Automatic Word Sense Mapping from Princeton WordNet to Latvian
Laine Strankale and Madara St
Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia
WordNet, Latvian, Automatic Extension.
Latvian WordNet is a resource where word senses are connected based on their semantic relationships. The
manual construction of a high-quality core Latvian WordNet is currently underway. However, text process-
ing tasks require broad coverage, therefore, this work aims to extend the wordnet by automatically linking
additional word senses in the Latvian online dictionary T
¯ and aligning them to the English-language
Princeton WordNet (PWN). Our method only needs translation data, sense definitions and usage examples to
compare it to PWN using pretrained word embeddings and sBERT. As a result, 57 927 interlanguage links
were found that can potentially be added to Latvian WordNet, with an accuracy of 80% for nouns, 56% for
verbs, 67% for adjectives and 66% for adverbs.
WordNets are an important tool for modern linguis-
tic research enabling in-depth semantic analysis of
synonymic, hyponymic and meronymic relations be-
tween word senses in Latvian, as well as correspond-
ing interlingual semantic relations. Additionally, it is
an essential resource in other NLP tasks such as word
sense disambiguation (WSD).
Until now, the focus of Latvian WordNet con-
struction has been on manually developing a small
but qualitative core wordnet. This paper aims to ex-
pand the coverage of the wordnet by automatic means.
More specifically, we attempt to automatically find
equivalence links between word senses in an exist-
ing Latvian language dictionary T
ezaurs with synsets
in the Princeton WordNet (PWN) which allows us to
transfer semantic links to Latvian and to combine Lat-
vian word senses into new synsets thus significantly
expanding the coverage of Latvian WordNet.
The first wordnet for English named Princeton Word-
Net (PWN) (Fellbaum, 1998) heralded the era of
wordnet constructions. It was created manually, how-
ever since then multiple projects (Vossen, 1998)(Tufis
et al., 2004) have tried to exploit semi-automatic or
automatic methods and existing resources to acceler-
ate the process.
A common approach for both initial construc-
tion and extension is to essentially copy the struc-
ture of PWN and then translate the synsets to the tar-
get language, for instance, in FinnWorNet (Lind
and Carlson, 2010) it was done by employing pro-
fessional translators who translated around 200 000
senses completely manually. Open Dutch WordNet
(Postma et al., 2016), Persian WordNet (Montazery
and Faili, 2010), WN-Ja (Bond et al., 2008) and
many other projects used existing bilingual dictio-
naries. The French WOLF (Sagot and Fi
ser, 2012)
also added translation data from Wikipedia and the
Slovene sloWNet (Fi
ser and Sagot, 2015) extracted
word pairs from parallel texts.
The translation step is usually followed by a fil-
tering step. For sloWNet and WOLF a classifier was
developed that used hand-crafted features such as se-
mantic distance and translation pair origin. Whereas
an unsupervised method (Khodak et al., 2017) (fur-
ther called the embedding method) was tested on Rus-
sian and French which used similarity metrics calcu-
lated from word embeddings to rank candidate links.
The filtering step seems essential for a large cover-
age otherwise the translation step is limited only to a
small subset of highly reliable translations.
In contrast to the copying method, core DanNet
used a monolingual construction approach wherein
they extracted semantic link information from an ex-
isting language resource: a dictionary (it mainly had
homonym links) (Pedersen et al., 2009). Similarly
RuWordNet (Loukachevitch and Gerasimova, 2019)
used existing sense level translations in RuThes to
Strankale, L. and St
ade, M.
Automatic Word Sense Mapping from Princeton WordNet to Latvian WordNet.
DOI: 10.5220/0011006000003116
In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) - Volume 1, pages 478-485
ISBN: 978-989-758-547-0; ISSN: 2184-433X
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
link with PWN.
