# Using Synchronic Definitions and Semantic Relations to Classify Semantic
Change Types

Pierluigi Cassotti, Stefano De Pascale, and Nina Tahmasebi
University of Gothenburg, VUB/FWO/KU Leuven
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
###### Abstract

There is abundant evidence of the fact that the way words change their meaning
can be classified in different types of change, highlighting the relationship
between the old and new meanings (among which generalization, specialization
and co-hyponymy transfer). In this paper, we present a way of detecting these
types of change by constructing a model that leverages information both from
synchronic lexical relations and definitions of word meanings. Specifically,
we use synset definitions and hierarchy information from WordNet and test it
on a digitized version of Blank’s (1997) dataset of semantic change types.
Finally, we show how the sense relationships can improve models for both
approximation of human judgments of semantic relatedness as well as binary
Lexical Semantic Change Detection.

Using Synchronic Definitions and Semantic Relations to Classify Semantic
Change Types

Pierluigi Cassotti, Stefano De Pascale, and Nina Tahmasebi University of
Gothenburg, VUB/FWO/KU Leuven<EMAIL_ADDRESS><EMAIL_ADDRESS>

##  1 Introduction

At any point in time, a word can have several meanings. Often, these meanings
share a certain degree of relationship with one another and the word is then
said to display polysemy. Diachronically, polysemy is a result of semantic
change, the process in which a word change its meaning/s. When new meanings
are derived, the relationship to existing meanings is determined by the type
of change that takes place. Examples include metaphorical extensions (the arm
of the sea) and specialization of usage (sand that used to mean the shore as
well as the grain of sand, and now only the latter). Several taxonomies of
semantic change have been proposed, from Reisig (1839) to Blank (1997), that
describe both the type of change as well as the causes of it.

However, the computational community, which has spent the past 15 years
developing computational models for automatically detecting semantic change in
corpora, has disregarded those taxonomies and processes proposed in the past
Tahmasebi et al. (2021). Sparked by the invention of neural word embedding
techniques, the focus has been on quantifying the degree of change, or
determining when change has taken place, without the use of sense-inventories,
dictionaries or knowledge-bases. Currently, the standard recipe for lexical
semantic change detection (LSC) is form-based Montanelli and Periti (2023):
each usage of a word is represented by its contextualized embedding, and the
embeddings of different time periods are compared using cosine similarity. The
comparison is done either by averaging pairwise similarities, or first
averaging the embeddings of each time periods and then comparing the averages.
When sense-based models are employed, (a) the embeddings are clustered to
derive sense representations and then clusters are compared over time, and
optionally (b) the clusters are labeled to ease interpretation.

So far, no computational work has been done to detect the qualitative type of
change that words experience. Nor have these change types been utilised when
building models for change detection. Our work therefore presents a first
approach to addressing both of these tasks.

We rely directly on generated definitions and sidestep the standard recipe,
i.e., to represent each usage of a word with its contextualized embedding. We
consider a word’s meaning, i.e., sense, as a latent construct and the
definition as one way to capture that latent construct. The use of definitions
to precisely describe the meaning of a word offers an ideal level of sense
representation, that combines both precision and abstraction, as well as a way
to summarize multiple usages. Thus, the use of definitions serves as a
dimension reduction technique, projecting N$\times$M contextualized embeddings
in two time periods to a significantly smaller set of n$\times$m definitions.
By using a single computational step rather than two (steps (a) and (b)
above), we reduce the complexity and decrease the uncertainty.

To classify semantic change types, we train a classifier on a synchronic sense
repository with definitions and semantic relations, namely WordNet. The
rationale behind this choice is that each of the type of semantic change
targeted in this paper corresponds to a synchronic relationship between
senses. This distant training procedure via synchronic information is
necessary as there are no available resources to date for change type
classification that surpass a few hundred examples.

Next, we test this classifier on an unseen dataset of historical semantic
changes. We therefore extend the dataset provided by Blank (1997), in which
for a given word and a pair of senses there is an associated type of semantic
change, with concise definitions for each sense. Finally, we incorporate
change type classification in a state-of-the art model for detection of
semantic change, and show an improvement in the model performance.

