Lodz Papers in Pragmatics 13.1 (2017): 3–24
DOI:10.1515/lpp-2017-0002

Ewa Rudnicka
Wrocław University of Technology
Francis Bond
Nanyang Technological UniversityŁukasz Grabowski
University of Opole
Maciej Piasecki
Wrocław University of Technology
Tadeusz Piotrowski
University of Wrocław

TOWARDS EQUIVALENCE LINKS BETWEEN SENSESIN PLWORDNET AND PRINCETON WORDNET
Abstract
The paper focuses on the issue of creating equivalence links in the domain of bilingual
computational lexicography. The existing interlingual links between plWordNet andPrinceton WordNet synsets (sets of synonymous lexical units – lemma and sense pairs) are
re-analysed from the perspective of equivalence types as defined in traditional
lexicography and translation. Special attention is paid to cognitive and translational
equivalents. A proposal of mapping lexical units is presented. Three types of links aredefined: super-strong equivalence, strong equivalence and weak implied equivalence. The
strong equivalences have a common set of formal, semantic and usage features, with someof their values slightly loosened for strong equivalence. These will be introduced manuallyby trained lexicographers. The sense-mapping will partly draw on the results of the existing
synset mapping. The lexicographers will analyse lists of pairs of synsets linked byinterlingual relations such as synonymy, partial synonymy, hyponymy and hypernymy.
They will also consult bilingual dictionaries and check translation probabilities in a parallel
corpus. The results of the proposed mapping have great application potential in the area of
natural language processing, translation and language learning.
Keywords
equivalence, wordnets, interlingual mapping, synset, lexical unit, sense-level

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Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki& Tadeusz Piotrowski
Towards equivalence links between senses in plWordNet and Princeton WordNet

1 Introduction
The paper presents a proposal of a system of equivalence links between lexicalunits of plWordNet of Polish (cf. Piasecki et al 2017) and Princeton WordNet ofEnglish (cf. Fellbaum 1998). Wordnets are big electronic lexico-semantic
databases in which words, or more specifically, their lexical meanings (also calledword senses), are connected by a rich network of lexico-semantic relations such assynonymy, hyponymy, or antonymy, to name just a few. The relations are the main
determinant of meaning in a wordnet, although they are supplemented by glosses1
and usage examples. For each sense, also a coarse-grained semantic domain isgiven (called a lexicographic file in Princeton WordNet). Moreover, in plWordNet
the information about the stylistic register of each lexical unit is provided (whether
it is general or any of the marked ones). Many wordnets are already interconnected forming large, multilingual networks, the biggest one being OpenMultilingual WordNet (cf. Bond and Foster 2013). There are two basic units oforganisation within a wordnet: lexical units that are lemma – Part of Speech –sense triplets and synsets that can be shortly and informally described as sets ofsynonymous lexical units. The inter-linking between wordnets takes place at thelevel of synsets. This is unlike in traditional bilingual dictionaries that offer (a setof) equivalents for each of the senses of a selected word. Still, as in traditional
dictionaries, the choice of the right equivalent (for a given context) is left to adictionary or wordnet user.
For some time there has been a trend to help a dictionary user in this task by
providing different types of clues and hints (cf. Crenn 1996; Yong and Peng 2007;
Adamska-Sałaciak 2013; Lew 2013; Kamiński 2016). First, the specific sense of aword is signalled by its part of speech and often by its synonym in the source
language. Sometimes also the name of a semantic domain and/or a register is given
as a label. This information helps a dictionary user to identify the meaning of asource language item and select the right sense listed in a dictionary. However,substantially less help is offered in the choice of the best equivalent in the target
language. Depending on the dictionary, usage examples are given; it also happensthat source-language lexical item in a dictionary is embedded in a sentence (whichis also translated) or additional minimal context is provided (e.g. with a collocate
of a source-language item) and translated as a longer phrase (Adamska-Sałaciak
2013: 223). Such solutions are used to handle the problem of interlingual
anisomorphism (that is, the fact that different languages structure reality in
1

A gloss is a form of short definition of a lexical unit (a triple: lemma, Part of Speech, sense
identifier, e.g. zamek: N 1 ‘a castle’) that comments on the intended meaning targeted andsupplements the primary definition in a form of the network of lexico-semantic relations.
Glosses are introduced to facilitate applications of plWordNet in the natural language
processing as well as human users comprehension of the meanings of the lexical units.

Lodz Papers in Pragmatics 13.1 (2017): 3–24
DOI:10.1515/lpp-2017-0002

5different ways) and a lack of absolute meaning equivalence, often calledsynonymy, between two languages (Adamska-Sałaciak 2013: 223–225). On theother hand, bilingual wordnets always provide source language item synonyms (ifthere are any), and a set of synonymous target language equivalents (if there are
more than one). The number of synonyms and the amount of information goingwith them depends on methodological assumptions underlying the construction ofa wordnet. These affect granularity of senses and the type and richness of semantic
information.
In an ideal world, a dictionary or a wordnet user would be offered enoughinformation to always properly identify the sense of the searched language item
and choose its right equivalent. That would require a very fine-grained network ofinter-lingual links (and a very rich network of intra-lingual links). Computers allow
us to do both. In this paper, we will sketch a proposal of a strategy of linking
wordnets at the level of lexical units, its smallest building parts. It will be based on
the results of the existing synset-level mapping between plWordNet and Princeton
WordNet and the recent theories of equivalence developed in bilingual
lexicography literature (cf. Piotrowski 2011; Adamska-Sałaciak 2014).

