OpenLogos Semantico-Syntactic Knowledge-Rich Bilingual Dictionaries
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![OpenLogos
Seman-co-‐Syntac-c
Knowledge-‐Rich
Bilingual
Dic-onaries
Anabela
Barreiro1,
Fernando
Ba0sta1,2,
Ricardo
Ribeiro1,2,
Helena
Moniz1,3,
Isabel
Trancoso1,4
1INESC-‐ID,
2ISCTE-‐IUL,
3FLUL/CLUL,
4IST
{abarreiro;fmmb;rdmr;helenam;imt}@l2f.inesc-id.pt!
http://www.l2f.inesc-id.pt/!
Characteris0cs
– Representa0on
schema
with
eclec0c
categories
– Designed
to
work
in
concert
with
the
lexical
resources
and
linguis0c
rules
(transfer
(TRAN)
and
seman0co-‐syntac0c
(SEMTAB)
rules)
– Easy
mapping
from
natural
to
symbolic
language,
represen0ng
both
meaning
and
structure
in
a
con0nuum,
undissociated,
represented
in
the
same
layer,
based
on
the
belief
that
seman0cs
of
a
word
oRen
affects
the
surrounding
syntax
– Extensible
system,
designed
so
that
developers
would
expand
and
add
to
its
capabili0es
– Ini0ally
developed
for
English,
but
many
of
its
elements
are
universal
(mostly
nouns,
adjec0ves,
and
adverbs)
and
applicable
to
other
languages
Representa0on
– SAL
knowledge
is
embedded
in
the
dic0onary
in
the
form
of
numeric
codes
(SAL
mnemonics
are
used
for
easier
understanding)
• E.g.
the
noun
(N)
table
has
two
SAL
representa0ons:
– COsurf
–
concrete,
surface
– INdata
–
informa0on,
recorded
data
– Nouns
have
12
supersets.
Superset
measure
(ME)
has
3
sets
and
11
subsets:
• SAL
codes
for
nouns
represent
seman0c
groupings,
and
are
language
independent,
as
concepts
are
transverse
across
languages
– Verbs
are
subdivided
in
3
types:
intransi0ve,
weak
transi0ve
and
strong
transi0ve.
Intransi0ve
verbs
have
3
supersets:
mo0onal
(INMO),
opera0onal
(INOP),
and
existen0al
(INEX)
• Existen0al
intransi0ve
verbs
include
be
and
be-‐
subs0tutes
that
take
predicate
nouns
and
adjec0ves
– Adjec-ves
are
classified
in
2
types:
descrip0ve
and
par0cipial,
sub-‐classified
according
to
syntac0c
rela0onships
with
other
words
• syntac0c
pa]erns
for
the
descrip0ve
pre-‐clausal
good-‐type
adjec0ves
– OpenLogos
(OL)
is
the
open
source
deriva0ve
of
the
Logos
machine
transla0on
(MT)
system
– OL
strength
resides
in
its
lexical
resources,
the
knowledge-‐rich
bilingual
dic-onaries
• contain
seman0co-‐syntac0c
knowledge
and
ontological
rela0ons
for
all
lexical
entries
represented
at
an
abstract/higher
level
by
the
Seman0co-‐
Syntac0c
Abstrac0on
Language
–
SAL
• present
other
idiosyncrasies
that
dis0nguish
them
from
other
publicly
available
dic0onaries
Mo0va0on
– OL
resources
were
used
successfully
in
the
Logos
commercial
MT
product
during
2-‐3
decades
• validated
by
the
Logos
development
team
and
clients
– Possible
applica0ons
• basis
for
new
linguis0c
and
NLP
tools,
especially
for
poor-‐resourced
languages
• enhancement
of
other
MT
systems
Bilingual
Dic0onaries:
EN
>
GE/FR/IT
– Verbs,
nouns
and
adjec0ves
are
clearly
the
most
represented
classes,
as
they
reach
more
than
80,000
entries
for
each
target
language.