#### Contributions:

In this paper, we show that

  1. 1.

definitions of word senses can be used to detect semantic change type;

  2. 2.

we can classify the type of semantic change by training on synchronic sense
relationships using sense definitions; and that

  3. 3.

the use of type information can improve models for both graded Word-In-Context
(WiC) as well as semantic change detection.

##  2 Background and Related Work

###  2.1 Lexical Semantic Change Detection

Lexical Semantic Change Detection (LSC) is the task of automatically
identifying words that change their meaning over time. Meaning changes that
take place because whole senses are added or lost over time are considered as
binary change. Change can also be more subtle and relate to existing senses,
for example, the decrease in frequency of a dominant sense. We consider the
latter as graded change. Words can experience both binary and graded change
over (longer or shorter) periods of time, and both add some senses, loose
some, and change others.

Up until 2020, the majority of the methods that computationally modeled
semantic change were tested only on the ability to say if a word had changed
or not, conflating all kinds of change into one. Evaluation was ad-hoc and as
a result, no comparability across different methods was possible. This changed
when the SemEval-2020 Task 1 on Unsupervised Lexical Semantic Change Detection
campaign Schlechtweg et al. (2020) was launched in 2020. As a part of the
shared task, benchmarks covering Swedish, English, German and Latin were
released. Each benchmark covers a set of words, across a pair of corpora (or
subcorpora) with a temporal gap between the two. For each word, up to a 100
usages are sampled from each time period. These usages are paired both within
a corpus and across the corpora. Each annotated pair receive a graded Word-in-
Context score (see Table 1) by human annotators. The usages are considered
nodes and the pairwise annotations as edges when a diachronic word usage graph
(DWUG) Schlechtweg et al. (2021) is created. The DWUG is clustered using
correlation clustering and distribution of cluster members across time is used
to attain the binary and graded change scores for each word. The information
within a corpus facilitates the possibility to differentiate the synchronic
senses (polysemy and homonymy), while comparing across corpora for the
estimation of semantic change.

$\Bigg{\uparrow}$ | 4: | Identical | Identity
---|---|---|---
3: | Closely related | Context variance
2: | Distantly related | Polysemy
1: | Unrelated | Homonymy

Table 1: The DURel relatedness scale Schlechtweg et al. (2018) and the
respective Continuum of semantic proximity proposed by Blank (1997).

Methods for encoding word meaning in the LSC task range from distributional
semantic models such as count-based or Word2Vec approaches Hamilton et al.
(2016) and contextualized models Laicher et al. (2021) to the most recent
task-informed models such as XL-LEXEME Cassotti et al. (2023) and models based
on definitions Giulianelli et al. (2023). Task-informed models are supervised
models leveraging information from different tasks for LSC prediction. XL-
LEXEME and Deep Mistake Arefyev et al. (2021) are models trained on the WiC
task, while Gloss Reader Rachinskiy and Arefyev (2021) exploits the Word Sense
Disambiguation task. Definition-based models, on the other hand, finetune LLMs
for in-context definition generation. XL-LEXEME holds the state of the art in
multiple languages Periti and Tahmasebi (2024) and will thus be used here.

###  2.2 Sense Definition Modeling

Highly related to our work is the task of Word Sense Disambiguation (WSD) that
has long been of great interest to the community Navigli (2009). Similar to
other NLP tasks, model architectures have evolved over time, including models
based on knowledge-bases or the more common ones based purely on neural
networks. With the outstanding performance on a majority of existing NLP
tasks, generative Large Language Models (LLMs) have contributed to a
paradigmatic shift also for WSD. While previous models assign a pre-defined
label to a given usage of word $w$, current models generate the label of the
word meaning. In particular, such a label could be the definition of the word
in the specific usage context.

In this work we refer to definition modeling or definition generation as the
task of automatically generating dictionary-like definitions of a (latent)
word sense given a certain a word usage. The task is carried out using several
different approaches Gardner et al. (2022) and datasets, such as WordNet
Fellbaum (1998), Wiktionary 111https://www.wiktionary.org, Oxford English
Dictionary (OED) 222https://www.oed.com and the Urban Dictionary
333https://www.urbandictionary.com. Noteworthy is the contribution of
Bevilacqua et al. (2020), which is one of the first works using a large pre-
trained model, followed by Huang et al. (2021) who introduce a way to control
the specificity of the generated sense definitions, and finally, Giulianelli
et al. (2023) who use similarities between word sense definitions to
approximate human judgments on semantic proximity. We will use the latter
model in our work.