2 Equivalence in bilingual dictionaries and wordnets
2.1 Equivalence types
What both bilingual dictionaries and bilingual wordnets do is to provide pairs, orsets of language items corresponding in, broadly speaking, meaning (called
equivalents, esp. in dictionaries.). The status of the items and the degree of theircorrespondence may differ in both types of resources. It seems an obvious
assumption that a lexical resource should aim at including only language items of aconfirmed lexicality (cf. Svensen 2009: 102–103). Yet, in the absence of direct
lexicalised equivalents, some dictionaries and some, especially translated,
wordnets provide non-lexicalised multi-word target language expressions asequivalents. Some dictionaries mark them with a special font or font attribute tosignal the lack of a lexicalised equivalent (cf. Svensen 2009). Some wordnets put
them in the so called “artificial” synsets to mark their non-lexicality;
MultiWordNet calls them phrasets (cf. Bentivogli and Pianta 2004; Finnet, Lindén
and Carlson 2010). An example is the translation of the English synset {toilet_roll1} to an Italian synset { GAP } (signalling a lexical gap in Italian) and next to an
Italian phraset {rotolo_di_carta_igienica}. Some equivalence theorists, e.g.
Piotrowski (2011) or Adamska-Sałaciak (2014:7), call them explanatory
equivalents.

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Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki& Tadeusz Piotrowski
Towards equivalence links between senses in plWordNet and Princeton WordNet

Unlike a cognitive or a translational equivalent, an explanatory equivalent is not an
established TL unit, but a free TL [Target Language] combination: a succinct
paraphrase of the meaning of the SL [Source Language] headword. It resembles amini-deﬁnition in the target language, except that it is normally short.

A different kind of strategy is to employ a TL word that can be used in translatinga phrase or sentence, and which does not correspond on all features with the ST
word, for example it has a different grammatical category; this does not often
happen in bilingual dictionaries (such cases are handled in examples), but it does
occasionally happen in wordnets. An example could be English He is a good cookcorresponding to Polish (On) dobrze gotuje. Such equivalents, whose main featureis the absence of word-level correspondence (Héja 2016: 4), are called functional
by Adamska-Sałaciak (2014: 7):functional equivalence is a relation holding not between the meanings of individual
lexical items but between the meanings of longer stretches of text. Typically, the TL
text portion either contains a TL word of a different grammatical category than the
SL headword or features no element whatsoever directly corresponding to that
headword.

Some bi- and multilingual wordnets, e.g. plWordNet mapped onto Princeton
WordNet, employ an interlingual relation of cross-categorial synonymy to showcorrespondence in meaning between synsets belonging to different grammaticalcategories (cf. EuroWordNet, Vossen 2002; enWordnet, Rudnicka et al 2015.) Thisis done to at least partly specify the sense of the SL synset in addition to providing
only a very general inter-wordnet hyponymy link, cf. the strategy employed in the
mapping of adjectives between plWN and WN (Rudnicka et al 2015), for instance:(1){neokatechumenalny 1 `~related to neocatechumenate ’}inter-lingual-hyponymy->{related 1}
interlingual-cross-categorial-synonymy-> {neocatechumenate 1}

Still, there are no claims made with regard to the possibility of using inter-lingual
cross-categorial synonyms as any kind of equivalents in translation, although sucha possibility cannot be excluded, especially when lexical translation is combinedwith the adaptation of the target language syntactic-semantic structure of thetranslated expression.
Neither explanatory nor functional equivalents will be our main focus here. We
instead focus on equivalence links only between lexicalised source and target
language expressions. Lexicographers distinguish between two basic types of such
equivalents, which can have different names cognitive (or semantic) andtranslational. We have to stress that the term cognitive has little relation toLodz Papers in Pragmatics 13.1 (2017): 3–24
DOI:10.1515/lpp-2017-0002

7“cognitive” as used in cognitive linguistics. It is a shorthand description of the factthat this relation is based on mental associations, and these are founded on “naive
semantics”, rather than another relation, translational, based on actual use inconcrete translation. Adamska-Sałaciak (2014: 6) defines cognitive equivalent in
the following way:
A cognitive equivalent has a high explanatory potential, i.e., it is capable offaithfully rendering the meaning of the SL headword. Its identiﬁcation is often moreor less effortless, because it tends to spring to mind immediately after a bilingual
speaker (lexicographer) has been presented with an SL headword. Thanks to that,cognitive equivalents are frequently identical in different dictionaries for the samelanguage pair, which gives rise to the feeling that they are somehow “real” or “true.”On the downside, a cognitive equivalent is often too general to work as a translationof the SL item in a particular context.

Clearly, the cognitive equivalent, which first comes to mind of a competentbilingual speaker, without him or her being presented with any context (AdamskaSałaciak 2010: 400)2, is the typical “naive meaning” equivalent, while not always
the “use” equivalent. Some contexts require more specific and less generalequivalents. Therefore, apart from cognitive equivalents, bilingual dictionaries also
list translational equivalents, which are referred to as contextual equivalents by
Héja (2016: 3); they are defined by Adamska-Sałaciak (2014: 7) as follows:
A translational equivalent, by contrast, while not being wholly identical in meaning to
the SL headword, produces a good translation when substituted for it in a particular
context (not least because it has similar combinatory properties).

We have to note that the phrases that Adamska-Sałaciak uses, “to faithfully render
the meaning” or “to be wholly identical in meaning”, are based on the assumptionthat there is a simple method of comparing the meaning of two items from two
languages, i.e., that meaning is finite and easy to describe. Unfortunately, that is
not the case, but we shall not be concerned with this problem in this paper [seeTaylor (2012) for a more detailed discussion].