Although the monolingual approach produces lin-
guistically higher quality results, the major disadvan-
tage of it is that the resource cannot be used in any
multilingual settings whereas after the copying ap-
proach the wordnet automatically gets linked to other
wordnets in the Open Multilingual WordNet (OMW)
(Bond and Paik, 2012).
Therefore, the monolingual wordnets often still
require subsequent linking to PWN. In the merging of
DanNet and PWN it was noted that the two resources
differ significantly in both structure and vocabulary
and, thus, a perfect merge is improbable (Pedersen
et al., 2019). Additionally, it should be noted that
the average inter-annotator agreement rate for PWN
is only 71% (Palmer et al., 2004).
From this, we can conclude that any alignment
technique be it done before or after initial wordnet
construction cannot produce very high precision re-
sults. However, an alignment process is unavoidable
if we want a highly applicable multilingual resource,
therefore, we have to be careful about how the align-
ment is generated and used to append data to the ex-
isting wordnet.
Given the previously outlined problems with both the
copying and monolingual methods, in Latvian Word-
Net construction, we have aimed to combine them
In the first phase we are manually constructing
a core wordnet of 5000 word senses. We largely
base our wordnet on the sense data from a pre-
existing resource T
ezaurs which is a digital compila-
tion of legacy dictionaries maintained by the Institute
of Mathematics and Computer Science of the Univer-
sity of Latvia (IMCS UL) and contains more than 381
000 entries (as of September 2021). In this phase we
take the most popular words (as determined by pars-
ing The Balanced Corpus of Modern Latvian), check
and edit the sense inventories and add usage exam-
ples (for future WSD tasks). A particular challenge
was developing a methodology for separating verb
senses in a systematic but language appropriate man-
ner(Lokmane and Rituma, 2021). These new synsets
have both inner and outer links, that is, they are con-
nected to each other and they have manually found
links to PWN synsets. Inner links have types:
approximate synonymy (weaker than the criteria
for inclusion in the synset)
related words (only when the semantic relation is
PWN links have three types:
- exact match
- narrower than Latvian WordNet sense
- broader than Latvian WordNet sense
Currently around 1700 Latvian synsets have PWN
links, of those 74% are with the type l
In the second phase - the topic of this paper -
we are using an automatic expansion method to copy
synsets from PWN. We believe this approach allows
us to maximize both quality and resources since we
know that the manually-created core wordnet already
includes the most common and highly-polysemous
words which would have been the most problematic
for automatic methods. This allows us to speed up the
process without a significant decrease in the quality
of the wordnet.
As previously noted, the core Latvian WordNet is
built on top of an existing dictionary T
ezaurs. Since
we want the core wordnet and the new data to be com-
patible we are using the T
ezaurs sense inventory also
in our extension phase.
4.1 Selection Criteria
To find the best approach for extending the Latvian
WordNet we looked at three factors: (1) quality of
results (precision and coverage); (2) easy of imple-
mentation; (3) resource availability for Latvian. The
criteria were chosen so as to minimize the manual re-
sources needed and account for the specifics of Lat-
vian resource availability.
The chosen method is an adaptation of the un-
supervised method which used word embeddings to
construct vector representations of synsets and rank
them by calculating similarity metrics. Our method
is adapted in the following ways: (1) the automatic
sense disambiguation step is skipped because we have
access to sense inventories in T
ezaurs; (2) the vector
representation of a synset uses BERT sentence em-
beddings in addition to word embeddings.
The method was chosen because, firstly, it doesn’t
necessitate or heavily rely on language resources,
such as Wikipedia or parallel texts, which are poor for
Automatic Word Sense Mapping from Princeton WordNet to Latvian WordNet
Latvian, secondly, the data preparation can be largely
automatized (no manual translations, checks, or hand-
crafted features).
