=Paper=
{{Paper
|id=Vol-1899/CfWNs_2017_proc4-paper_3
|storemode=property
|title=Challenges Behind the Data-driven Bulgarian WordNet (BulTreeBank Bulgarian Wordnet)
|pdfUrl=https://ceur-ws.org/Vol-1899/CfWNs_2017_proc4-paper_3.pdf
|volume=Vol-1899
|authors=Petya Osenova,Kiril Simov
|dblpUrl=https://dblp.org/rec/conf/ldk/OsenovaS17
}}
==Challenges Behind the Data-driven Bulgarian WordNet (BulTreeBank Bulgarian Wordnet) ==
https://ceur-ws.org/Vol-1899/CfWNs_2017_proc4-paper_3.pdf
Challenges behind the data-driven Bulgarian
WordNet (BulTreeBank Bulgarian WordNet)
Petya Osenova and Kiril Simov
Institute of Information and Communication Technologies, BAS,
Akad. G. Bonchev. 25A, 1113 Sofia, Bulgaria
{petya,kivs}@bultreebank.org
Abstract. The paper presents our work towards the simultaneous cre-
ation of a data-driven WordNet for Bulgarian and a manually annotated
treebank with semantic information. Such an approach requires synchro-
nization of the word senses in both - syntactic and lexical resources,
without limiting the WordNet senses to the corpus or vice versa. Our
strategy focuses on the identification of senses used in BulTreeBank, but
the missing senses of a lemma also have been covered through exploration
of bigger corpora. The identified senses have been organized in synsets
for the Bulgarian WordNet. Then they have been aligned to the Prince-
ton WordNet synsets. Various types of mappings are considered between
both resources in a cross-lingual aspect and with respect to ensuring
maximum connectivity and potential for incorporating the language spe-
cific concepts. The mapping between the two WordNets (English and
Bulgarian) is a basis for applications such as machine translation and
multilingual information retrieval.
1 Introduction
There have been two prominent trends in language resources creation — compil-
ing syntactically annotated resources (treebanks), on the one hand, and build-
ing lexical resources (WordNets), on the other. The former resources reflect the
syntagmatic connectedness of the words, while the latter encode primarily the
paradigmatic relations among words (via hierarchies). There are also works fo-
cused on the semantic annotation of corpora/treebanks, which apply the lexical
knowledge onto real texts. Here we report on the challenges behind the construc-
tion of the BulTreeBank Bulgarian WordNet (BTBWN). BTBWN has been cre-
ated in three different ways: (1) by manual translation of English synsets from
Core WordNet subset of Princeton WordNet (PWN — [2])1 into Bulgarian. This
step ensures comparable coverage between the two WordNets on the most fre-
quent senses; (2) by identification of senses used in BTB. The identified senses
have been organized in synsets for the BulTreeBank Bulgarian WordNet. The
newly created Bulgarian synsets are mapped onto the conceptual structure of
1
The Core WordNet contains the 5000 most frequent synsets of PWN. http:
//wordnetcode.princeton.edu/standoff-files/core-wordnet.txt
PWN. In this way, the BTBWN was extended with real usages of the words in
texts. Also, the coverage of the core and base concepts for Princeton WordNet
has been evaluated over a Bulgarian syntactic corpus; (3) by sense extension,
which includes two activities: a) detection of the missing senses of processed lem-
mas in BulTreeBank and adding them to the BTBWN, and b) a semi-automatic
extraction of information from the Bulgarian Wiktionary mapped to synsets
from PWN and then manually checked2 In this paper we present the second
step of creating the BTBWN — simultaneous annotation of BTB with senses,
the extension of BTBWN with these new synsets and their mapping to PWN.
The structure of the papers is as follows. Section 2 briefly discusses related
work. The construction of BTBWN is presented in Section 3. Section 4 introduces
the general principles of mapping. Section 5 presents two extensions of BTBWN
in progress and future direction of developments. The last section concludes the
paper.