2

Adamska-Sałaciak (2010: 400) assumes that identification of a cognitive equivalent byskilled bilinguals “is characterized by a high-degree of intersubjective agreement”. In
dictionary-making this task is performed by lexicographers, who are skilled bilinguals(ibid.), and who use their linguistic intuition. While Adamska-Sałaciak does not refer to any
empirical studies on the psychological foundations of lexicographers’ work, she bases her
observations on her experience in practical lexicography. In fact, we do not know any
psycholinguistic work on lexicographer intuitions, and we also have our experience inpractical lexicography.

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Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki& Tadeusz Piotrowski
Towards equivalence links between senses in plWordNet and Princeton WordNet

To provide an example of the difference between cognitive and translational
equivalents, a translational equivalent of the English adjective heavy3 may be,depending on context, wzmożony or duży (e.g. heavy traffic) or mocny (e.g. heavy
make-up) or even pogrubiony (e.g. (to write in) heavy type), yet its cognitive
equivalent (ciężki), which first comes to mind of a naive user of both languages, isa very general one, and, while it may be successfully used in many contexts (e.g.
heavy bag, heavy suitcase, heavy fighting), it may not fit some specific contexts.
That is why Héja (2016: 7) argues that “cognitive equivalents and contextual[translational] equivalents should be considered as two ends of a scale”, that is,from perfect to occasional to rare interchangeability, and that their position on thescale should be determined using a mathematical formula called conditional
probability (ibid., 8). In other words, the position on the scale presents the potentialof the use of equivalents in translation in a given context. From yet anotherperspective, Héja (2016: 7) notices that the scale also reflects the degree ofsymmetry between equivalents, namely that [perfect] cognitive equivalents are
symmetric while translational equivalents tend to be asymmetric. Obviously, thedegree of symmetry would depend on adopted criteria.
Adamska-Sałaciak (2014: 7) remarks further that since the number of contexts
a SL item may appear in is unlimited, so is the number of its possible translations.
For reasons of size limitations, dictionaries typically list only the most frequentones (ibid.) For some, hints may be given with respect to their appropriateness inparticular contexts, which will make them more useful for language learners andtranslators.
Clearly, the whole typology presented so far is more of interest to dictionary
compilers or researchers rather than to dictionary users. The latter are not informed
about the typological status of different equivalents for a given word sense. Often
the differences between cognitive and translational equivalents are subtle and they
are hard to distinguish even for a professional lexicographer. This is not the goal of
constructing dictionaries. They aim to be of the greatest possible help for languageusers. On the other hand, bi- and multilingual wordnets have been and are
constructed more as machine-readable dictionaries for natural language processing
tasks (such as automatic translation, information retrieval etc.) than as resources
for translators or language learners. Still, due to their large data coverage andrichness in lexico-semantic information, they have been found a valuable resource
for language users too. For example, plWordNet is accessed online over 10,000
times a month and linked to Princeton WordNet of English is used in the popular
Polish multilingual online dictionary Ling.pl: <http://ling.pl>. The Japanese
wordnet, also with the Princeton WordNet is used as as a bilingual resource in a3This example is based on an entry in PWN-Oxford Polish-English Dictionary.

Lodz Papers in Pragmatics 13.1 (2017): 3–24
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varietyoflanguagelearningsites,<http://www.manythings.org/japanese/reading/wnjpn/>.

9

such

as

2.2 Equivalence relations in wordnets
The majority of bilingual wordnets employ simple, one-to-one equivalence links
between source and target synsets, with Princeton WordNet English synsets
usually functioning as target ones (cf. EuroWordNet, Vossen 2002; OpenMultilingual Wordnet, Bond and Foster 2013). The links are sometimes establishedmanually (e.g. GermaNet cf. Hamp and Feldweg 1997), plWordNet, cf. Rudnicka
et al 2012). In most cases the WN structure is copied and next semi-automaticallyfilled with translations of synset members, e.g. in the case of SlowNet (the wordnetof Slovene, cf. Fišer and Sagot (2015)) this is done using multilingual aligned
corpora. Occasionally WN synsets are only manually translated (e.g. FinnNet, thewordnet of Finnish, cf. Lindén and Carlson (2010). Such simple equivalence links
are called inter-lingual synonymy by wordnet developers (starting from Vossen
2002). Similarly to bilingual dictionaries, they include cognitive and translational
equivalents. However, since different wordnets are based on slightly different
theoretical assumptions and, consequently, their synsets may differ in granularity(the subtlety of meaning distinctions resulting in the number of lexical units persynset), the simple equivalence link between two synsets does not entail that all
lexical units from both synsets can function as each other’s perfect translational
equivalents.
(2){pies 2 - ‘dog’, pies domowy 1 - ‘domestic dog’} (zw) (semantic domain: ‘animal’)pies 2 register: general;
gloss: pies domowy - popularne zwierzę domowe, przyjaciel
człowieka - ‘ domestic dog - a popular pet, man’s friend’
usage example: Pies był bez kagańca.
‘The dog didn’t have a muzzle.’
pies domowy 1 register: specialised; no gloss; no usage example given
Synset relations:hyponym of {pies 1 - ‘canine’} (zw) and {czworonóg 1 - ‘quadruped’} (zw)hypernym of {kundel 1 - ‘mongrel’} (zw), {pies myśliwski 1- ‘hunting dog’} (zw)interlingual synonym of:{dog 1, domestic dog 1, Canis familiaris 1} (zw) (semantic domain: ‘animal’)gloss: a member of the genus Canis (probably descended from the common wolf)that has been domesticated by man since prehistoric times; occurs in many breeds;
usage example: “the dog barked all night”