Finally, there are two important points that should
be noted concerning the chosen method:
(1) This work is concerned with the information
that can be extracted by finding commonalities be-
tween the Princeton WordNet and T
ezaurs sense dic-
tionary. Thus only concepts that exist in both lan-
guages, to be exact, in both resources, can potentially
be found and added to the Latvian WordNet. This is a
limitation but it still allows us to get a large number of
word senses and, importantly, it produces synsets that
are linked to a PWN equivalent, thus, making them a
useful resource in future multilingual applications.
(2) PWN and T
ezaurs are sense inventories with
different development principles and levels of granu-
larity. Therefore, there is an upper limit to the pre-
cision that can be achieved with this method. As
already noted, to compensate for the differences the
manually-set interlingual links had three different la-
4.2 Overview
Fundamentally, we are trying to align T
ezaurs and
PWN by automatically getting all possible links be-
tween a T
ezaurs sense and a PWN synset (further re-
ferred to just as links), scoring the links using a sim-
ilarity metric calculated from embeddings, and pick-
ing the best links.
This is done in three steps (see figure 1):
1. Link Generation
2. Link Scoring
3. Link Curation
4.3 First Step: Link Generation
In the first step we prepare data and generate the set
of all possible links for each word sense.
4.3.1 Description
Firstly, how do we generate all possible links for a
set of T
ezaurs senses? The most obvious way is to
produce a list of all possible English translations for
entry lemmas and match them with lemmas in PWN.
We have chosen to use a combination of three transla-
tion sources: bilingual dictionary, machine translation
(MT), and links between Wikipedia article titles in
different languages. The hand-crafted bilingual dic-
tionary yields the highest quality translations while
MT and Wikipedia allow to extended the vocabulary
Secondly, which subset of data from T
ezaurs is
worth analyzing? An alignment with PWN allows us
only to extract wordnet data for words and concepts
which are common across languages. Thus old and
regional words should be excluded from the dataset
and left for future research. Additionally, MWEs
might behave differently and would necessitate addi-
tional processing, therefore, at the current stage we
also exclude those.
4.3.2 Implementation
The T
ezaurs dictionary is split into entries which have
lexemes (usually only one) and a sense inventory.
Word senses are structured into a two-level hierarchy
with main senses and their subsenses (here a subsense
indicates a slight shift in meaning.) The entries of-
ten (but not always) include the POS tag. All senses
have a gloss and a few have examples of use. Glosses
do not always follow the same structure and some in-
clude unprocessed textual information about its origin
or synonymy.
For this task we only look at single-word en-
tries. As the word associated to the sense we choose
the main lexeme with the exception of some two-
lexeme adjective+adverb entries, for instance, ”ener-
getic” and ”energetically”, where from a single sense
inventory we generate two sets of sense lists. The
ezaurs word list is further filtered down to exclude
obsolete word, regional words, proper names that are
specific to Latvia, slang, etc.
PWN synsets are divided by their POS: noun,
verb, adjective or adverb. Only the T
ezaurs lexemes
belonging to one of those categories are included. If
a lexeme does not have POS information then it is
found using a morphological analyzer for Latvian
We translate the glosses to English using Google
Translate. The lemmas are translated if possible with
Tilde’s bilingual dictionary otherwise with Google
Translate and translations extracted from Wikipedia
entry interlanguage link data
. Note that a single
lemma can have multiple translations.
Now comes the main step: generation of all possi-
ble (PWN synset)-(T
ezaurs sense) links. For each En-
glish translation t of lemma l we find all PWN synsets
s with same POS that include t in their synset lemma
list. To each l sense we add s as a potential link. For
instance, lemma ”cel¸
s” has 15 translations including
”path”, ”road”, ”way” and 17 PWN synset include
one of them in their lemma list like the synset path,
NLPinAI 2022 - Special Session on Natural Language Processing in Artificial Intelligence
Figure 1: Schema for the extension method for a single T
ezaurs entry (word). For each word all possible English translations
are found and looked up in PWN. Then all T
ezaurs senses for the given word are combined with all the found PWN synsets
to produce candidate links. Finally, for each sense the final link is found by scoring all its candidate links, discarding those
below a threshold θ and taking the highest (if has any).
route, itinerary - an established line of travel or ac-
cess. For each 13 senses and subsenses of ”cel¸
s” we
add all the 17 synset as possible equivalence links. As
can be seen correctly determining the equivalence is
not trivial.