###  2.3 Classification in Historical Semantics

The way in which semantic change manifests itself is of great interest to the
linguistic community and over time various taxonomies of semantic change have
been proposed Reisig (1839); Paul (1880); Darmesteter (1893); Bréal (1904);
Stern (1931); Bloomfield (1933); Ullmann (1957); Blank (1997). These include a
rich set of examples that have undergone semantic change. While there is a
large overlap between the taxonomies (with generalization, specialization,
metaphorical and metonymical changes as its recurring core) they are very much
indebted to the then dominant theoretical frameworks (e.g., historical-
philological semantics, structuralist semantics etc.) Geeraerts (2010). All
taxonomies have been created using traditional, qualitative approaches and
while relying on collections of texts, the approach are not data-driven.

The lack of resources tailored to train models on these complex semantic
changes has prevented computational work thus far. Semantic change often
entails a complete loss of previous meanings, making most contemporary
computational lexical resources insufficient, as they typically miss the
historical meanings. Although datasets exist for phenomena like metaphors,
metonymies, or analogies, they often concentrate on the novel use of word
meanings and omit conventional examples Maudslay and Teufel (2022) thus
limiting the models’ ability to predict semantic change types. While there
exists no resources for training models, in this paper, we release a first
benchmark for testing models on their ability to detect semantic change types
given definitions.

##  3 LSC-CTD Benchmark

One of the most comprehensive and recent classification of semantic change is
Blank’s taxonomy Blank (1997). In this work, about 650 cases of semantic
change are classified covering the vocabulary of the Romance languages and to
some extent also German and English. Blank tries to reinterpret traditional
types of classification from a cognitive-linguistics point of view. For the
LSC Cause-Type-Definitions (LSC-CTD) Benchmark444The benchmark is available on
Zenodo https://zenodo.org/doi/10.5281/zenodo.11471317 we digitize the cases
reported by Blank and create a benchmark of definitions manually curated by an
historical linguist assisted by ChatGPT555https://chat.openai.com/. The format
of the dataset, the dataset statistics and the annotation procedure are
described further in Appendix A.

Blank’s type classification include specialization, generalization, co-
hyponymous transfer, auto-antonym, metaphor, antiphrasis, metonymy, auto-
converse, ellipsis, folk-etymology, analogy, meaning dilution, meaning
reinforcement, and doubtful cases. For our study, we use the following types
of change for which we can find a counterpart in the synchronic lexical
database.666Descriptions based on Geeraerts (2020).

  * generalization:

the old meaning is a sub-case of the new meaning (or: the new meaning has at
least one less definitional feature than the old meaning); other terms used to
refer to this type of change are broadening and widening. An example is the
(non-attested) Vulgar Latin *adripare, which originally meant ’to reach the
shore (by vessel)’ but in contemporary Romance languages has generalised to
’arrive (by any means of transport)’ (e.g.: French arriver, Italian arrivare)

  * specialization:

the new meaning is a sub-case of the old meaning (or: the new meaning has at
least one extra definitional feature than the old meaning); other terms used
to refer to this type of change are narrowing and restriction. An example is
Latin necare, which used to refer to ’to kill (by any means)’ but whose French
derivation noyer is now restricted to ’to kill by drowning’

  * co-hyponymous transfer:

the change that occurs due to naming confusion, that is, when a word is used
for new referents that are (wrongly) thought to be similar if not identical to
the old referents. For instance, in some Romance languages the word for ’rat’
is also used (in a derived form) to refer to ’mouse’ or vice versa
(Piedmontese: rat for ’rat’ and rató for ’mouse’; Spanish: ratón for ’rat’ and
rata for ’mouse’)

  * auto-antonymy:

also called contronymy or enantiosemy, is when a word develops a new meaning
that expresses a contrast, or is in opposition, to its old meaning. For
instance, the Latin adjective sacer started out with the positive sense of
’sacred’ and only later acquired the negative sense ’cursed’, as evinced by
the French sacré Traugott (2017); Karaman (2008)

##  4 Generating examples from WordNet

To train a classifier, we use WordNet, a database that provides a large set of
examples of lexical-semantic relations that are the synchronic counterpart of
the semantic change types. WordNet is a multilingual synchronic lexical
resource that can be queried to obtain semantic information for the covered
vocabulary. Each lemma in WordNet is found within a synset, which brings
together cognitive synonyms. Each synset therefore represent a sense or
concept shared between several lemmas. Synsets are organized hierarchically in
a tree structure, where the arc between a node and its parent represents a
hyponymy relationship, meaning the lower node represents a more specific
concept than that represented by the parent. This relationship can also be
viewed inversely, where each parent node is connected to a child node if and
only if there is a hyperonymy relationship between them, that is, the parent
expresses a more general concept than the child node.