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Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki& Tadeusz Piotrowski
Towards equivalence links between senses in plWordNet and Princeton WordNet

Looking at the members of synsets in example (2), a competent bilingual speaker(or a translator) will identify pairs of lexical units that are better and worsetranslational “matches”. The better ones are clearly pies 2 and dog 1, and pies
domowy 1 and domestic dog 1. Apart from the shared meaning, they also agree inregister: general in the former pair, specialised in the latter and in the internal
structure: simple word vs. multi-word expression. It has not been studied, as far as
we know it, what those beliefs are based on, it is quite likely that their source isfolk (naive) assumptions about languages, in European culture very strongly
influenced by writing.
Such many-to-many links do not exist explicitly in bilingual dictionaries,
though they can be recovered implicitly. What they usually offer implicitly is a
one-to-many link, or one-to-one link. The former one appears and it is also quite
frequent in wordnets when we deal with a single lexical unit source synset linked
to a multiple lexical unit target synset, or the other way round. Such a case isillustrated in example (3) below:(3){olej słonecznikowy 1 - ‘sunflower oil’} (jedz) (semantic domain: ‘food’),register: general;
gloss: olej roślinny, spożywczy wytwarzany z nasion rośliny oleistej - słonecznika‘vegetable oil, foodstuff, produced from the seeds of oil plant - sunflower’hyponym of {olej roślinny 1 - ‘vegetable oil’}interlingual synonym of:{sunflower oil 1, sunflower-seed oil 1} (semantic domain: ‘food’),
gloss: oil from sunflower seeds
hyponym of {vegetable oil 1}

Despite the fact that the English synset includes two lexical units: sunflower oil 1
and sunflower-seed oil 1, both seem to be very good translations of the Polishlexical unit olej słonecznikowy 1.
Intuitively, equivalence is perceived as a relation between one source language
item and one target language item. However, it does not need to be so, as
illustrated by example (3) above. It is also not such a frequent case in bilingual
dictionaries or in bilingual wordnets. In the former, it occurs when only one
equivalent is listed for a source language headword, in the latter when theequivalence link is established between two synsets including one lexical unit each.
Below we provide an example (4) of the latter with the pair of plWN and WN
synsets linked by an inter-wordnet relation of interlingual synonymy:(4){indyk 1} (jedz) ‘turkey’ (semantic domain: ‘food’),register: general; gloss: mięso z indyka- ‘the meat from turkey’,usage example: Kiedyś bardzo lubiłam wędliny z indyka.
`I used to like cold cuts made from turkey.’Lodz Papers in Pragmatics 13.1 (2017): 3–24
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hyponym of {drób 1 - ‘poultry’}
hypernym of {indyczka 1 - ‘hen turkey’}interlingual synonym of:{turkey 4} (jedz) (semantic domain: ‘food’),
gloss: flesh of large domesticated fowl usually roasted
hyponym of {poultry 1}
hypernym of {hen turkey 1}{drób 1} - I-syn {poultry 1}{indyczka 1} - I-syn {hen turkey 1}

It seems that the link between the plWN synset {indyk 1} and the WN synset{turkey 4} yields a pair of very good translational equivalents. A subtle sense
contrast can be tracked to the gloss of {turkey 4} which specifies that the meat is
usually roasted, while no such information is included in the gloss of {indyk 1}.
Yet it does not result in any substantial sense difference. The I-synonymy relation
is established on the basis of correspondence in meaning which is mainly
determined on the basis of correspondence in relation structure, (cf. Rudnicka et al.
2012). As demonstrated above, both hypernyms and hyponyms of the two synsets
are also each other’s I-synonyms. Also, PWN-Oxford Polish-English Dictionary
lists ‘turkey’ as an equivalent of indyk under a modifier culin. - ‘culinary’. It is the“first choice”, spontaneous equivalent that comes to mind of a bilingual speaker.
Thus, the (two) lexical units: {indyk 1} and {turkey 4} also appear to function as
cognitive equivalents.
In comparison to bi- and multilingual wordnet alignment among wordnets ofother languages, the mapping between plWordNet and Princeton WordNet is
unique with respect to a rich set of inter-wordnet interlingual relations applied.
Apart from the simple equivalence relation that is I-synonymy, a set of more
complex interlingual relations is employed (cf. Rudnicka et al. 2012). They are
partly modelled on Equivalence Relations proposed within EuroWordNet project(cf. Vossen 2002), but they were not really used in any of EuroWordNet wordnets.
The most frequent interlingual relation is I-hyponymy (with the reverse Ihypernymy relation). It is illustrated in example (5) for one Polish and two English
synsets:(5){palec 1} (czc) ‘finger or toe; digit’ (semantic domain: ‘body parts’)
register: general; gloss: ‘u ludzi lub zwierząt’ - ‘of people or animals’
hyponym of {element anatomiczny 1 - ‘anatomical element’
hypernym of {duży palec 1 - ’big toe’}, {mały palec 1 - ‘little finger or toe’},{palec wskazujący 1 - ‘index finger’}, {palec środkowy 1 - ‘middle
finger’}, {serdeczny palec 1 - ‘ring finger’}12

Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki& Tadeusz Piotrowski
Towards equivalence links between senses in plWordNet and Princeton WordNet

interlingual hypernym of:{finger 1} (czc), (semantic domain: ‘body parts’)
gloss: any of the terminal members of the hand (sometimes excepting the thumb);
usage example: "her fingers were long and thin";{toe 1} (czc), (semantic domain: ‘body parts’)
gloss: one of the digits of the foot;{hammertoe 1} (czc) (semantic domain: ‘body parts’)

Polish, unlike English, does not lexicalise the semantic distinction between ‘finger’
and ‘toe’. It has one word subsuming the meaning of the two English words 4.
When the context does not disambiguate its meaning, the Polish word can be postmodified with a prepositional phrase: palec u ręki - ‘digit of a hand’, or palec unogi - ‘digit of a foot’. Usually, it is not needed: But mi obtarł palca. - ‘The shoe
hurt my toe’. vs. Włożył dwa palce do rękawiczki. - ‘He put two fingers into theglove’. Therefore, {finger 1} and {toe 1} are linked to {palec 1} via I-hyponymy
relation. We ran a translation probability check in the Polish-English parallel
corpus Paralela5 (cf. Pęzik 2016) and we found the following results:(6)

palec-finger 1400
palec-toe 160
palec-hand 85
palec-thumb 40
palec-digit 0
palec-dactyl 0

Although an analysis of the sample of the results in (5) showed that quite a few ofmatches are false-positives (they appear in the parallel sentences, but the words are
not necessarily translations of each other), there is clearly a tendency for palec tobe translated as finger. This, in turn, is clearly related to the fact that, intuitively,
we talk more frequently about fingers than about toes.
Another interlingual relation used in plWN-WN synset mapping is I-partialsynonymy, devised to cater for cases of partial overlap of meanings and relation
structures. It is exemplified below:

45Interestingly, Japanese also has one word for finger and toe - yubi 指.
http://paralela.clarin-pl.eu/

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(7)

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{mebel 1 - ‘a piece of furniture’} (wytw) (semantic domain: ‘artefact’)
register: general;
gloss: sprzęt użytkowy przeznaczony do wyposażenia wnętrz mieszkalnych ipublicznych, posiada też walory dekoracyjne, reprezentacyjne; w odróżnieniu odstałych elementów wyposażenia wnętrz (schody, boazeria) meble są
ruchomościami‘an instrumentality dedicated to furnishing private and public interiors, it also has
decorative and representative value; in contrast to immovable elements offurnishing(stairs, wainscot) furniture is movable’
usage example: Kolejną kwestią sporną, dotyczącą mieszkanka, jest rozstaw mebli
w kuchni.
‘Another arguable issue concerning the little flat is the setup of
furniture in the kitchen.’hyponym of {element wyposażenia 1 - ‘an article of furnishing’}
hypernym of {biblioteczka 1 - ‘bookcase’},{S mebel sypialniany 1 ‘a piece of bedroom furniture} (artificial
synset) (and a few more)meronym of {umeblowanie 2 - ‘furniture’}interlingual partial-synonym of:{furniture 1, piece of furniture 1, article of furniture 1} (semantic domain:‘artefact’)
gloss: furnishings that make a room or other area ready for occupancy;
usage example: "they had too much furniture for the small apartment";"there was only one piece of furniture in the room"
hyponym of {furnishing 1}
hypernym of {bookcase 1}, {bedroom furniture 1}, (and a few more)interlingual hypernym of {umeblowanie 2 - ‘furniture’}

The Polish synset {mebel 1 - ‘a piece of furniture’} includes only one lexical unit
in singular number and it is within singular tree (see its hypernym and hyponyms).
Polish also has a mass term with the equivalent meaning placed in a separate synset- {umeblowanie 2 - ‘furniture’}, linked by a holonym relation to {mebel 1}. The
most semantically close equivalent English synset subsumes both a mass term –furniture 1, together with two singular composite terms – piece of furniture 1 andarticle of furniture 1. It is located under a singular hypernym - {furnishing 1}, andhas both singular and mass hyponyms - {bookcase 1} and {bedroom furniture 1}.
After the analysis of the relation structures, glosses and usage examples of {mebel1}, {umeblowanie 2} and {furniture 1, piece of furniture 1, article of furniture 1}, alexicographer decided to link {mebel 1} via I-partial synonymy and {umeblowanie
2} via I-hyponymy to {furniture 1, piece of furniture 1, article of furniture 1}.

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Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki& Tadeusz Piotrowski
Towards equivalence links between senses in plWordNet and Princeton WordNet

Clearly, stronger direct equivalence links could be established between the lexicalunits mebel 1 and piece of furniture 1, and between umeblowanie 2 and furniture 1.

3 Defining equivalence for sense mapping
So far we have seen that the current synset-level mapping between plWordNet andPrinceton WordNet offers a lot of information with respect to potential
translational equivalents between Polish and English, and yet, despite a rich system
of inter-wordnet interlingual relations, it does not unequivocally signal the strengthof the link of particular members of the mapped pair of synsets (see Example (2)).
There are two main reasons behind such state of affairs. First, synsets differ in
granularity: they may contain one or more lexical units which entails three optionsof matching: 1-to-1, 1-many, and many-to-many. Second, inter-wordnet
interlingual relations are established largely on the basis of correspondence ofrelation structures between the two synsets. Hence, we deal with systemcorrespondence rather than usage correspondence here (cf. Rudnicka et al 2017).
This opens up the way to the possibility of occurrence of both cognitive andtranslational equivalents not only within I-synonymy, but also within I-hyponymy,
I-hypernymy and I-partial synonymy (see Example (4)).
Therefore, in this paper we propose a strategy for signalling the strength ofsemantic link between lexical units (nouns) of plWordNet and Princeton WordNet.
The starting point for creating an (additional), more fine-grained network of lexical
unit links will be the existing network of interlingual relations between Polish andEnglish synsets. These will be re-analysed from the perspective of cognitive andtranslational equivalence (described in Section 2). The candidate lexical units witha potential for stronger links will be identified and verified against a set of featuresthat will be specified below.
In the first step, we will extract the following list of bilingual pairs from themapped plWordNet:(8)

i. single lexical unit synsets linked by I-synonymy (1-to-1 match)ii. single and multiple lexical unit synsets linked by I-synonymy (1-to-many
match)
iii. multiple lexical unit synsets linked by I-synonymy (many-to-many match)