4.4 Second Step: Link Scoring
In the second step we create a vector representation
for each entity, that is, each T
ezaurs word sense and
each PWN synset, which allows us to score each po-
tential link.
4.4.1 Description
To score links we have chosen to employ a vector sim-
ilarity metric. The results of the original embedding
method indicate that a combination of word embed-
dings can encompass meaningful information about
a synset and thus can in our task. Additionally, due
to more recent developments we have chosen to aug-
ment their technique with BERT data, which we ex-
pect could improve the rather simplistic construction
of sentence embeddings in the original paper (they
used a sum of word vectors).
Representation for a PWN synset rep
is con-
structed as follows:
1. Calculate v
, where L is a list of lemmas
in the PWN synset
2. Calculate v
where D is PWN synset definition
3. Calculate v
where E is a list of us-
age examples for the PWN synset
4. rep
= αv
+ (1 α)avg(v
, v
) where avg is
the element-wise average and α is a pre-computed
Representation for a T
ezaurs sense rep
is simi-
1. L is a list with one element: entry lemma for the
2. D is the word sense definition
3. E is a list of usage examples for the sense (most
do not have this information)
4. same calculation for rep
Then we use the representations rep
and rep
for each link to calculate its similarity score. To fur-
ther interpret the scores we have chosen to use a sim-
ple ranking algorithm wherein we gather a list of all
links for a single T
ezaurs sense and sort the list based
on the score. The sense gets assigned the link with the
highest score. This link is equivalent to the manually
added interlanguage link of type l
(note: in the man-
ual case these are synset-to-synset links but here we
have sense-to-synset link; we assume that all senses
that link to the same PWN synset should form a new
Latvian synset).
4.4.2 Implementation
To create a PWN synset representation we calcu-
late v
using pre-computed word embedding resource
based on the corpus data from Google News articles
(Mikolov et al., 2013). v
and v
is calculated us-
ing sentence-BERT (sBERT) (Reimers and Gurevych,
and the pre-trained BERT model all-MiniLM-
. The T
ezaurs sense representations are cal-
Automatic Word Sense Mapping from Princeton WordNet to Latvian WordNet
culated similarly except we use the English transla-
tion we obtained in the first step and not the original
For each link we calculate a lemma similarity
score and a definition similarity score via a simple
vector dot product.
4.5 Third Step: Link Curation
In the third step we determine the most equivalent
PWN synset for each T
ezaurs word sense and further
filter the results.
4.5.1 Description
In the previous step we assumed that all the highest
scoring links are valid l
links. This is not the case
because it is possible that, firstly, no such link exists
or, secondly, our step 1 did not generate the valid link
as one of the possibilities since word translations can
be lacking especially for less common word senses.
Therefore, we calculate and use a score threshold θ
below which all links are considered invalid.
The calculation for θ as well as α (from Step 2)
requires a correctly labeled dataset of T
links. When parameters are calculated we can use
them to directly generate results for the complete
4.5.2 Implementation
To obtain the coefficients α and β we extract a dataset
of interlanguage links from the manual Latvian Word-
Net. Note that the core word list differs from the
word list used in this extension phase since here we
are working with rarer words. However, this is the
only available labeled dataset and manual linking is
time consuming.
We process these senses as detailed in the first
and second steps to obtain the lemma and definition
similarity scores for each. Then we calculate the fi-
nal scores with gradually incremented values of α,
choose the highest-scoring synset and check whether
it matches with the one indicated in the core Latvian
WordNet. The parameter that yields the highest pre-
cision are further used for the rest of the data set.