2 Related Work
Concerning WordNets, many of them for the European languages have been cre-
ated within EuroWordNet and BalkaNet projects (including BulNet for Bulgar-
ian). However, also some of these WordNets are not publicly available (including
BulNet). This motivated us to start our own WordNet creation endeavor, since
we needed the lexico-semantic information in our work on Machine Translation,
eLearning and Word Sense Disambiguation.
There are two main methods for building a WordNet as pointed out in [8]: the
expand method and the merge method. The former relies on the translation of the
synsets from the source into the target language, thus complying initially to the
source hierarchy of concepts. The latter takes (also) into account the language
specific resources. Different WordNet projects used the above mentioned methods
alone or in combination with other strategies. For example, translation of English
PWN into another language; data-driven approaches via identification of synsets
within real texts; automatic extraction from existing lexical resources; various
combinations of these. In our WordNet project we exploit all of these approaches
at different stages of the resource development. They will be explained in more
detail in the next sections.
Let us introduce briefly some best practices in the WordNet creation for spe-
cific languages. Most of them seem to go for the expand approach first. Some
WordNets were created on the basis of publicly available resources. For exam-
ple, the Open Dutch WordNet3 (see [7]) was created by “removing the propri-
etary content from Cornetto4 , and by using open source resources to replace this
proprietary content.” For the Basque language [5] the construction approach re-
lies on the joint development of WordNets and annotated corpora. The Basque
2
We would like to thank Antoni Oliver Gonzalez who provided the automatic mapping
from Bulgarian Wiktionary to PWN.
3
http://wordpress.let.vupr.nl/odwn/
4
http://www2.let.vu.nl/oz/cltl/cornetto
WordNet was developed within the EuroWordNet framework. First, a quick core
Basque WordNet was developed through semi-automatic methods. The quality
control included a concept-to-concept manual review. Afterwards, an additional
word-to-word review was performed as a higher-level quality check. The Slove-
nian WordNet started as automatic translation from a closely-related language
resource, namely — the Serbian WordNet with the help of a bilingual dictionary.
Later on, manual correction has been performed [1]. The Croatian WordNet [8]
also used the expand method, but it additionally explored monolingual dictionar-
ies for incorporating language-specific relations into the resource. One of the few
endevours for constructing a language-specific WordNet first, and then mapping
it to some already existing one, such as the Princeton WordNet, is the Polish
WordNet [9].
To sum up, there is no easy way to achieve typological consistency in building
WordNets - if the expand method is chosen, the language resource suffers from
lack of nativeness of the hierarchy and relations. If the merge method is followed,
the language resource differs too much from other similar resources and it is
time-consuming to map it back to them.
Now let us turn to the accompanying sense corpora. The usual way of anno-
tating senses in treebanks is the following: there is a WordNet for the language
in question, and then the treebank is annotated with senses from it. This is the
case in the German Tuba/DZ treebank, the Italian treebank and the Polish tree-
bank [3]. All of them use the WordNets they created in EuroWordNet Project
for sense annotation of the treebanks. Thus, they bear also the restrictions that
are presented in the so-called static lexical resources. This means the following:
if we want to annotate our texts with senses, but some sense is missing in the
lexical database, and we cannot control the WordNet resource to add it, then
the sparseness of the sense coverage would be really problematic.
Our work differs from the above mentioned approaches in the fact that we
first annotated the treebank with senses from an explanatory dictionary of Bul-
garian [6] and then started the formation of synsets. They were then mapped
to the PWN while keeping track of the various sense discrepancies by differ-
ent mappings. We explain our motivation for such a decision below in a more
contextually-bound manner. Here it can be only mentioned that in this way a
wider sense coverage was achieved quickly for the purposes of Machine Transla-
tion, since our initial WordNet covered only the core concepts from PWN.
