=Paper=
{{Paper
|id=Vol-2915/paper14
|storemode=property
|title=Intrinsic Word Embedding Model Evaluation for Lithuanian Language Using Adapted Similarity and Relatedness Benchmark Datasets
|pdfUrl=https://ceur-ws.org/Vol-2915/paper14.pdf
|volume=Vol-2915
|authors=Mindaugas Petkevičius,Daiva Vitkutė-Adžgauskienė
|dblpUrl=https://dblp.org/rec/conf/ivus/PetkeviciusV21
}}
==Intrinsic Word Embedding Model Evaluation for Lithuanian Language Using Adapted Similarity and Relatedness Benchmark Datasets==
https://ceur-ws.org/Vol-2915/paper14.pdf
Intrinsic word embedding model evaluation for Lithuanian
language using adapted similarity and relatedness benchmark
datasets
Mindaugas Petkevičiusa, Daiva Vitkutė-Adžgauskienėa
a
Vytautas Magnus University, K. Donelaičio g. 58, Kaunas, 44248, Lithuania
Abstract
Word embeddings are real-valued word representations capable of capturing lexical semantics
and trained on natural language corpora. Word embedding models have gained popularity in
recent years, but the issue of selecting the most adequate word embedding evaluation methods
remains open. This paper presents research on adaptation of the intrinsic similarity and
relatedness task for the Lithuanian language and the evaluation of word embedding models,
testing the quality of representations independently of specific natural language processing
tasks. 7 different evaluation benchmarks were adapted for the Lithuanian language and 50 word
embedding models were trained using fastText, GloVe, and Word2vec algorithms and evaluated
on syntactic and semantic similarity tasks. The obtained results suggest that for the intrinsic
similarity and relatedness task, the dimension parameter has a significant impact on the
evaluation results, with larger word embedding dimension yielding better results.
Keywords
Word embeddings, evaluation, Lithuanian language, word2vec, fastText, GloVe
1. Introduction
The development of natural language processing tools influenced a growing need for word
embeddings as real-valued representations of words for text analytics, generated by applying
distributive semantic models. While word embeddings have become one of the most widely used tools
in modern natural language processing (NLP) applications, their limitations have not yet been fully
explored. The problem of assessing word embedding consistency and quality is one of the most relevant
questions in distributive semantics research.
The idea of word embeddings is not new, but it gained popularity after Mikolov et al. [1] presented
the Word2vec model in 2013. The fastText model, developed by Facebook AI Research (FAIR),
introduces embeddings using subword information. The next big improvement came from Stanford
with GLoVE (Global-Vectors) [2], based on word-word co-occurrence statistics in a corpus.
There are two types of word embedding evaluation: intrinsic and extrinsic. Intrinsic evaluation tests
the representation quality independent of specific natural language processing (NLP) tasks, while
extrinsic evaluation uses word embeddings as input features to an NLP task and measures changes in
corresponding performance metrics. We focus on intrinsic evaluation methods, based on human
annotated datasets, because datasets can be adapted for different languages by translating and
reevaluating human annotated scores.
The method of word semantic similarity, based on correlation with human judgment of how closely
words are related among themselves, was one of the first intrinsic evaluation metrics for distributional
meaning representations. According to this method, words smart and intelligent should be closer in the
vector space than smart and dumb, since smart and intelligent are intuitively better semantically related.
_________________________
26th International Conference Information Society and University Studies - IVUS 2021
EMAIL: mindaugas.petkevicius@vdu.lt (A. 1); daiva.vitkute@vdu.lt (A. 2)
ORCID: 0000-0002-1120-4848 (A. 1); 0000-0001-7923-1087 (A. 2)
Copyright 2021 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
There are gold-standard benchmarks for evaluating distributive semantic models such as SimLex999
[3], MEN [4], etc., focused on semantic relatedness. These benchmarks consist of certain word pairs
and their relative similarity scores. The similarity scores are defined in the interval between 0 and 10,
e.g., the score for the words book and paper is 7.46. When applied, these scores are compared with
word pair cosine vector similarity results for word embeddings.
The word analogy method aims to identify words based on operation prediction in a word vector
space. The method tries to predict a missing word in a word pair based on a known relationship in
another word pair. Thus, for a dataset a–b, c–d, the task is to identify an unknown word d based on the
known relationship between words a and b. For example, given the words, a (brother), b (sister), and c
(father), this method should correctly predict the value mother for word d [5]. The Google analogy
dataset [6] and BATS [7] are the most popular datasets. The Google test set has become the standard
for word embedding analysis. BATS is a newer dataset that is much larger and more balanced.