The score threshold - the cutoff point under which
we considered that the link does not represent a valid
equivalence - is calculated by maximizing the F
ric and looking at thresholds in the range [0, 1] incre-
mented by 0.01. We used the same test data set from
the core Latvian WordNet to get the final value.
When all parameters are determined we run the
ranking algorithm for all word senses in the data set,
get the highest scoring and discard the link if its score
is below the threshold.
Finally, the new links are used to create new Lat-
vian synsets in T
¯ by combining senses that
link to the same PWN synset. In addition for each
link we also save the generated score, which allows
future users of the Latvian WordNet to filter data by
the precision level which is desired.
WordNet evaluation methods vary widely which
makes it difficult to have cross-wordnet comparisons.
Some results only make sense in the context of the
language, the specific method chosen and their initial
staring point, and there is no standardization of eval-
uation methods. Therefore, we have chosen to mostly
focus on evaluating our results in the context of Lat-
vian and our specific needs.
We evaluate our results by looking at the coverage
(total link count) and the precision (how many of the
generated links are valid l
In evaluation we are using two different data sets.
Firstly, we are comparing it to the core Latvian Word-
Net, which has high quality, independently chosen
PWN links. Secondly, we are taking a random sample
of 400 produced links (100 of each POS) and manu-
ally checking whether they are valid. The two data
sets were chosen to show the method’s performance
on data sets of differing complexities which gives a
better sense of the real precision (see figure 1).
Table 1: Average word polysemy (including words with one
sense) in each Princeton WordNet, core Latvian WordNet,
and the T
ezaurs wordlist used for automatic extension.
Latvian WordNet
Our wordlist
Noun 1.24 3.18 1.26
Verb 2.17 5.99 2.23
Adj 1.40 4.89 1.72
Adv 1.25 2.92 1.93
Latvian and English differ linguistically in how
words are formed and used depending on the POS.
Therefore, we evaluate each POS separately. Addi-
tionally, this lets us avoid the issue wherein the more
common POS (noun) or more polysemous POS (verb)
skew or occlude the results when viewed in aggregate.
NLPinAI 2022 - Special Session on Natural Language Processing in Artificial Intelligence
5.1 Evaluation against Core Latvian
The details of the extracted test set can be seen in table
2. Significant portion of those links are of types l
and l
. We have chosen to exclude those from our
evaluation since our method aims to find l
Table 2: Interlanguage link counts in the test set extracted
from the core Latvian WordNet.
Noun 1144 402
Verb 495 310
Adj 134 95
Adv 101 23
At first we compare the results with the data set
from core Latvian WordNet. Here we look at the link
rankings produced in step 2 and measure whether the
correct link appeared in the top 1, 3 or 5 highest scor-
ing links. As seen in table 3 the precision is the high-
est for nouns and lowest for verbs, as we would ex-
pect. Given that, for instance, nouns have a median
of 22 candidates per sense and verbs 44 candidates,
the top 3 and top 5 metrics are significant and indi-
cate that although the method is not powerful enough
to distinguish between all those cases, the data could
be useful if further processed.
In addition we experimented with a setup where
the vector representations were calculated entirely us-
ing word embeddings (more similar to the setup in
the original embedding method) and as can also been
seen in Table 4, the results are better across all POS.
Table 3: Precision of the generated data after the applica-
tion of the threshold θ when compared to the links in core
Latvian WordNet.
POS Top 1 Top 3 Top 5
Noun (α = 0.34) 49.0% 65.7% 68.9%
Verb (α = 0.50) 37.3% 46.5% 47.2%
Adj (α = 0.59) 39.5% 50.8% 52.4%
Adv (α = 0.86) 47.4% 64.9% 68.0%
Table 4: Precision comparison for two methods: the origi-
nal method that uses only word embedding and our method
that supplements them with sBERT.