Later, we will also extract similar lists for other interlingual relations such as Ihyponymy, I-hypernymy and I-partial synonymy. The lists will be then given tobilingual lexicographers. They will be asked to verify the strength and specificity
of bilingual links between all member lexical units of the extracted pairs of synsets
on the basis of detailed guidelines and they will introduce special new links whereapplicable.

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The two types of strong equivalence described in Section 2, that is cognitive
and translational, emphasise its two different aspects, namely meaning and use,
respectively (cf. Piotrowski 2011; Adamska-Sałaciak 2014). For the purposes of(direct) lexical unit mapping, we would like to define two types of strong
equivalence cross-cutting through these two aspects. The first type, super-strong
equivalence would entail the agreement (identity and/or compatibility) in thepossibly high number of features. The second type, strong equivalence would
mean the agreement in the reasonably high member of features. For both, acommon set of primary, always necessary to agree in, features will be specified.
Below we present a proposal of super-strong equivalence set of features:(9)Super-strong equivalence:i. identity in grammatical category (given from the synset mapping)ii. identity in number
iii. identity in sense (synset (and lexical unit) relation structure and gloss)
iv. identity in registerv. identity in countability
vi. compatibility in (semantic) gender (if relevant/applicable)
vii. ‘first choice’ equivalent: listed first in bilingual dictionaries
viii. bidirectional
ix. high translation probability if it appears in a parallel corpus
x. unique for a single lexical unit

The set of super-strong equivalence features proposed in (8) includes: formal(morpho-syntactic) features such as grammatical category, number andcountability; semantic features such as sense, register and (semantic) gender; andusage features such as dictionary listing, directionality of translation (substitution)and translation probability. The last feature is more of a requirement for
lexicographers who will do the linking. It stipulates that there can be only one such
relation per one lexical unit. Formal features are generally easy to determine.
Besides, lexical unit mapping will draw on the results of synset mapping andinterlingual synset relations obtain between synsets of the same grammaticalcategory (except for cross-categorial synonymy, which will not be taken into
account here). As for number and countability, plWordNet always puts singularand mass terms into separate synsets, but this does not always hold for Princeton
WordNet (see Example (7) for a ‘mixed’ singular and mass synset).
Semantic features bring about more challenge. As for gender, both wordnets
place feminine terms into separate synsets. Moreover, feminine gender is marked
in plWordNet by the lexical unit relation Żeńskość - ‘feminine gender’. Feminine
terms are usually derived from masculine terms in Polish and it is a very
productive process. If not derived, they are still located under a feminine gender
hypernym. Apart from exceptional cases (e.g., fiancé/fiancée, waiter/waitress),

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English does not mark morphological gender. To some extent, gender is lexicalisedin English (e.g., actor/actress). Again, this a fairly productive derivation process.
Feminine terms can be hyponyms of neutral/masculine terms in Princeton
WordNet. Still, the information about feminine gender can be always found in thesynset gloss.
On the other hand, the information about stylistic register is often absent from
Princeton WordNet. Sometimes, stylistically marked lexical units appear in thesame synsets with neutral, unmarked terms. This usually does not happen in plWN.
Stylistically marked terms are put into separate synsets which are linked to theirunmarked synonyms by the relation of Bliskoznaczność - ‘Inter-registersynonymy’. Moreover, the constructors of plWordNet have recently started toenrich lexical unit information with register (cf. the system of registers in
plWordNet). Undoubtedly, the semantic description offered by wordnets is still tosome extent limited and there are contrasts and gaps in lexical description between
plWN and WN (also see below), so lexicographers will need to consult otherlexical resources too.
Clearly, the key and most challenging feature to determine is identity in sense.
The key sense denominator in a wordnet is a network of lexico-semantic relations.
Both Princeton WordNet and plWordNet have a rich network of synset relations.
plWordNet has also developed an equally rich network of lexical unit relations
which are present, but much less numerous in the original WordNet. They add
more specification to the semantic information of individual lexical units.
Moreover, plWordNet provides glosses on the lexical unit level, although not all
lexical units have been attributed with them by now. Originally, plWordNet did notinclude glosses at all. On the other hand, Princeton WordNet has glosses for all
synsets. For some, there are also usage examples provided, similarly for
plWordNet. Again, there has recently been a shift in plWordNet development
process to move examples to lexical unit level. An important factor in determining
the correspondence in relation structures will be the existing network of interwordnet interlingual synset relations between plWordNet and Princeton WordNet.
It has to be taken into account in determining the meaning and degree of
equivalence between a given pair of Polish and English lexical units, but, ingeneral, lexical unit mapping needs to go beyond the existing synset mapping andshow more detailed (subtle) sense and usage correspondences.
The last sub-set of features involves the potential of the use of equivalents intranslation. It takes from the cognitive equivalent the “first choice” requirement.
Since leaving it (purely) to the intuition of individual lexicographers would resultin inconsistent results, we decided to treat bilingual dictionary listing as a kind ofbenchmark. It should be (preferably) listed first in bilingual dictionaries (for the
specific sense of a headword). Moreover, a Polish-English pair of lexical units
linked by super-strong equivalence should be bidirectional, that is, it should have