The word clustering method evaluates a word embedding space by applying the word clustering
approach. It is aimed at splitting a given word set into groups of words corresponding to different
categories based on word vectors. For example, words dog and cat belong to one cluster, while words
car and plane – to another [8].
The situation with word embeddings for the Lithuanian language is influenced by its specifics. The
Lithuanian language is a morphologically rich Baltic language, being considered one of the most archaic
living Indo-European languages [9]. It has a relatively large vocabulary, containing over 500 000 unique
words [10]. On the other hand, the Lithuanian language lacks textual resources due to the small size of
the nation using it. Lithuanian Wikipedia, for example, has 199 567 articles, while better represented
languages have over a million each [11]. Several attempts were made to perform intrinsic and extrinsic
evaluation of the Lithuanian language embeddings. However, so far there are no available semantic
similarity benchmarks for this purpose.
The goal of this research was to adapt selected intrinsic similarity benchmarks for the Lithuanian
language and to apply them for experimental evaluation of fastText, Word2vec, and GloVe embedding
models with different hyperparameters.
In order to reach this goal, we perform the following tasks: related work analysis (Section 2), corpus
building for embedding training (Section 3), methodology for the adaptation of evaluation benchmarks
for the Lithuanian language (Section 4), experimental evaluation of different embeddings based on the
derived benchmarks (Section 5), conclusions and future plans (Section 6).
2. Related Works
In recent years, there have been several critical articles on intrinsic assessment methods: some
researchers address the subjectivity of human judgments, the vagueness of instructions for particular
tasks, and terminology confusions [12]. However, despite these flaws, these methods are widely used
for embedding model evaluation for different languages.
There have been successful attempts to adapt intrinsic evaluation benchmarks to other languages.
Research has shown that when monolingual vector space models were translated into German, Russian,
and Italian, it became clear that their predictions did not always correlate well with human decisions
made in the language used for model training [13].
Another study attempted to translate the SymLex999 benchmark into Estonian and discovered that,
unlike in the original research, computational word embedding models better correlate with noun scores
rather than adjective scores [14].
A few studies on the evaluation of Lithuanian word embeddings have been carried out. In the first
study, word embeddings for different models and training algorithms were evaluated against a limited
implementation of the Lithuanian WordNet [15], showing that the Continuous Bag of Words (CBOW)
approach performed significantly better than the skip-gram approach for Word2vec word embeddings,
vector dimensions having little effect in this case.
The second study compared traditional and deep learning approaches for sentiment analysis using
word embeddings, finding that deep learning performed well only when applied to small datasets, and
that traditional methods performed better in all other contexts [16].
The third study was conducted with Transformer models using GloVe word embeddings [17]. The
study concluded that multilingual transformer models can be fine-tuned to word vectors, but still
perform much worse than specifically trained embeddings.
In conclusion, we see that the Lithuanian language lacks word embedding evaluation benchmarks.
3. Corpus
Semantic intrinsic similarity benchmarks cover many different types of test domains, such as
geography, languages, currency, etc. Therefore, we need to have a wide variety of data for embedding
training. Research has shown that for a larger corpus we get better word embeddings [18]. For this
reason, it is important to build an extensive corpus for embedding training, that will be further used for
evaluation.
Wikipedia texts are usually a typical approach for building a corpus for embedding training. In order
to expand our experimental corpus, we used articles from Lithuanian news portals, mainly from the
largest one, Delfi.lt, the collected articles covering different topical areas such as news, cars, fitness,
culture, food, and so on.
In order to obtain better word embeddings, we also included texts from the Corpus of Contemporary
Lithuanian Language (CCLL) [19], texts in a variety of genres and topics.
Statistics for our combined experimental corpus is presented in Table 1.
Table 1
Experimental corpus: initial version
Corpora Documents Token count Unique tokens
Wikipedia 286 089 22 942 951 971 506
CCLL 8 128 136 279 087 2 329 976
News articles 118 930 60 456 637 718 051
Total 413 148 219 678 675 2 894 874
The pre-processing phase consists of two steps: 1) breaking text into tokens, lowercasing text,
removing special symbols, numbers, non-Lithuanian words and stop-words 2) removing short
documents, less than 50 characters in size; 3) lemmatizing the texts, this being important for rich
morphology languages [20]. Lemmatization was performed using lexical and morphological analysis
tools from the Lithuanian language technology infrastructure built in the Semantika2 [21] project.
