Learning for NLP
• Syntactic Parsing of sentence structure
Input

Deep learning for NLP
• Could be solving many problems
– Morphologic analysis: vectors of Morphemes combined with NN
– Semantics: Words, phrases and logical expressions are vectors -> compared
using NN to evaluate their similarity
– Sentiment analysis: combining various analyses with NN -> recursive NN
– Question answering: vectors of facts compares with NN
– …
• But not all the NLP problems in any context (domain specific corpora …)
• Requires large corpora to design word vectors, or to reuse existing word
vectors (built on Wikipedia and GigaCorpus)
– http://nlp.stanford.edu/projects/glove/
– https://github.com/idio/wiki2vec/
• Requires expertise to define the layers, the vectors and content of the NN

Outline of the talk

Semantic relations,
what do we mean?
• Semantic relation … what do you have in mind?
– Binary relation
– Hypernymy … meronymy
– Causality, temporal, spatial
– What about other kinds of relations?
(Cat, eats, mouse)
(« SimplyRed », plays, « The right thing »)
(« Eiffel Tower », has-height, « 324 m »)
(artist, performs, piece of music, date, location)
• Relation extraction from text: what do we have in mind?
– The relation is expressed in a single sentence.
– The relation is expressed in tables or tagged XML sections
Binary relations
Hierachical
relations
General
relations
N-ary relations

Semantic relations,
what do we mean?
Research field
• Linguistics: semantic
relations, semantic roles,
discourse relations
• Terminology
– Weak structure
– Stored in DB or SKOS models
• Information extraction
– Small set of classes
– Gazetteers contain lists of
entity labels
What is a relation
A tree comprises at least a trunk, roots and
branches.
A tree [Plants] comprises [meronymy] at least a
trunk, roots and branches.
(tree has_parts trunk)
(tree, has_parts, roots) …
in a gardening terminology
looks for relations between instances
tree Plantation year Species Branches
Tree1 1990 Oak > 20
Tree2 1995 Oak 15
whole
parts

Semantic relations,
what do we mean?
Research field
• Domain Ontology engineering
– Formal (logic, RDF, OWL …)
– Formal properties: transitivity …
– used to infer new knowledge
– part of a network
– May be shared or reused
• Semantic web
– Independent triples that
connect resources
– Publically available in data
repositories with W3C Standard
format
– Connect triples with existing
ones, with web ontologies
What is a relation
bot:Tree bot:has_part bot:Branch
Trunk
Has-part
Root
Plant
Fonguscereals
is_a
Tree
Has-
part
Branch
bot:Tree bot:has-part bot:Branch
bot:Plant bot:has-part bot:Root
rdfs:subClassOf
Has-
part
Root

Example: tree in DBPedia
29
dbpedia-
owl:tree
dbpedia-owl:Speciesdbpedia-owl:Place
SEMANTICS - NLP and KE at the era of SemWeb: Semantic relations -
Aussenac
12/09/2017

dbpedia-
owl:PhysicalEntity
rdfs:subClassOf
dbpedia-
owl:Organism
Example: Plants in DbPedia
30
owl:SameAs
yago:WordNet_Plant_
100017222
dbpedia-
owl:Plant
dbpedia-
owl:Acer_Stone
bergae
dbpedia-
owl:Alopecurus_ca
rolinianus
dbpedia-
owl:Alsmithia_long
ipes
dbpedia-owl:…
rdf:typerdf:type
rdf:typerdf:type
SEMANTICS - NLP and KE at the era of SemWeb: Semantic relations - Aussenac12/09/2017

Outline of the talk

Finding semantic relations,
some parameters
• Knowledge sources
– human experts, text
– existing semantic resources
– Domain specific vs general knowledge
• Text collection(s)
– Size, domain specific vs general
– Structure, quality of writing
– Textual genre (knowledge rich text?)
• Target representations
– Input/ output format of the process
– Nature of the semantic relation

Finding semantic relations,
some parameters
• Extraction techniques from text
– “obvious” language regularities, known relations and
classes (or entities) -> Patterns
– “more implicit” language regularities, medium size
corpora, open list of classes/entities -> supervised
learning
– Very large corpora, unexpected relations ->
unsupervised learning
• Validation
– What makes a relation representation valid?
Relevant?