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the same equivalent power in both translation directions (Polish-English/EnglishPolish). Again, this can be (at least partly) verified in bilingual dictionaries.
In addition, we decided to measure translation probability in a Polish-English
parallel corpus: Paralela (Pęzik 2016) taking into consideration its limitations. The
first one is the current size of the corpus, which includes 262 million words in
10,877,000 translation segments; as for the structure and composition of the
corpus, legal texts (European Union legislation, proceedings of European
Parliament etc.), press releases, medical texts (made available online by theEuropean Medicine Agency) as well as film subtitles predominate therein (Pęzik
2016: 68). The second one is the level of alignment it offers, which is currently at
the level of sentences (Pęzik 2016: 70), yet the search engine supports query-based
word alignment and the equivalents of the source-language words (queries) are
identified and ranked using the Dice coefficient (Pęzik 2016: 73). However, only asmall part of the corpus was manually aligned (popular science texts fromAcademia and Center of Eastern Studies (OSW) as well as 114 literary novels),
which means that there is still an area for improvement as regards an overall
quality of alignment. In addition, there seem to be a few files where the text
labelled Polish is in fact English or Russian, and a few places where the same text
appears multiple times, so we will do some preprocessing to remove these,
rejecting all sentence pairs not identified as (Polish, English) by langid.py (Lui and
Baldwin 2011) and then removing any duplicate pairs.
For our purposes, we would ideally need a sense-level alignment (which would
require prior bilingual word-sense disambiguation of corpus data). We willcalculate ngram-based translation probabilities: the probability that contiguous
chunk of text in one language is translated by a chunk in the other, using theanymalign tool (Lardilleux and Lepage, 2009), a lightweight aligner whichcalculates the probabilities based on random sampling. anymalign gives absolute
frequency of the ngram pair as well as the probability of translation from source totarget (e.g. what is the probability that an instance of palec being translated as
finger) and target to source (finger to palec). A good pair should have hightranslation probabilities in both directions. Since we are interested in the lexical
unit rather than the surface form, we will calculate the probabilities both forsurface form of the words and the lemmatized version (where fingers is replacedby finger).
Note that pairs of equivalents extracted from parallel corpora are translational
ones based on words: they tend to be context-specific and not necessarily identicalin meaning. For our super-strong equivalence links, we really want items withidentity in sense (as described above). Still, all this does not mean that data
extracted from parallel corpora are of no use for us. It certainly showstranslation/equivalent tendencies and professional lexicographers are usually able
to disambiguate the meaning of a searched word on the basis of its context.

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Towards equivalence links between senses in plWordNet and Princeton WordNet

However, it is important to bear in mind that given the limitations of parallel
corpora (in terms of structure, composition, representativeness, balance etc.), any
tendencies in translation, revealed by, for example, translation probabilities, should
be interpreted only with respect to texts found in a given parallel corpus rather thanbeing extrapolated to the originals and their translations in their totality. In
particular, a low translation probability does not mean that a pair is not a goodequivalent, it may just be the case that this usage never appeared in the corpus.
Now, it may seem hard to find any pairs of Polish-English lexical units
fulfilling all of the above described features. Our suggestions for super-strong
equivalence instances go as follows: indyk 1 and turkey 4, indyczka 1 and hen
turkey 1, olej słonecznikowy 1 and sunflower oil, pies 2 and dog 1, pies domowy 1
and domestic dog 1, mebel 1 and piece of furniture 1. A detailed description ofthese examples was presented in Section 2, examples (2), (3), (4) and (7).
The super-strong equivalence as defined above rests on the assumption that
there is some commonality between lexical structures of the two languages. It
assumes that some concepts are lexicalised to a very similar degree. However, it isgenerally accepted that no universal lexical structure exists (cf. von Fintel andMatthewson 2008) and there are a number of lexical gaps and mismatches between
languages (cf. Svensen 2009). This was confirmed in the process of synset
mapping between plWordNet and Princeton WordNet (cf. Rudnicka et al. 2016).
The most frequent interlingual relation is I-hyponymy. Still, despite thesedifferences, translations are made, dictionaries are constructed and speakers ofdifferent languages do communicate. To capture meaning and usage
correspondences that are still very strong, yet not complete in all aspects, we would
like to propose the strong equivalence relation. It will share the features withsuper-strong equivalence, yet the requirements for some of them will be slightly
relaxed in comparison with the former, see below:(10)Strong equivalence features:
i. identity in grammatical category (given from the synset mapping)ii. identity in number
iii. largely compatible in sense (synset (and lexical unit) relation structure andgloss)
iv. identity in registerv. identity in countability
vi. compatibility in (semantic) gender (if relevant/applicable)
vii. often ‘first choice’ equivalent: often listed first in bilingual dictionaries
viii. can be either uni or bidirectional
ix. preferably high translation probability in a parallel corpus
x. need not be unique for a single lexical unit

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The strong equivalence specification differs from the super-strong equivalence in a
number of aspects. The values for formal features are alike. In terms of semantics,
it allows for large compatibility in sense instead of identity. In terms of usage,
unidirectionality is accepted, along with bidirectionality, and the ‘first choice’ is apreference, not an obligation. Consequently, uniqueness of a link also need not tobe observed. Strong equivalence examples are as follows: palec 1 and finger 1,
palec 1 and toe 1, olej słonecznikowy 1 and sunflower-seed oil 1, mebel 1 andarticle of furniture 1.
Hence, the proposed lexical unit mapping will be partly based/modelled on theexisting system of interlingual relations between synsets. It is aimed to enrich thecurrent system of bilingual links, and not to repeat the same information.
Therefore, we will introduce new, more detailed links only in the cases where there
is enough motivation for such a decision. The remaining, not directly linked lexicalunits from synsets otherwise linked by I-synonymy, I-partial synonymy, Ihyponymy and I-hypernymy, will be argued to have implied weak translational
equivalence. That basically means that they are potential translational equivalents
depending on context.