– Dic0onaries
stored
in
self-‐contained
XML
files
• easily
addressed
by
small
programs
• supported
by
exis0ng
efficient
XML
APIs
– Example
for
the
verb
entry
depart,
extracted
from
the
English-‐French
dic0onary
Introduc0on
Seman0co-‐Syntac0c
Knowledge
– Part-‐of-‐speech
(POS)
– Gender
(GEN)
– Number
(NUM)
– Morphological
paradigms
(PAT)
for
source
and
target
words
• make
it
possible
to
map
inflected
forms
across
languages
and
improve
agreement
in
SMT
– Head
word
(HEAD)
in
mul0word
• useful
to
correct
MT
problems
related
to
agreement
within
mul0words
or
within
larger
units
(e.g.
between
nominal
mul0words
and
verb
or
agreement
within
verbal
mul0words)
– Homographs
(HOMO)
• homographs
are
a
major
source
of
transla0on
errors
and
their
iden0fica0on
is
crucial
– Auxiliary
(AUX)
• helps
improve
precision
in
the
transla0on
when
auxiliary
choice
is
subtle
– Alternate
word
(ALT)
• nominaliza0on
(process
noun),
predicate
adjec0ve,
etc.
-‐
useful
for
paraphrasing
purposes
– Causa0ve
verb
(CAUS)
– Reflexive
verb
(REFL)
– Aspectual
verb
(ASP)
– Seman0co-‐Syntac0c
Knowledge
(SAL)
• interlingua-‐style
hierarchical
taxonomy
with
over
1,000
elements,
embracing
all
POS
• 3
levels
of
representa0on:
superset
(SUPER),
set
(SET),
and
subset
(SUB)
-‐
embedded
in
the
dic0onary
entries
and
in
the
transla0on
system’s
rules
(help
with
disambigua0on).
E.g.
pipe,
hose:
OpenLogos
Data
3
2
1
– Three
bilingual
dic0onaries
were
created
• English-‐French;
English-‐German;
English-‐Italian
• online
and
free
for
research
purposes
– h]p://metanet4u.l2f.inesc-‐id.pt/
– The
resources
contain
seman0co-‐syntac0c
knowledge
concerning
the
conceptual
formaliza0on
of
things,
ideas,
rela0onships,
disposi0ons,
condi0ons,
processes,
etc.
• valuable
for
MT
and
other
NLP
applica0ons
• stored
in
XML
format
for
easy
processing
– In
the
future,
we
will
make
available
three
complementary
bilingual
dic0onaries
• English-‐Portuguese;
English-‐Spanish;
German-‐
English
Acknowledgments
– This
work
was
supported
by
na0onal
funds
through
Fundação
para
a
Ciência
e
a
Tecnologia,
under
grants
SFRH/BPD/91446/2012
and
SFRH/BPD/95849/2013
and
project
PEst-‐OE/EEI/LA0021/2013
Conclusions
and
Future
Work
5
Resul0ng
Resources
4
Instituto de Engenharia de Sistemas e Computadores
Investigação e Desenvolvimento em Lisboa
Laboratório de Sistemas de Língua Falada
id
EN-‐GE
EN-‐FR
EN-‐IT
Noun
1
28266
25910
23505
Verb
2
33855
33354
33021
Adverb
(loca0ve)
3
465
442
450
Adjec0ve
4
21219
20749
20518
Pronoun
5
121
121
121
Adverb
(manner,
agency,
degree)
6
2207
2167
2173
Preposi0on
(non-‐loca0ve)
11
140
140
139
Auxiliary
and
Modal
12
34
34
34
Preposi0on
(loca0ve)
13
148
148
148
Definite
Ar0cle
14
194
194
189
Indefinite
Ar0cle
15
66
66
65
Arithmate
in
Apposi0on
16
208
208
203
Nega0ve
17
2
2
2
Rela0ve
and
Interroga0ve
Pronoun
18
23
23
20
Conjunc0on
19
160
160
160
Punctua0on
20
30
30
30
Total
87138
83748
80778
nouns%
concrete%
func+onals%
conduits%
word%class%
superset%
set%
subset%barriers% containers%
…%…%
…% …%
…%…%
<Entry
source="depart"
target="qui]er">
<source
head_word="1"
homograph="no"
word_type="01">
<pos
descrip0on="Verb"
wclass="02"/>
<morphology>
<inflec0on
descrip0on="like
walk,
walked,
walking"
example="walk"
id="1"/>
</morphology>
<sal
code="13,98,596"
descrip0on="create,
etc."