Historic perspective
on relation extraction techniques
• Early period: around 1990
– Patterns (Hearst, 1992) to explore definitions
– Learning selectional preferences (Resnick)
– Machine Learning : ASIUM (Faure, Nedellec)
– Relations between classes
• From 2000 to 2010: more patterns, more learning
– Association rules (Maedche & Staab, 2000)
– Supervised Learning from positive/ negative exemples
– Joint use of various methods (Malaisé, 2005), Text2Onto (Cimiano, 2005), RelExt
– Relations between entities
• Since 2005: open relation extraction
– Semi-supervised learning from small sets of data
– Unsupervised learning: KnowItAll (Etzioni et al., 2005), TextRunner (Banko, 2007)
– Distant supervision (using a KB) ; deep learning
– Very large corpora (web)

Pattern-based relation extraction
• Hearst, 1992. Patterns for hypernymy in English
“Y such as X ((, X)* (, and|or ) X)”
“such Y as X”
“X or other Y”
“X and other Y”
“Y including X”
“Y, especially X”
• A shared list of patterns for French: MAR-REL
– CRISTAL project (linguistics and NLP)
– 3 types of binary relations: hypernymy, meronymy, cause
– UIMA format
– Evaluation on various corpora
– http://redac.univ-tlse2.fr/misc/mar-rel_fr.html

Tuning a pattern …
an endless effort ?
• On appelle route nationale une route gérée par l’état.
• Sur cette carte, on symbolise par un triangle un sommet de plus de 2000m.
• Il appelle souvent son chat la nuit. -> error
• On dénommait Louis-Philippe « la poire ». -> missed
• On appellera dans la suite de ce mémoire relation lexicale une relation qui …
-> missed

Pattern based relation extraction,
known issues
• A tree comprises at least a trunk,
roots and branches.
• With branches reaching the ground,
the willow is an ornamental tree.
• The tree of the neighbor has been
delimed.
• He climbs on the branches of the tree.
• This tree is wonderful. Its branches
reach the ground.
• Plant tangerine trees in a sheltered
place out of the wind.
• verb: lexical variation; enumeration >
various parts; modality (exactly, at
least, at most, often, …)
• With: meronymy pattern only in some
genres (such as catalogs, biology
documents); insertion between the
arguments
• Delimed : Term and pattern are in the
same word; implicitness: requires
background knowledge: delimed ->
has_part branches (and branches are
cut)
• Of : Very ambiguous mark; polysemy
reduced in [verb N1 of N2]
• Its : reference; necessity to take into
account two sentences
• Out of: negative form: representation
issue

Pattern-based relation extraction,
other issues
• Not enough flexibility
– not able to handle (unexpected) variations
– Miss find many relations
– Need adaptation to be relevant on a new corpus
• Too strong "matching" between the sentence and
the pattern itself
• Generic patterns
– widely used with poor results (no surprise)
– often appear as a baseline
• Building relevant domain/corpus-specific patterns
is time consuming and difficult

Using ML to learn patterns (1)
• Patterns are seen (and stored) as lexicalizations of ontology properties
• Patterns are “extracted” from syntactic dependencies between related
entities (in triples)
• Assumes that patterns are structured around ONE lexical entry
• Lemon format for lexical ontologies
• Entries can be frames
ATOLL—A framework for the automatic induction of ontology lexica
S. Walter, C. Unger, P. Cimiano, DKE (94), 148-162 (2014)

• Patterns are seen (and stored) as lexicalizations of ontology properties
• Patterns are “extracted” from syntactic dependencies between related
entities (in triples)
• Assumes that patterns are structured around ONE lexical entry
• Lemon format for lexical ontologies
• Entries can be frames
ATOLL—A framework for the automatic induction of ontology lexica
S. Walter, C. Unger, P. Cimiano, DKE (94), 148-162 (2014)

12/09/2017
Aussenac
41
Michelle Obama is the wife of Barack Obama, the current president.
Michelle Obama allegedly told her husband, Barack Obama, to ..
Michelle Obama, the 44th first lady and wife of President Barack
Dbpedia:spouse
Find all lexicalizations of the entities: Michelle Obama, Mrs. Obama, Michelle
Robinson …

12/09/2017
Aussenac
42
• Pattern = shortest path btw the 2 entities in the dependency graph
[MichelleObama (subject), wife (root), of (preposition), BarackObama (object)]
• Lexical entry in the ontology

Finding semantic relations:
what can large corpora and machine
learning do for you ?
• Learning patterns
– Poor results
– Requires very large data sets
– Reasonable for general
knowledge
• Learning relations
– Much more relevant
– Large variety of approaches in
the state of the art
– Key step = select feature