Only word
(our method)
Noun 49.0% 43.2%
Verb 37.3% 29.1%
Adj 39.5% 27.8%
Adv 47.4% 38.5%
5.2 Evaluation of Errors
To helps highlight any discrepancies in the automati-
cally generated links and make the necessary adjust-
ments we manually evaluated a subset of 100 sam-
ples, which were distributed evenly throughout the
four main lexical categories: nouns, verbs, adjectives,
and adverbs (25 samples in each).
The automatic links of adjectives and verbs have
been the most difficult to form, probably due to more
specific and distinct meanings that are less inter-
changeable with each other and more situationally
used than other parts of speech.
5.2.1 Nouns
In 11 samples the manually selected link matched the
first choice of automatically generated links. In one
case the manually selected link matched third-best
choice. In some cases, the system failed to differen-
tiate between more general and specific notions, for
example, by selecting “morality” (a set of perceived
values) instead of “moral” (a lesson). A similar ten-
dency could be seen in the Latvian term “frontier”,
for which the system instead had selected the seman-
tically broader term “boundary”. In other cases, how-
ever, the algorithm succeeded at selecting more ap-
propriate and nuanced links, surpassing the manu-
ally selected data. This was seen in the following
pairs: “mother” “ma”; “poetry” “verse”, where
the first option was manually selected, whereas the
latter was more semantically appropriate and selected
by the system.
5.2.2 Verbs
In 11 samples the manually selected link matched the
first choice of generated links, in two cases it matched
the third-best choice. Most discrepancies were con-
nected to verbs describing verbal exchange of infor-
mation, e.g. “say”, “tell”, “assure”, “verify”. This
could indicate that a broader set of samples is neces-
sary to identify, separate and correctly link the more
nuanced notions of verbal communication, which are
slightly different in Latvian and English. Forming
links for verbs describing the production of sound
also proved to be problematic, especially when deal-
ing with figurative meanings, as in “sing” (when talk-
ing about instruments, not people).
5.2.3 Adjectives
In 10 samples the manually selected links matched
the first choice of automatically generated links. In
four samples it matched second-best choice, and two
Automatic Word Sense Mapping from Princeton WordNet to Latvian WordNet
matched third or lower options. The best results were
yielded by less ambiguous adjectives the meaning of
which is not particularly nuanced, e.g. “Olympic”
or “central”, whereas the adjective “dear” proved to
be unexpectedly challenging, as the system could not
differentiate between the financial and sentimental
meanings of this term. On the other hand, the system
could successfully differentiate between the closely
similar terms “accomplishable” and “achievable” and
the link it generated matched the manually created
5.2.4 Adverbs
In 14 samples the manually selected links matched the
first choice. In four samples the manually selected
link matched second-best choice. In the case of ad-
verbs, the system generally seems to favour uncom-
mon terms with narrower, more specific meanings, for
example, by selecting “afresh” instead of “again” or
“synchronously” instead of “simultaneously”. As ex-
pected, the system also faced some difficulty select-
ing the right option for ambiguous Latvian terms that
are highly situational, for example “reiz” (once) and
“reiz” (finally), but it should be noted that such mean-
ings are the primary reason for using specialists in
combination with automatic linking.
5.3 Evaluation of All Generated Links
We have shown an evaluation against the core word-
net data. However, in reality we are interested
in the performance on the larger T
ezaurs wordlist,
which contains more Latvian-specific and less com-
mon words. The full extension data was evaluated
with the 400 link test set and as can be seen in table
5 has significantly higher precision, probably, mostly
due to reduced polysemy levels.
The final extension step was to remove highly du-
bious and invalid links. We used a simple threshold
metric which as shown in table 5 was effective at re-
ducing the proportion of invalid links in our resulting
data set. The link counts before and after this step
can be seen in table 6 and, as expected, there is a sig-
nificant decrease in the wordnet link counts, however,
given our experience from the core wordnet develop-
ment we know that we should not expect all Latvian
senses to have a perfect alignment in PWN. A careful
manual evaluation of the links in our sample supports
It revealed that we are mainly dealing with three
types of invalid links: (1) the Latvian sense does not
have an equivalent in PWN (wordlist selection fail-
ure); (2) the full candidate list for a sense does not
contain the valid PWN link (translation failure); (3)
the full candidate list contains the valid candidate but
it is not the highest scoring link (scoring failure). The
most common type, especially for verbs, was the first
and second.