Alternatively, text could have been stemmed instead, but lemmatization was preferred, as all our
documents were in normative spelling and punctuation. Stemming is more favorable in case of social
texts with lots of out-of-dictionary words. Also, stemming has its limitations, e.g. over-stemming and
under-stemming problems [22].
The statistics for the final version of our experimental corpus are presented in Table 2.
Table 2
Experimental corpus: final version
Parameter Value
Document count 303 443
Total tokens 160 174 732
Unique tokens 1 396 607
4. Methodology
The methodology part covers the methods applied in this research: (1) benchmark dataset adaptation
method for semantic similarity based intrinsic embedding model evaluation; (2) semantic similarity
based embedding evaluation using the adapted benchmarks.
4.1. Adaptation of benchmark datasets
As a result of a brief analysis, the following English-language benchmarks were selected for the
adaptation to the Lithuanian language, their popularity being the main criteria:
1. MEN (Marco, Elia and Nam), 3 000 pairs [23].
2. WordSim-353, 353 pairs assessed by semantic similarity [24].
3. WordSim-353-REL, 252 pairs [25].
4. WordSim-353-SIM, 203 pairs [26].
5. SimLex-999, 999 pairs [27].
6. MTurk-287, 287 pairs [28].
7. RG-65, 65 pairs [29].
There are 5160-word pairs (2191 unique words) in total across all datasets.
The following algorithm was applied for the dataset adaptation to the Lithuanian language:
1. Automated translation of datasets (by applying the Google Cloud Translation API) [30].
2. Inconsistency checking (manual examination), discarding inconsistent word pairs.
3. Word lemmatization.
4. Re-evalution of the score that was initially assigned to the English language word pairs was
done by two independent persons (manual procedure). An average score was calculated.
4.2. Embedding evaluation using semantic similarity benchmarks
As mentioned in Chapter 1, the semantic similarity datasets are based on correlation with human
judgments of how closely words are related.
The similarity benchmark datasets consist of a certain number of word pairs. Each pair is determined
by its similarity and relatedness. The values are in the range [0, 10], depending on the dataset.
The word embedding models are represented by corresponding vectors for each word in the
dictionary. If a word is missing in the trained word embedding model, it is replaced by the mean of all
vectors. In order to calculate the similarity between vectors, we can use the cosine similarity formula
(see Eq. 1), where a and b are vectors in the word embedding vector space.
𝒂 𝒊 . 𝒃𝒊 (1)
𝑠𝑖𝑚𝑐𝑜𝑠 (𝑎𝑖 , 𝑏𝑖 ) = ,
||𝒂𝒊 || × ||𝒃𝒊 ||
here ai and bi are vectors of N-dimension. The result of cosine similarity is a value in the range [-1, 1]
interval, where 1 stands for identical vectors, and -1 for opposite vectors.
A human-annotated benchmark dataset consists of n triplets containing pairs of words and their
corresponding similarity scores ⟨wi , wj , hij⟩, where wi , wj are dictionary words, and hij is the score.
Let h = (hi1, hi2, . . . , hiN ) be a vector of human annotated benchmark datasets, and m = (mi1, mi2, . .
. , miN ), correspondingly, a vector of similarity scores calculated from word embeddings.
Then, the evaluation score for the corresponding embedding model, based on the selected
benchmark, is calculated as Spearman’s correlation ρ (see Eq. 2) between h and m.
Spearman 𝑝 value can be any value satisfying−1 ≤ 𝑝 ≤ 1, and the interpretation is that p values
close to +1 indicate stronger relationship, while those closer to -1 indicate weaker relationship.
The Spearman correlation formula is:
6 ∑ 𝑑𝑖2 (2)
𝑝 = 1− ,
𝑛(𝑛2 − 1)
where n – dataset length, d – difference between ranks of h and m.
The aggregated score of one-word embedding model 𝑝𝑎𝑣𝑔 is calculated (see Eq. 3) as:
𝑛
1 (3)
𝑝𝑎𝑣𝑔 = ∗ ∑ 𝑝𝑖
𝑛
𝑖=1
where 𝑝𝑖 is Spearman correlation value of specific benchmark, n – number of benchmarks.