mnemonic="generictransi0ve4"
set="other98"/>
</source>
<target
aux="1"
head_word="1"
word_type="01">
<pos
descrip0on="Verb"
wclass="02"/>
<morphology>
<inflec0on
descrip0on="regular
ending
in
-‐er:
parler"
example="parler"
id="3"/>
</morphology>
</target>
</Entry>
<Entry
source="depart"
target="par0r">
<source
head_word="1"
homograph="no"
word_type="01">
<pos
descrip0on="Verb"
wclass="02"/>
<morphology>
<inflec0on
descrip0on="like
walk,
walked,
walking"
example="walk"
id="1"/>
</morphology>
<sal
code="10,24,596"
descrip0on="from
=
away
from,
off
of,
out
of"
set="governsawayfrom"/>
</source>
<target
aux="2"
head_word="1"
word_type="01">
<pos
descrip0on="Verb"
wclass="02"/>
<morphology>
<inflec0on
descrip0on="Irreg.
in
-‐ir
with
shortened
stem
..."
example="par0r"
id="12"/>
</morphology>
</target>
</Entry>
Mnemonic
Example
Verb
Example
Sentence
INEXbe-‐type
be
She
was
at
the
seashore
all
summer.
INEXbecome-‐type
become,
remain
He
became
a
doctor
at
a
very
young
age.
INEXgrow-‐type
sound,
look
Their
voices
sounded
cheerful.
INEXseem-‐type
seem,
appear
He
seemed
happy
with
the
results.
Mnemonics
Descrip-on
Examples
MEabs
abstract
measurable
concepts
humidity,
length
MEdis
discrete
measurable
concepts
sum,
increment
MEunit
units
of
measure
See
subsets
MEunitwt
units
of
weight
ounce,
pound
MEunitvel
units
of
velocity
mph,
megahertz
MEunitvol
unites
of
volume
measure
gallon,
liter
MEuni]emp
units
of
temperature
degrees
celsius
MEunitener
units
of
energy/force
wa],
horsepower
MEunitsys
measurement
systems
fahrenheit,
kelvin
MEunitdur
units
of
dura0on
hour,
year
MEunitspec
specialized
units
of
measure
oersted,
ohm
MEunitvalue
units
of
money/value
dollar,
euro
MEunitlin
units
of
linear/area
measure
inch,
mille
MEundif
undifferen0ated
measure
degree,
share
PaQern
Example
Sentence
It
is
ADJ
that
It
is
silly
that...
It
is
ADJ
for
NP
that
It
is
good
for
the
employees
that...
It
is
ADJ
to
VP
It
is
smart
to
exercise.
It
is
ADJ
for
NP
to
VP
It
was
silly
for
them
to
expect...
It
is
ADJ
V'ing
It
is
smart
doing
the
right
thing.