It could be possible to further clean-up our word-
net by developing additional heuristics about which
words are unlikely to have a PWN link or a good
translation. However, we leave this as a research di-
rection for future work.
Table 5: Precision of the generated data before and after
the application of the threshold θ as determined by manual
evaluated of 400 links.
POS Precision (before θ) Precision (after θ)
Noun 52% 80%
Verb 35% 56%
Adj 49% 67%
Adv 47% 66%
Table 6: Count of the generated links before and after the
application of the threshold θ.
POS Count (before θ) Count (after θ)
Noun (θ = 0.49) 51 487 28 644
Verb (θ = 0.54) 35 181 20 667
Adj (θ = 0.55) 10 828 7 609
Adv (θ = 0.56) 3 667 1 007
Total 101 163 57 927
Our method achieves results that are similar to
those with more complex methods and for well-
resourced languages. The original embedding method
used on Russian (we chose to compare to Russian as
opposed to French since its language characteristics
should be more similar to Latvian) achieved 73.4%
precision with approx. 51 000 synsets. However, their
precision metric excludes all cases where the correct
synset was not in the generated list of synsets (in our
case 20%). WOLF found 55 159 pairs which in man-
ual evaluation and before thresholding and clean up
where 52% correct, after clean up 81% (almost 80%
of the new WOLF is nouns). sloWNet used a similar
method and achieved 25% initial precision and 82%
after clean up. Finally, a method applied to ajz, asm,
arb, dis and vie had an output of on average 53 000
and the average precision evaluated on a 5-point scale
was 3.78 (Lam et al., 2014)
In this paper we have described the method used for
the automatic extension of Latvian WordNet. First,
we outlined the current state of core Latvian Word-
Net, a manually constructed high-quality wordnet of
5000 word senses for the most common words which
NLPinAI 2022 - Special Session on Natural Language Processing in Artificial Intelligence
contains both interlanguage links and links to PWN.
Then we described the automatic extension technique
used to increase the coverage of Latvian WordNet. In
it we attempt to align the Latvian dictionary T
with PWN by, first, translating senses to English, sec-
ond, constructing a vector representation for each Lat-
vian sense and PWN synset using word embeddings
and sBERT, and, third, score links and filter them us-
ing a threshold.
The results were evaluated in terms of precision
and coverage by, first, comparing them to core Lat-
vian WordNet and, second, manually checking a sam-
ple of 400 senses. Ultimately we found 57 927 new
sense-synset pairs with precision of 80% for nouns,
56% for verbs, 67% for adjectives and 66% for ad-
The focus of this paper was on how to extract in-
formation from other wordnets, namely, PWN. How-
ever, as mentioned in Section 2, it is also possible
to extract interlanguage link information from a re-
source in the same language, if such a resource exists.
ezaurs sense glosses contain some textual informa-
tion about word formation and synonyms. However,
this data has not yet been processed and the existing
errors fixed, therefore, we have chosen to exclude it
for now but it is a fruitful future research direction
which could be explored.
We have shown that automatic extension of a
wordnet requiring only a target-language dictionary
and translation resources is possible. Our results will
be added to the current core Latvian WordNet and
merged into the online resource T
ezaurs, where they
will be available also to the public. Additionally, the
data will be used to develop word sense disambigua-
tion (WSD) capabilities for Latvian.
This research work was supported by the Latvian
Council of Science, project “Latvian WordNet and
word sense disambiguation”, project No. LZP-
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Automatic Word Sense Mapping from Princeton WordNet to Latvian WordNet