3 Construction of BTBWN
In this section we present the steps of construction of BTBWN also from a
historical point of view. The creation of this resource started as an attempt to
construct domain vocabularies for two domain ontologies: the domain of Infor-
mation Technology for End Users, and the domain of Home Textile — see [11]
and [10]. In both cases the domain ontologies were aligned to an upper ontology
for the reasons of consistency and inheritance of general knowledge. The ontology
and the aligned lexicons were used for several tasks: (1) semantic annotation of
domain documents; (2) multilingual search; (3) common conceptualization; and
(4) interaction with the end users. Thus, the lexicon interrelated the concepts in
the ontology to the lexical knowledge used by the grammar in order to recognize
the realizations of the concepts in the text; and the lexicon represented the main
interface between the user and the ontology. In order to achieve these goals the
need of general lexica became apparent. Thus our next goal was to extend the
domain lexicons to cover (at least) the most frequent senses in Bulgarian. We
could not find any evaluation on the distribution of word senses in Bulgarian.
Thus we decided to solve this problem in two steps: (1) by transferring the most
frequent senses from another language to Bulgarian, assuming that European
languages share substantial number of most frequent senses; and (2) by annota-
tion of Bulgarian texts where we believed that the most frequent senses would
be present. For the purposes of applications, such as word sense disambiguation,
annotated texts were needed. So we decided to annotate the senses for all open
class words in the texts.
Concerning the first step — transfer of most frequent senses from another
language — we translated manually the English synsets from the Core WordNet
subset of the Princeton WordNet into Bulgarian. The translation was done by
two people with excellent knowledge of English. First, they formulated a Bulgar-
ian definition reflecting the content of the concept represented by its correspon-
dence to the English synset. Then they formed the Bulgarian synset recording
the Bulgarian lemmas that have this meaning. Some of the lemmas might be
multiword expressions. After this first phase a lexicographer checked both - the
definition and the lemmas. The result from this work was published as part of
the Open Multilingual WordNet5 under CC BY 3.0 license6 .
Our next step for extending the BTBWN was the manual annotation of
running Bulgarian texts. Here our goals were: (1) to extend the coverage of
BTBWN to really frequent Bulgarian words; (2) to have a corpus of semantically
annotated texts which to be used for experiments with tasks like Word Sense
Disambiguation; and (3) to check how many of the English most frequent senses
are frequent also in Bulgarian. The actual annotation of the treebank was done
in the following way: (1) for each lemma of the open class word forms in the
treebank a concordance was created; (2) each lemma in the concordance was
annotated with all possible senses from the Core WordNet version of BTBWN as
well as from an explanatory dictionary of Bulgarian; (3) the annotators selected
the appropriate sense for each example, if available. If there was no appropriate
sense, or there was no available senses for a given lemma, the annotator had the
possibility to create a new sense (definition). After the completion of this initial
annotation the result was turned into lexical entries which contain the lemmas,
selected in the text, the chosen definitions and the examples.
The following step was to manually map each new lexical entry to an ap-
propriate synset in PWN. Thus we achieved several goals: (1) different lemmas
with similar senses were grouped together and in this way the lexical entries
5
http://compling.hss.ntu.edu.sg/omw/
6
https://creativecommons.org/licenses/by/3.0/
for synonyms were recorded in the corresponding synsets; (2) the mapping to
PWN allowed the execution of various bilingual applications; (3) mapping to
WordNets of other languages. The annotation was checked by a second person
and validated by a judicator. After the completion of the annotation, BTBWN
contained about 11000 synsets. From them about 1800 synsets are from the Core
WordNet version of BTBWN. In this way we empirically showed that the most
frequent senses in the texts of BulTreeBank correspond roughly to one third of
the English Core WordNet.
The next extension of BTBWN was performed by a semi-automatic addition
from Bulgarian Wiktionary mapped to synsets from PWN and then manually
checked. Behind this extension we added new senses for the words that have
been already included in synsets of BTBWN. The idea is that each word is
represented with all its senses.