WordNet associates each synset with a sense gloss and a series of corpus
examples. The gloss functions as a concise definition which has been
specifically conceived to uniquely express the meaning of the synset, and thus
exemplify the latent construct of a sense. The examples, on the other hand,
are mere uses of the words, which are often under-specified with respect to
their meaning. For this reason, we focus on the sense gloss.

The relations of hyperonymy, hyponymy, co-hyponymy and antonymy can all be
considered the synchronic counterpart of the semantic change types introduced
in Section 3. Below we illustrate how we map the taxonomical relationship
between the sense glosses of synsets in WordNet to each considered change
type. Change types in Blank’s taxonomy that cannot be modelled through the
WordNet hierarchy are disregarded for this work. For hierarchical
relationships of hyponymy, hyperonymy, and co-hyponymy, we consider paths of
maximum length 1, meaning pairs for which there is a direct relationship.

#### Generalization and specialization:

For every synset, we extract the list of associated hyperonymous synsets, and
form pairs of a synset sense gloss with its hyperonym sense gloss. Viewed from
the perspective of the child synset to the parent synset, we obtain instances
of generalization. Similarly, by exploiting the inverse relationship (from
parent to child synset), we obtain examples of specialization.

#### Co-hyponymy:

We define a co-hyponymy relationship as two separate synset nodes with a
common synset parent, in which the two child synsets represent two different
and more specialized cases of the same general concept. By pairing the sense
glosses of the child synsets, we generate the same sense relationship that
lies at the basis of the diachronic co-hyponymous transfer.

#### Antonymy:

WordNet also encodes the relationship between synsets that express opposite
concepts, often between adjectives, but also found between other word classes
such as nouns (father vs. mother) and verbs (to buy vs. to sell). For each
synset, we therefore extract the synset, or the lemma within the synset,
labeled as antonym, and pair the sense glosses of these two synsets.

#### Homonymy:

Finally, we randomly generate pairs of synsets with the same part-of-speech
that do not lead to hyperonymous/hyponymous, co-hyponynic and antonymic
pairings of sense glosses. The potential unrelatedness between the senses
should be a good proxy for homonymy, defined as the occurrence of completely
unrelated senses (for the same word form). While it is possible that related
senses of the same word are paired in the random pairing, the risk is
extremely small (i.e., on average 3-4 out of the 30-40,000 randomly sampled
synsets can belong to the same word). Homonymy is not treated on the same par
as the other types of semantic change by Blank (1997), even though its
importance as a driver of language change is uncontroversial. For this reason
it has been included in the experiments.

Semantic Change type | Synchronic relation
---|---
Generalization | Hyperonymy
Specialization | Hyponymy
Co-hyponymous transfer | Co-hyponymy
Auto-antonymy | Antonymy
Unrelated | Homonymy
Table 2: Mapping between Semantic change types to the synchronic relations
found in WordNet.

#### The WordNet testset:

We divide the final dataset into three distinct datasets: training,
validation, and testing, selecting 80%, 10%, and 10% of the data respectively.
The examples are split in such a way that the same pair of definitions for a
specific class never appears simultaneously in two datasets
(training/validation, validation/test, training/test). Furthermore, to avoid
class imbalance, we limit the maximum number of examples per class to 30,000,
3,000, and 3,000 for training, validation, and testing respectively. The
statistic of the dataset are documented in Appendix C.

##  5 Model

To classify the relationship between senses, we utilize the data collected in
Section 4 and train a classification model777Our code is available on GitHub
https://github.com/ChangeIsKey/change-type-classification. The classification
model is available on the Hugging Face Model Hub
https://huggingface.co/ChangeIsKey/change-type-classifier. that takes as input
the two definitions of the respective senses and outputs the corresponding
class, i.e. {homonymy, hyperonymy, hyponymy, antonymy}.