Using ML to learn relations:
Hypotheses
• A large variety of learning algorithms
– classification
– regression
– Probabilities (naives Bayes)
– Linear separation …
• Classification = grouping similar learning objects
– Ojects are designed from input sentences
– Sentences where two arguments occur
– Either vectors or graphs or lists made of features
– Similarity measure: cosine or cartesian distance or
sequence alignment for graphs

Main stages of the process
1. Preprocessing
– Tagging the entities to be considered as arguments
– NLP preprocessing
2. Object representation
– Collect sentences where pairs co-occur
– Identify features
– Represent sentences with features
3. Training the algorithm (if supervised)
4. Running the trained model

Example2: Learning domain specific
relations using ALVIS-RE (1)
1. Preprocessing (AlvisNLP/ML platform)
– Tokenization in words and sentences
– Lemmatization, POS tagging using CCS parser
– Named Entity tagging (canonical form)
– Dependency relations (graph)
– Semantic relations are added when known (positive examples)
– Word sequence relations (wordPath)
VALSAMOU D., Automatic Information Extraction from scientific scholar to build a network of biological
regulations involved in the seed development of Arabidopsis Thaliana. ED STIC, univ. Paris Sud. 2017

• Representation as a path
• 3 experiments : depencies, surface (wordPath relations)
and a combination of the 2
• Find the shortest path between the terms Arg1 and Arg2.

• Paths are turned as sequences w,rel
• Empty nodes (gaps) are added if needed + weight (gap penalty)
• Weights are assigned to some words

Example2: Learning domain
specific relations using ALVIS-RE (4)
4. Classification
– Use SVM algorithm
– Improved using semantic information
• Distributional representations (DISCO or Word2Vec)
• Classes manually related to each other
• Classes from WordNet
• Evaluation on a real corpus

Example3: learning relations from
enumerative structures
12/09/2017
Aussenac
50
IS_A
IS_A
Learning relations from an parallel enumerative structure =
- classification task to identify the relation (IS_A, part_Of, other)
- Term extraction to identify the primer and the items
J.-P.Fauconnier, M. Kamel. Discovering Hypernymy Relations using Text Layout (regular paper). Joint Conference
on Lexical and Computational Semantics (SEM 2015),(ACL), 2015.

Relation extraction:
learning relations from enumerative structures
• Corpus
– 745 enumerative structures from
Wikipedia pages
– 3 relation types: taxonomic,
ontological_non_taxonomic,
non_ontological
• Classification task
– Feature definition
– Automatic evaluation of features
– 3 algorithms are compared : SVM,
MaxEntropy and baseline (majority)
– Training of the 2 algorithms
• Results
– 82% f-measure for SVM
– Best result with a 2 step process
(ontological yes/no -> feature and
then taxonomic yes/no)

Example4: comparing patterns and
ML for hypernym relation extraction
• Overall objectif
– Define various relation extraction techniques
– Adapted to various ways to express relations
• Sempedia project
– To enrich the French DbPedia
– To extract relations from Wikipedia pages
• Experiment on desambiguation pages
– Contain definitions and many hypernym relations
– General knowledge
• Techniques
– Patterns
– Basic pre-processing (no dependency parsing)
– Distant supervised learning

Example4: Wikipedia
desambiguation pages
Different ways to express hypernym relations in the same corpus
07/09/2017 Extracting hypernym relations 53

Example4 : Application to Wikipedia
Disambiguation Pages
• Corpora
– Reference corpus: 20 pages ; manual annotation (entities and relations linking entities)
– Training corpus: all remaining French disambiguation pages (5904 pages)
• Semantic resource : BabelNet (www.babel.org)
– very large multilingual semantic network with about 14 million entries (Babel synsets)
– connects concepts and named entities with semantic relations
– rich in hypernym relations
• Features