The extensions on the basis of text annotation and the existing lexicons ex-
hibit however the sparseness problem: not all synonyms appear in the annotated
texts and the lexical entries. For that reason, we performed checks on the com-
pleteness of the synsets with respect to the missing synonyms. The checks have
been performed with respect to the available monolingual synonymic dictionar-
ies of Bulgarian. Special attention was paid to the aspect variation of verbs. In
many of the synsets it turned out that for one of the verbs in the aspect pair
there were only few real examples or no examples at all in the data. Thus, we
started searching for examples from bigger corpora or on the web. Our goal is
to have at least five examples for each synset. Ideally, examples are expected to
be included for each lemma in the synsets.
4 Types of Mapping
The synchronization of word senses in BTBWN and the word senses in PWN is
complicated by the fact that many senses in BTBWN originate from BulTree-
Bank, where the words reflect Bulgarian lexicalization of concepts which differ
from PWN, which provides the English-specific view on the lexical relations.
From the annotator perspective, the mapping of the word from the text starts
with its translation into English and is followed by a search through the corre-
sponding lemmas in the PWN. Factors of importance for the adequate mapping
are the following: the Bulgarian definition and the matching examples from the
treebank. The provision of examples plays a crucial rule for the specification of
the correct definition as well as the English description and accompanying ex-
amples. The PWN examples themselves can also help in indicating the matching
concept, since the Bulgarian definition and the English one can be phrased in
different ways and might reflect various granularity of conceptualizations.
Several types of correspondences have been attested during the mapping pro-
cess: full correspondence (one-to-one); partial correspondence (one-to-many or
many-to-one); forced connectivity (re-design of Bulgarian definition); common
general meaning; resolving metonymies; incorrect and extended correspondences.
Needless to say, these are not novel at all. However, they are still valuable, be-
cause they provide feedback for the typologically-based and the resource-oriented
similarities and differences between Bulgarian and English, thus opening the path
to comparisons with other languages as well.
4.1 Full Correspondence
The ideal case in the mapping is when equal concepts are encountered, i.e. the
concepts in the two languages map one-to-one. That is, the Bulgarian con-
cept matches the one in the Princeton WordNet. For example, the Bulgarian
“сигурност”, “sigurnost” and English “safety” both mean in short ‘lack of dan-
ger’. If a Bulgarian definition corresponds equally well to more than one definition
in the Princeton WordNet, then all these definitions are mapped to the Bulgarian
one, using a special separator. For example, English “answer” and “response”
map to Bulgarian “отговор”, “otgovor”.
4.2 Partial Correspondence
In many cases, however, the concepts differ in terms of specificity in both lan-
guage directions. In the first case, the Bulgarian definition is more specific than
the English one. In this case, it is mapped to a more general English one, but
it is also marked with a specificity label. The most frequent cases here are the
following ones: (i) regular polysemy — for example, in Bulgarian “прокуратура”,
“prokuratura”, is given also as the building, while in English it is the institu-
tion, the group of people and the act; (ii) restrictive in Bulgarian vs. general
in English definitions — for example, “дирекция”, “direkcia”, in the meaning of
“director’s office” in Bulgarian is mapped to the more general concept “office”
in English with the meaning of “place of business”.
A second scenario is possible, where the Bulgarian definition is more general
and subsumes one or more synsets from PWN. In this case, the following ap-
proach has been taken — the common definition in Bulgarian is mapped once
to the more specific English definitions (with relation specificity) and a second
time to their hypernyms (with relation subsumption). For instance, in Bulgarian
“режисьор”, “rezhisyor”, “director” has only one definition: The lead person in
the making of a theater play, film, TV program, etc. However, in PWN there
exist two synsets that can be related to it: director as someone who supervises
the actors and directs the action in the production of a show (with a hypernym
“supervisor” as one who supervises or has charge and direction of) and director
as the person who directs the making of a film (with a hypernym “film maker”
as a producer of motion pictures). In order to preserve both — the more ab-
stract concept in Bulgarian as well as the hierarchical structure of PWN — the
Bulgarian definition is mapped to all four ones of these synsets — with relation
specificity to the specific ones, and with relation subsumption to their hypernyms.