NP
is
ADJ
to
VP
John
is
smart
to
exercise.](https://image.slidesharecdn.com/5mdzczpksww0nhwkkpoa-140601172152-phpapp02/85/openlogos-semanticosyntactic-knowledgerich-bilingual-dictionaries-1-320.jpg)
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OpenLogos Semantico-Syntactic Knowledge-Rich Bilingual Dictionaries
- 1. OpenLogos Seman-co-‐Syntac-c Knowledge-‐Rich Bilingual Dic-onaries Anabela Barreiro1, Fernando Ba0sta1,2, Ricardo Ribeiro1,2, Helena Moniz1,3, Isabel Trancoso1,4 1INESC-‐ID, 2ISCTE-‐IUL, 3FLUL/CLUL, 4IST {abarreiro;fmmb;rdmr;helenam;imt}@l2f.inesc-id.pt! http://www.l2f.inesc-id.pt/! Characteris0cs – Representa0on schema with eclec0c categories – Designed to work in concert with the lexical resources and linguis0c rules (transfer (TRAN) and seman0co-‐syntac0c (SEMTAB) rules) – Easy mapping from natural to symbolic language, represen0ng both meaning and structure in a con0nuum, undissociated, represented in the same layer, based on the belief that seman0cs of a word oRen affects the surrounding syntax – Extensible system, designed so that developers would expand and add to its capabili0es – Ini0ally developed for English, but many of its elements are universal (mostly nouns, adjec0ves, and adverbs) and applicable to other languages Representa0on – SAL knowledge is embedded in the dic0onary in the form of numeric codes (SAL mnemonics are used for easier understanding) • E.g. the noun (N) table has two SAL representa0ons: – COsurf – concrete, surface – INdata – informa0on, recorded data – Nouns have 12 supersets. Superset measure (ME) has 3 sets and 11 subsets: • SAL codes for nouns represent seman0c groupings, and are language independent, as concepts are transverse across languages – Verbs are subdivided in 3 types: intransi0ve, weak transi0ve and strong transi0ve. Intransi0ve verbs have 3 supersets: mo0onal (INMO), opera0onal (INOP), and existen0al (INEX) • Existen0al intransi0ve verbs include be and be-‐ subs0tutes that take predicate nouns and adjec0ves – Adjec-ves are classified in 2 types: descrip0ve and par0cipial, sub-‐classified according to syntac0c rela0onships with other words • syntac0c pa]erns for the descrip0ve pre-‐clausal good-‐type adjec0ves – OpenLogos (OL) is the open source deriva0ve of the Logos machine transla0on (MT) system – OL strength resides in its lexical resources, the knowledge-‐rich bilingual dic-onaries • contain seman0co-‐syntac0c knowledge and ontological rela0ons for all lexical entries represented at an abstract/higher level by the Seman0co-‐ Syntac0c Abstrac0on Language – SAL • present other idiosyncrasies that dis0nguish them from other publicly available dic0onaries Mo0va0on – OL resources were used successfully in the Logos commercial MT product during 2-‐3 decades • validated by the Logos development team and clients – Possible applica0ons • basis for new linguis0c and NLP tools, especially for poor-‐resourced languages • enhancement of other MT systems Bilingual Dic0onaries: EN > GE/FR/IT – Verbs, nouns and adjec0ves are clearly the most represented classes, as they reach more than 80,000 entries for each target language. – Dic0onaries stored in self-‐contained XML files • easily addressed by small programs • supported by exis0ng efficient XML APIs – Example for the verb entry depart, extracted from the English-‐French dic0onary Introduc0on Seman0co-‐Syntac0c Knowledge – Part-‐of-‐speech (POS) – Gender (GEN) – Number (NUM) – Morphological paradigms (PAT) for source and target words • make it possible to map inflected forms across languages and improve agreement in SMT – Head word (HEAD) in mul0word • useful to correct MT problems related to agreement within mul0words or within larger units (e.g. between nominal mul0words and verb or agreement within verbal mul0words) – Homographs (HOMO) • homographs are a major source of transla0on errors and their iden0fica0on is crucial – Auxiliary (AUX) • helps improve precision in the transla0on when auxiliary choice is subtle – Alternate word (ALT) • nominaliza0on (process noun), predicate adjec0ve, etc. -‐ useful for paraphrasing purposes – Causa0ve verb (CAUS) – Reflexive verb (REFL) – Aspectual verb (ASP) – Seman0co-‐Syntac0c Knowledge (SAL) • interlingua-‐style