In order to compare different embedding model types (Word2vec, fastText, GloVe). We can
calculate an average score of all models’ embeddings (see Eq. 4).
𝑛
1
𝑃𝑡 = ∗ ∑ 𝑝𝑎𝑣𝑔 (4)
𝑛
𝑖=1
where 𝑃𝑡 – an average score of all t word embeddings, n – number of t word embedding models.
5. Experiments and results
Experiments were carried out in a series of tasks:
1. Firstly, 7 selected evaluation benchmark datasets were adapted for the Lithuanian language.
2. Secondly, 50 word embeddings with different hyperparameter sets were trained on the
accumulated experimental corpus.
3. Thirdly, the obtained word embedding models were evaluated using the adapted intrinsic
evaluation benchmarks.
4. Finally, the resulting data were examined in order to determine the effect of different
hyperparameters on benchmark evaluation results.
5.1. Adaptation of benchmark datasets
The selected 7 (see Chapter 4.1) evaluation benchmark datasets were adapted from English to
Lithuanian language. There were 5610 word pairs at the beginning. After the adaptation process, 5573
word pairs remained. A total of 37 word pairs were discarded. The following problems were observed
during the adaptation process:
1. Multiple words – in some cases, one-to-one word translation is not possible, when a two-word
expression in the Lithuanian language is a correspondence to a single word in the English language.
For example, for the word pair “computer – software”, the Lithuanian translation would be
“kompiuteris – programinė įranga”. As we use vector-to-vector comparison, such word pairs were
discarded.
2. The meaning of certain words has been shaped by American culture, e.g. words like soccer,
football, and FBI. These are words that are commonly used in the US. Such words were replaced
with Lithuanian synonyms
3. A few older words have undergone semantic changes as their meanings evolved. For example,
the word pair “Arafat – terror”, had a greater similarity back in history than it does now. Such pairs
were discarded.
4. In some cases, both English words have the same meaning in the Lithuanian language, for
example, the following pairs: “smart – intelligent”, “happy – cheerful”, “fast – rapid”. Such word
pairs as a result contained two equal words, and their scores were set to 10 (maximum similarity).
An excerpt of the adapted SimLex999 dataset for the Lithuanian language is presented in Table 3.
Table 3
Excerpt of the adapted of SimLex999 benchmark for the Lithuanian language
word1 word2 value word1 word2 value
steak meat 7.47 kepsnys mėsa 8.2
nail thumb 3.55 nagas nykštys 4.5
band orchestra 7.08 grupė orkestras 7.6
book bible 5.00 knyga biblija 5.6
The first two columns contain an English word pair in its original form. The third column contains
the human-generated similarity score. The fourth and fifth columns contain Lithuanian translations of
English words and revalued Lithuanian word scores.
5.2. Word embedding model training
The following tools were used for word embedding training: python genism wrapper of Word2vec1,
fastText – official python library2, GloVe - official library3. We used similar training parameters in order
to be able to compare different word embeddings (see Table 4).
Table 4
Hyperparameters used for embedding model training
Word2vec FastText GloVe
Architecture CBOW, Skip-gram CBOW, Skip-gram
Global word-word Co-
Negative Sampling,
Model training Negative Sampling occurrence matrix
hierarchical SoftMax
Dimensions 100, 300, 500, 1000 100, 300, 500 100, 300
Window size 5 5, 10 5, 10
Minimum count 1, 2, 5 2, 5 2 ,5
A total of 50 (19 Word2vec, 20 fastText, 11 GloVe) word embeddings were created by applying
different hyperparameter sets.
5.3. Word embedding model evaluation
All the trained embedding models were evaluated using the Spearman ρ correlation coefficient
between human benchmark scores and vector space model scores. The results were grouped by different
vector model types, characterized by different hyperparameter sets (see Table 4).
The best 4 and the worst 4 models ranked by the benchmark result average are presented
correspondingly in Table 5 and Table 6. The first column in these tables indicates model name together
with hyperparameter indication. The following labels are used: N – negative sampling, S – SoftMax,
CBOW – Continuous Bag of Words, SKIP – Skipgram, d – dimension, w – window size, m – minimum
count threshold, i – iteration count. The rest are benchmark names and Spearman ρ correlation scores.
The last column shows aggregated Spearman 𝑝𝑎𝑣𝑔 correlation score of all the benchmarks.