These mappings are presented in Fig. 1. Some more explanations are presented
below.
Ensuring a One-to-One Mapping. In some cases of mismatch the one-
to-one mapping can be achieved through re-working the Bulgarian definitions.
Fig. 1. Classification of a Bulgarian definition with respect to English synsets in Prince-
ton WordNet hierarchy. We use the relation subsumption to map Bulgarian concept
(definition) to more general synsets in Princeton WordNet, and the relation specificity
to map it to more specific English synsets.
This often means dividing the Bulgarian definition into two separate ones. For
example, the word “седмица”, “sedmica”, “week”, has the following definition:
seven consecutive days, usually counted from Monday to Sunday. All examples
correspond to this definition. There are two synsets in English: “week” as any
period of seven consecutive days, and “week” as a period of seven consecutive
days starting on Sunday. Such a division in nouns referring to the passing of
time has been done in Bulgarian for the concept of “month”. Thus it can be
implemented for the “week” as well. Since the Bulgarian definition has been
mapped to the second synset in English, it can remain as it is, while a second
definition is introduced (Seven consecutive days), which is mapped to the first
synset; the examples are correspondingly divided between the two definitions.
Searching for a More General Meaning. There is another group of
examples to which no equivalent sense can be detected. In this case the strategy
is to find a more general one. Usually this applies to the cases of regular polysemy:
1. Types of institutions, buildings, people. Often there is no node in PWN
corresponding to a given institution. Thus a mapping is made to the general
node for an institution, company, establishment, etc.
2. Words like “‘цар”, “car”, “king”, etc. in Bulgarian refer to both concepts —
a person and a title. In PWN there is a definition only for a person, therefore
there is no word sense corresponding to the title meaning, as in “The title of
the Bulgarian and Russian monarchs.” Therefore this Bulgarian definition is
mapped to the more general concept for title. On the other hand, a second
definition in Bulgarian is added to reflect persons.
3. The Bulgarian concepts that are more specific than the English ones are
treated in a special way, too. For instance, “чичо”, “chicho”, and “вуйчо”,
“vujcho”, mean different things in Bulgarian. The former is brother of a
person’s mother or husband to a sister of the father or the mother. The latter
is brother of a person’s father. In this case the relations are mapped to the
more general “uncle” concept by specificity relation.
As it was mentioned, since it is important to preserve access to the PWN
hierarchy, it is necessary to align the concepts by introducing a non-lexicalized
definition in the Bulgarian lexicon, namely “Brother of a person’s mother or
father”, which corresponds to the English one. We should note again that this
step is not done at the cost of losing language specific concepts. In this way a
conditional connection is established, which will be made more complex in the
Bulgarian concept hierarchy, because the relevant definitions of “чичо”, “chicho”,
and “вуйчо”, “vujcho”, will denote subcategories of the newly created one.
Metonymic Usages. If a word is used with its metonymic sense, then the
metonymic sense is mapped to the appropriate sense in the PWN. For example,
in Bulgarian the word “армеец”, “armeec”, can be used in two senses: literal and
rare (soldier), and metonymic and more frequent (member of a specific football
team). When used in the latter sense, it must not be mapped to the concept of
soldier, but to the concept of footballer. In this way, the specific features of the
figurative language are kept in the lexicon.
Extended Mapping. With regard to Bulgarian, derived nouns with a spe-
cial suffix and an ending “-ка”, “-ka”, mark the feminine gender and denote
female persons. Thus, an addition is needed of a definition indicating the ref-
erence to a female. For example, “баскетболист”, “basketbolist”, is a basketball
player, but “баскетболистка”, “basketbolistka”, is a a female basketball player.
Accordingly, the definition remains mapped to the more general synset in English
(provided that no equivalent-level definition is available). The two definitions in
Bulgarian are related in a one-to-one manner, without indicating that one is a
subclass of the other.