4 Conclusions
In this paper, we have proposed a strategy of linking plWordNet and Princeton
WordNet at the level of its smallest building parts - lexical units. The proposedsense-level matching may be viewed as a kind of extension of the existing interlingual mapping between synsets. Capitalizing on equivalence types from(traditional) lexicographic literature, we have defined two types of links between
lexical units: super-strong equivalence and strong equivalence. They are aimed tocross-cut through the import of the so-called cognitive and translational
equivalents. They share a set of formal, semantic and usage features. The
requirements for strong equivalence are only partly loosened in comparison tosuper-strong one. The sense-level mapping will draw on the results of synset-levelmapping to the extent that lists of pairs of synsets linked by I-synonymy, I-partialsynonymy, I-hyponymy and I-hypernymy will be extracted and further grouped
into single lexical unit pairs (1-1), multiple lexical unit pairs (many-many) andsingle to multiple lexical unit pair (1-many). The lists and sets of equivalencefeatures will be next given to trained bilingual lexicographers who will verify thestrength of correspondence between bilingual pairs of lexical units (of each synsetpair) and will introduce new super-strong and strong equivalence links whereapplicable. They will be encouraged to consult various lexical resources as well as
check translation probabilities in parallel corpora. The remaining lexical units ofthe earlier mapped synset pair are argued to have the implied weak translational

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Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki& Tadeusz Piotrowski
Towards equivalence links between senses in plWordNet and Princeton WordNet

link which signals a potential for becoming a translational equivalent in an
appropriate context.
The proposed strategy paves the way for creating a more fine-grained, detailed
network of interlingual links of a great potential of application in both natural
language processing tasks (e.g. automatic translation, bilingual word-sense
disambiguation, sense-level alignment of parallel corpora), manual translation andlanguage learning, among others.

Acknowledgements
The paper is the result of works carried out within the project funded by the
National Science Centre (Narodowe Centrum Nauki), Poland, under the grant
agreement no: UMO-2015/18/M/HS2/00100.

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About the Authors
Ewa Rudnicka is a Research Associate at the Department of Computer
Science and Management, Wroclaw University of Technology, Poland. Her
research interests include computational bilingual lexicography,
comparative linguistics, formal semantics, translation studies. She is thecoordinator of the process of mapping plWordNet onto Princeton WordNet.
She is a member of G4.19. Language Technology and Computational
Linguistic Research Group.
Francis Bond is an Associate Professor at the Division of Linguistics andMultilingual Studies, Nanyang Technological University, Singapore. He
worked on machine translation and natural language understanding inJapan, first at Nippon Telegraph and Telephone Corporation and then at the
National Institute of Information and Communications Technology, where
his focus was on open source natural language processing. He is an active
member of the Deep Linguistic Processing with HPSG Initiative (DELPHIN) and the Global WordNet Association. His main research interest is innatural language understanding. Francis has developed and released
wordnets for Chinese, Japanese, Malay and Indonesian and coordinates theOpen Multilingual Wordnet.
Łukasz Grabowski is an Associate Professor at the Institute of English,
University of Opole, Poland. His research interests include corpus
linguistics, phraseology, formulaic language, translation studies andlexicography. He is also interested in computer-assisted methods of text
analysis. He has published internationally in International Journal of
Corpus Linguistics and English for Specific Purposes, among others. He is
also Managing Editor of the journal Explorations: A Journal of Language
and Literature.
Maciej Piasecki is an Associate Professor at the Department of Computer
Science and Management, Wroclaw University of Technology, Poland. He
is a Polish National Coordinator of CLARIN ERIC (www.clarin.eu) and amember of Global WordNet Association Board. He has been an initiatorand is the leader of plWordNet project (a large wordnet of Polish) and is theleader of G 4.19. Language Technology and Computational Linguistic
Research Group. His research interests cover different areas of natural
language processing and engineering, computational lexicography, data
extraction and information retrieval.

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Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki& Tadeusz Piotrowski
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Tadeusz Piotrowski is a Professor at the English Department, University of
Wrocław, Poland. His research interests include theory, practice, andhistory of monolingual and bilingual lexicography and dictionaries, corpus
linguistics, translation studies, participated in most major bilingual
dictionary projects in Poland, working with such companies as PWN, OUP,
Pons-Klett, Langenscheidt, Prószyński, Wiedza Powszechna, Kościuszko
Foundation, and wrote a number of dictionaries for Spotkania. He is alsointerested in computational lexicography and computer-assisted text
analysis. He published three books and about 200 papers.
Address
Ewa Rudnicka
Department of Computer Science and Management
Wrocław University of Technology
Wybrzeże Wyspiańskiego 27
50-370 Wrocław, Polande-mail: ewa.rudnicka@pwr.edu.pl