hierarchical taxonomy with over 1,000 elements, embracing all POS • 3 levels of representa0on: superset (SUPER), set (SET), and subset (SUB) -‐ embedded in the dic0onary entries and in the transla0on system’s rules (help with disambigua0on). E.g. pipe, hose: OpenLogos Data 3 2 1 – Three bilingual dic0onaries were created • English-‐French; English-‐German; English-‐Italian • online and free for research purposes – h]p://metanet4u.l2f.inesc-‐id.pt/ – The resources contain seman0co-‐syntac0c knowledge concerning the conceptual formaliza0on of things, ideas, rela0onships, disposi0ons, condi0ons, processes, etc. • valuable for MT and other NLP applica0ons • stored in XML format for easy processing – In the future, we will make available three complementary bilingual dic0onaries • English-‐Portuguese; English-‐Spanish; German-‐ English Acknowledgments – This work was supported by na0onal funds through Fundação para a Ciência e a Tecnologia, under grants SFRH/BPD/91446/2012 and SFRH/BPD/95849/2013 and project PEst-‐OE/EEI/LA0021/2013 Conclusions and Future Work 5 Resul0ng Resources 4 Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa Laboratório de Sistemas de Língua Falada id EN-‐GE EN-‐FR EN-‐IT Noun 1 28266 25910 23505 Verb 2 33855 33354 33021 Adverb (loca0ve) 3 465 442 450 Adjec0ve 4 21219 20749 20518 Pronoun 5 121 121 121 Adverb (manner, agency, degree) 6 2207 2167 2173 Preposi0on (non-‐loca0ve) 11 140 140 139 Auxiliary and Modal 12 34 34 34 Preposi0on (loca0ve) 13 148 148 148 Definite Ar0cle 14 194 194 189 Indefinite Ar0cle 15 66 66 65 Arithmate in Apposi0on 16 208 208 203 Nega0ve 17 2 2 2 Rela0ve and Interroga0ve Pronoun 18 23 23 20 Conjunc0on 19 160 160 160 Punctua0on 20 30 30 30 Total 87138 83748 80778 nouns% concrete% func+onals% conduits% word%class% superset% set% subset%barriers% containers% …%…% …% …% …%…% <Entry source="depart" target="qui]er"> <source head_word="1" homograph="no" word_type="01"> <pos descrip0on="Verb" wclass="02"/> <morphology> <inflec0on descrip0on="like walk, walked, walking" example="walk" id="1"/> </morphology> <sal code="13,98,596" descrip0on="create, etc." mnemonic="generictransi0ve4" set="other98"/> </source> <target aux="1" head_word="1" word_type="01"> <pos descrip0on="Verb" wclass="02"/> <morphology> <inflec0on descrip0on="regular ending in -‐er: parler" example="parler" id="3"/> </morphology> </target> </Entry> <Entry source="depart" target="par0r"> <source head_word="1" homograph="no" word_type="01"> <pos descrip0on="Verb" wclass="02"/> <morphology> <inflec0on descrip0on="like walk, walked, walking" example="walk" id="1"/> </morphology> <sal code="10,24,596" descrip0on="from = away from, off of, out of" set="governsawayfrom"/> </source> <target aux="2" head_word="1" word_type="01"> <pos descrip0on="Verb" wclass="02"/> <morphology> <inflec0on descrip0on="Irreg. in -‐ir with shortened stem ..." example="par0r" id="12"/> </morphology> </target> </Entry> Mnemonic Example Verb Example Sentence INEXbe-‐type be She was at the seashore all summer. INEXbecome-‐type become, remain He became a doctor at a very young age. INEXgrow-‐type sound, look Their voices sounded cheerful. INEXseem-‐type seem, appear He seemed happy with the results. Mnemonics Descrip-on Examples MEabs abstract measurable concepts humidity, length MEdis discrete measurable concepts sum, increment MEunit units of measure See subsets MEunitwt units of weight ounce, pound MEunitvel units of velocity mph, megahertz MEunitvol unites of volume measure gallon, liter MEuni]emp units of temperature degrees celsius MEunitener units of energy/force wa], horsepower MEunitsys measurement systems fahrenheit, kelvin MEunitdur units of dura0on hour, year MEunitspec specialized units of measure oersted, ohm MEunitvalue units of money/value dollar, euro MEunitlin units of linear/area measure inch, mille MEundif undifferen0ated measure degree, share PaQern Example Sentence It is ADJ that It is silly that... It is ADJ for NP that It is good for the employees that... It is ADJ to VP It is smart to exercise. It is ADJ for NP to VP It was silly for them to expect... It is ADJ V'ing It is smart doing the right thing. NP is ADJ to VP John is smart to exercise.