Example 4: Processing chain
Preprocessing
Corpus (Wikipedia
disambiguation pages)
Annotated corpus
Term pairs
extraction
(<T1
1, T1
2>, sent1>)
(<T2
1, T2
2>, sent2>)
(<T3
1, T3
2>, sent3>)
…
Semantic
resource
BabelNet
{ <Tj
1, Tj
2>,sentj, <traitj
1, …, traitj
p>, neg>
}j
Gazetteer
(Babelnet terms)
TTG
{ <Ti
1, Ti
2>,senti, <traiti
1, …, traiti
p>, pos >}i
Feature vectors
building
test set (2000 +, 2000 -)
{ <Tj
1, Tj
2>,sentj, <traitj
1, …, traitj
p>, neg>
}j
training set (4000 +, 4000 -)
Binary logistic
regression
(MaxEnt)
Evaluation
(precision, recall,
F-measure)
Learning
model
{ <Ti
1, Ti
2>,senti, <traiti
1, …, traiti
p>, pos >}i

Example4: Application to
Wikipedia Disambiguation Pages
• Evaluation on the reference corpus
– 688 true positive examples and 278 true negative examples
– Best results with window size of 3
– Comparison between 2 baselines and 2 models
• Baseline1: generic lexico-syntactic patterns for French
• Baseline2: generic patterns AND ad-hoc patterns for the disambiguation pages
• Model_POSL: trained with vectors composed of POS and lemma features
• Model_AllFeatures: trained with vectors composed of all features

Example4: Application to Wikipedia
Disambiguation Pages - discussion
– Number of true positive hypernym relations per type of hypernym
expression
– Quantitative gain: machine learning identifies more examples, no
development cost, ensuring a systematic and less empirical approach.
– Impact of the way relations are expressed:
• ML performs as well as patterns on well-written text
• Ad-hoc pattern perform (a little) better on low-written text
• ML can identify all forms of relation expressions (current patterns are unable
to identify relations with head modifiers)

• Examples
– correctly identified by ML
– would require additional ad-hoc patterns > extra cost
(1) Louis Babel, prêtre-missionnaire oblat et explorateur du Nouveau-Québec
(1826-1912) .
<Louis Label, prêtre-missionnaire oblat>
<Louis Label, explorateur du Nouveau-Quebec>
(2) La fontaine a aussi désigné le “vaisseau de cuivre ou de quelque autre métal, où
l’on garde de l’eau dans les maisons”, et encore le robinet de cuivre par où coule
l’eau d’une fontaine, ou le vin d’un tonneau, ou quelque autre liqueur que ce soit.
<fontaine, robinet de cuivre>
Example4: Application to Wikipedia
Disambiguation Pages

Example5: NN to extract relations
from scientific papers
• Corpus
– Full scientific papers (ISTEX French project)
– 15 years of Nature journal (50Ko of text)
• Open relation extraction with distant supervision
– Semantic resource: NCIT (thesaurus)
– Learning objects: vector made of the word embedding vectors
of a subset of lemmas of the sentences (around arguments)
– Learning algorithm: Self Organizing Maps
• Results
– Find 13 classes 5 of which as easy to interpret with a majority of
relations of one type
– 80% of accuracy for hypernym relations

Example5: NN to extract relations
from scientific papers
• Difficulties with SOM
– Size of the map > computation time
– Interpretation of the resulting classes
– Evaluation of the recall
• Limitations of supervised learning hypotheses
– One sentence may contain more than 2 domain concepts or
entities > arbitrary selection of the 1st 2
– 2 entities may be related by several relations in the semantic
resource > which annotation?
– The vocabulary in the corpus and semantic resource may differ
• Supervised learning requires expertise to adjust
– The number of iterations to get the optimal number of classes
– The size of the map or layers in the NN
– The features that form the classified objects
Person Work-in Company
holds

Towards more complementarity
ML can be used to
• To learn Patterns
• To find relation in complex
and long sentences
• For open relation
extraction, when the list of
possible relations is not
known
• When a domain resource is
available, ML with distant
supervision
• Deep learning makes the
process fully automatic but
requires very large corpora
Pattern can be used as
• input to define features: tag
sequences matching the
pattern (will become a
feature)
• an "easy method" when
regularities are obvious (cf
Polysemy pages in
Wikipedia)
• To boot-strap and
automatically identify
positive examples

Conclusion
• Context
– complexity and diversity of what we call semantic relation
extraction
– A lot of work has been done in designing and evaluating
patterns for semantic relations
• Many perspective to improve relation extraction
– Capitalize better exiting patterns
– Collect results about the most relevant features and the most
efficient representation to feed ML algorithms
– Need to implement pre-processing chains (even with NN
algorithms) for a larger set of languages
– Study how performant each technique is on a variety of NL text
where relations are expressed in many ways
– Design a plat-form where various methods could be used
together

Aussenac semanticsnl pwebsem2017-v4

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