1 https://radimrehurek.com/gensim/models/word2vec.html
2 https://github.com/facebookresearch/fastText/
3 https://github.com/stanfordnlp/GloVe
Table 5
The best 4 word embeddings ranked by 𝒑𝒂𝒗𝒈 (Spearman aggregated score)
Model MEN WS353 WS353R WS353S SimLex999 RG65 MTurk 𝑝𝑎𝑣𝑔
FastText SKIP
0.718 0.693 0.539 0.779 0.412 0.733 0.684 0.651
300d 5w 5m 5i
FastText SKIP
0.717 0.679 0.513 0.771 0.41 0.737 0.682 0.644
300d 5w 2m 5i
FastText SKIP
0.712 0.681 0.544 0.785 0.388 0.721 0.678 0.644
100d 5w 5m 5i
Word2vec NSKIP
0.711 0.679 0.507 0.749 0.422 0.766 0.66 0.642
300d 5w 1m 5i
Table 6
The worst 4 word embeddings ranked by 𝒑𝒂𝒗𝒈 (Spearman aggregated score)
Model MEN WS353 WS353R WS353S SimLex999 RG65 MTurk 𝑝𝑎𝑣𝑔
GloVe 300d 10w
0.657 0.584 0.426 0.689 0.378 0.706 0.614 0.579
1m 5i
GloVe 100d 10w
0.65 0.584 0.414 0.683 0.363 0.724 0.611 0.575
2m 5i
FastText CBOW
0.65 0.564 0.389 0.679 0.419 0.761 0.559 0.574
100d 5w 1m 5i
GloVe 100d 10w
0.647 0.588 0.432 0.673 0.356 0.709 0.609 0.573
1m 5i
Comparison between different types of embeddings (Word2vec, fastText, GloVe) was done by
averaging 𝑝𝑎𝑣𝑔 by embedding type (see Eq. (4).
To be able to do score comparison, only embedding models with the same hyperparameters were
used: dimensions (100, 300), window size (5), and minimum count (2, 5) (Figure 1).
Figure 1: Aggregated Spearman ρ correlation scores over different model types 𝑃𝑡 .
GloVe's word embedding model scores 𝑃𝑡 were on average lower than those of fastText and
Word2vec. The previous two were nearly identical, with a difference of only 0,001 between them.
Additionally, the experiment results were analyzed to determine whether a particular
hyperparameter had a significant effect on the results. Following a thorough examination of all the
hyperparameters, we discovered a correlation between the dimension value and the correlation results.
(Figure 2).
Figure 2: The correlation between vector size (d) hyperparameter and benchmark aggregated scores
grouped by embedding model type.
Different dimension values for various embedding types had a significant effect on the results. The
larger the dimension of the word embedding, the more accurate the results. As illustrated in Figure 3,
as vector size increases, the model correlation score also increases.
Figure 3: The correlation between vector size (dim) hyperparameter and benchmark
aggregated scores.
We can use Pearson correlation score 𝑟 (see Eq. 5) to see if there is correlation between values.
𝑛(∑ 𝑥 𝑦) − (∑ 𝑥)(∑ 𝑦) (5)
𝑟=
√[𝑛 ∑ 𝑥 2 − (∑ 𝑥 )2 ] [𝑛 ∑ 𝑦 2 − (∑ 𝑦)2 ]
where n – number of models, x – dimension value. y – Spearman correlation value for a model.
𝑟 = 0.918, this indicates strong relationship between values.
6. Conclusions
This was the first attempt to adapt the most popular intrinsic similarity and relatedness benchmark
datasets for the Lithuanian language. Despite reported challenges when adapting benchmarks to other
languages, we proved, that this can be done even for morphology rich languages like Lithuanian.
The application of the adapted benchmark datasets for the evaluation of the embedding models,
trained on an experimental corpus, showed, that GloVe model performed worse than fastText and
Word2vec, judging by average benchmark results.
We also conclude, that for the intrinsic similarity and relatedness task, the dimension
hyperparameter has a significant impact on the evaluation results, with larger word embedding
dimension yielding better results.
In the future, we plan to adapt other types of embedding evaluation benchmarks, such as
categorization and analogy testing, as well as extrinsic evaluation with POS tagging, named entity
recognition (NER), and other NLP tasks. This would allow us to compare intrinsic and extrinsic
evaluation methods. Also, we will continue to expand our corpus for future tests.
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