5 Current and Future Developments
Here we discuss briefly: the treatment of MultiWord Expressions (MWEs) as
specific cases of lexicalization; and extensions of the current version with new
words. We also present some directions of future developments.
5.1 Treatment of MultiWord Expressions in BTBWN
Currently, we include MWEs as strings of several words separated by spaces.
They are represented in their standard form: lemmatized (where possible) and
reflecting the canonical word order. However, in this representation we lose in-
formation about possible word order variations of the MWE elements and their
potential for morphosyntactic variation and modification. In order to add this
information we rely on the notion of catena.
The notion of catena (chain) was introduced in [4] as a mechanism for repre-
senting the syntactic structure of idioms. He shows that for this task a definition
of syntactic patterns is needed that does not coincide with constituents. He
defines the catena in the following way: The words A, B, and C (order irrel-
evant) form a chain if and only if A immediately dominates B and C, or if
and only if A immediately dominates B and B immediately dominates C. In
rootC dobj
clitic
Vpi Pp Nc
– poss plur|def
– си очите
затварям си око
shut one’s eyes
CNo1 CNo1 CNo2
LC
SM CNo1: { run-away-from_rel(e,x0 ,x1 ), fact(x1 ), [1](x1 ) }
rootC
iobj pobj
Vpi R N
– – –
– пред –
затварям пред –
shut at –
CNo1 No1 No2
Frame semantics: No2: { fact(x), [1] (x) }
rootC
iobj pobj
Vpi R N
– – –
– за –
затварям за –
shut for –
CNo1 No1 No2
Frame semantics: No2: { fact(x), [1] (x) }
Fig. 2. Lexical entry for затварям си очите, “zatvaryam si ochite”, ‘I close my eyes’.
our work on BTBWN we convert MWEs into a representation defined in [12]
and [13] in which the catena is depicted as a dependency tree fragment with
appropriate grammatical and semantic information. Here we demonstrate the
model by just one lexical entry for the Bulgarian MWE: затварям си очите, “zat-
varyam si ochite”, “I close my eyes”. The lexical entry uses the following format:
a lexicon-catena, semantics (SM) and valency (Frame). The lexicon-catena
for the MWEs is stored in its canonical form. The realization of the catena
in a sentence has to obey the rules of the grammar. In this way the possible
word order is managed. The semantics of a lexical entry specifies the list of
elementary predicates contributed by the lexical item. When the MWE allows
for some modification (including adjunction) of its elements, i.e. modifiers of a
noun, the lexical entry in the lexicon needs to specify the role of these modifiers.
For example, the MWE represented in Fig. 2 ‘затварям си очите’.7 The valency
frame contains two alternative elements for indirect object introduced by two
7
The grammatical features are: ‘poss’ for possessive pronoun, ‘plur’ for plural number
and ‘def’ for definite noun.
different prepositions. The situation that the two descriptions are alternatives
follows from the fact that the verb has no more than one indirect object. If there
is also a direct object then the valency set will contain elements for it as well.
The semantic contribution of the indirect object is specified for each valency
element. This semantic contribution is added to the semantic contribution of
the lexical entry when the valency element is realized. In the dependency tree
fragments also grammatical features and lemmas are represented. The catena for
the frame and for the whole lexical entry are unified on the basis of nodes with
the same names. In this case CNo1. Within BTBWN the semantic contribution
will depend also on the corresponding synsets to which the MWEs belong.
5.2 Extensions of BTBWN
The extension of the BTBWN coverage is a constant task. The selection of new
lexical entries, including new synonyms, new senses for words that are already
in synsets in BTBWN, new words with corresponding new senses and synsets is
an on-going activity. To perform this task we use the following approaches: (1)
a task-based approach; (2) a dictionary-based approach; (3) a corpus-based ap-
proach; and (4) a linguistically-based approach. All of these approaches are used
by different language groups for the construction of the corresponding wordnets.
We briefly present each of them.
The task-based approach concerns with the coverage of BTBWN for a concrete
application. For example, the construction of lexicons for domain ontologies. In
this case we identify concepts for the task to be performed and construct an
aligned lexicon. The synsets in the domain lexicon are also aligned to the rest of
BTBWN. The identification of the domain concepts frequently is based on the
annotation of appropriate domain texts.
The dictionary-based approach is performed by comparing senses for each
word that is already in BTBWN with senses of the same word in a given dictio-
nary. We did this in two ways comparing with senses registered in the Bulgarian
Wiktionary and with senses in the Bulgarian explanatory dictionary. In many
cases we reformulated the identified uncovered senses on the basis of the existing
senses in BTBWN and with the requirement for a better mapping to the English
PWN.
The corpus-based approach uses several mechanisms for identification of new
words and senses to be added to BTBWN. They include at least the following
ones: (1) annotation of new texts; (2) clustering of word forms in a large corpus on
the basis of their contexts (Polish WordNet and some others); and (3) checking
the coverage of BTBWN over a frequency list compiled from a large corpus.
Currently, we perform point three over a frequency list compiled over a 7- million-
word corpus covering different types of text. Our goal is to include all the words
that appear in the corpus at least 100 times. Any word that is not presented
in BTBWN is lemmatized in all possible ways and then it is included by each
possible lemma and each possible sense. For example, the Bulgarian word form
“поет”, “poet”, is lemmatized as a noun “поет”, “poet”, and a verb “поема”,
“poema”, “take”. In this way we cover all frequent word forms in the corpus
with their relevant senses, independently from the context. The people who add
the new words and senses are free to search for usages of the corresponding
lemmas not only within the corpus, but also on the web.
The linguistically-based approach exploits productive phenomena within the
language. We mainly exploit derivation patterns with clear new semantics. For
example, the names of citizens of a given location is such a case: from “New
York” to form “New Yorker”. This pattern is easy to recognize in the corpus and
the definition and mapping to the rest of BTBWN is thus predictable.
Besides these two current activities we plan to perform also the following two
tasks: (1) addition of relational structure over BTBWN; and (2) including the
synsets that are not mapped exactly to synsets in PWN to the Collaborative
Interlingual Index (CILI) — [14]. For the latter task it is necessary to write ap-
propriate definitions in English. For the former task we will exploit the mapping
to English WordNet and additionally the mapping from the English WordNet
to the Polish Wordnet. In this way we will be able to transfer relations between
synsets in English and Polish WordNets to Bulgarian. Thus, we expect to impose
a reliable relation structure over BTBWN. We manually will check the cases of
lexical relations like antonymy and derivation and the cases where English and
Polish Wordnets disagree with each other.
6 Conclusion
The paper discussed our strategy for the mapping of the word senses in a tree-
bank to the WordNet ones in the context of the overall construction of the
BTBWN. In the presented approach the resource annotation does not rely on
pre-created WordNet, but rather on an explanatory dictionary of Bulgarian.
Later on, these senses have been mapped to the PWN 3.0, while keeping the
language specific concepts through the introduction of special markings. The
adopted strategy allowed for dense connectivity between the resources, and at
the same time it leaves room for the further creation of a language-specific hi-
erarchy. Currently BTBWN contains 12,147 synsets equivalent to the synsets in
PWN, and about 2500 additional synsets mapped as described in the paper.
These mappings have been exploited actively for knowledge-based word sense
disambiguation of Bulgarian by using the English WordNet as a knowledge graph
that transfers the linguistic relations to the Bulgarian lemmas. Also, they will
determine the language-specific hierarchy of concepts over the Bulgarian def-
initions.
Acknowledgements
This research has received partial support by the grant 02/12 — Deep Models
of Semantic Knowledge (DemoSem), funded by the Bulgarian National Science
Fund in 2017–2019. We are grateful to the anonymous reviewers for their re-
marks, comments, and suggestions. All errors remain our own responsibility.
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