Specifically, we concatenate the textual definitions $\delta_{1}$ and
$\delta_{2}$ together with special tokens into a single string
$\delta=\text{<s>}\delta_{1}+\delta_{2}\text{</s>}$, which is then tokenized
and encoded using roberta-large Liu et al. . From this, we obtain the vectors
corresponding to the sub-tokens of the original string $v_{1},..,v_{K}$. The
vector $v_{1}$, corresponding to the initial token, is used for
classification. We apply a linear transformation to reduce the vector’s
dimension to the label space, such that $v_{1}^{*}=Wv_{1}$, where
$W\in\mathbb{R}^{C\times N}$ and $C$ is the number of classes. Finally, we use
cross-entropy to optimize the model.

To counteract class imbalance, examples in each batch are drawn proportionally
from each class. We use AdamW for optimization with a weight decay of 0.1 and
a learning rate of 1e-6. Early stopping is employed to determine when to halt
training based on accuracy on the development set. Additionally, we assess the
outcomes using two baselines. The results for these baselines are detailed in
Appendix B.

##  6 Evaluation of the Type Classification

We first evaluate the model on the test portion of the synchronic relations
found in WordNet. Next, we test the model on a historical, novel data, namely
the LSC-CTD benchmark.

Figure 1: Confusion matrix for predictions of sense relationships on the
WordNet testset.

###  6.1 Evaluation on WordNet

We evaluate the classifier on the WordNet testset and investigate each class
individually using the confusion matrix found in Figure 1. The diagonal
results show correctly classified predictions for each class. The normalized
recall rate range from 0.83 for the co-hyponym class to 0.95 for the honomym
class. That means, the model correctly identifies between 83% – 95% of the
true class members showing that the model performs well in distinguishing
these classes.

For hyperonyms and hyponyms, the model shows a normalized recall rate of 0.91
and 0.90 respectively. The confusion mainly occurs with the converse
relationship (hyponyms/hyperonyms) (0.048/0.057), which is logical given the
close relationship between hyperonyms and hyponyms, as a hyperonym’s
definition is inherently included within its hyponym’s definition. There are
minor confusions with co-hyponyms (0.03) and a negligible amount with antonyms
and homonyms.

The model exhibits the lowest normalized recall rate of 0.83 for co-hyponyms.
Despite being almost 10-points lower than that for hyperonyms and hyponyms, it
still suggests a substantial capability to correctly classify co-hyponyms. The
confusion is distributed across the other hierarchical relationships. Co-
hyponyms get misclassified as hyperonyms (0.07), hyponyms (0.06), and to a
very small proportion as homonyms (0.01). The slightly lower performance in
this category could be due to the more complex nature of co-hyponym
relationships, which require the model to understand not just a direct
hierarchical link, but also parallel connections within the same level of
hierarchy.

The negative class serves as a control or baseline class against which
hierarchical or antonym relationships are compared. The model correctly
identify 95% of instances that do not exhibit a hierarchical relationship.

The small fractions of misclassification with other classes (0.008 with
hyponym, 0.009 with hyperonym, 0.026 with co-hyponym, and 0.004 with antonym)
indicate that the model is very effective at distinguishing non-hierarchical
and non-antonym relations from homonyms ones. This high performance is crucial
as it shows the model’s strong discriminatory power, effectively reducing the
false positive rate where hierarchical or antonym relationships are
incorrectly inferred.

###  6.2 Evaluation on the LSC-CTD Benchmark

To answer how well the model trained on synchronic relations can classify
diachronic relations, i.e., semantic change types, we test the model on the
LSC-CTD Benchmark. We follow the mapping shown in Table 2 and consider a
prediction correct within the mapped class. As an example, a pair of
definitions classified as having exhibited specialization is correct if the
model predicts hyponomy. Result are shown in Figure 2.

Figure 2: Confusion matrix for predictions of sense relationships on the LSC-
CTD Benchmark.

The model shows a high normalized recall rate of 0.85 for Specialization, that
is, 85% of all the specialization instances are correctly predicted as
hyponyms. The confusion occurs with co-hyponyms (0.15). This suggests that
while the model is generally good at recognizing the specialization that
occurs from a general to a more specific sense, it can sometimes fail to see
the vertical relationships between these senses and consider them on the same
horizontal level (as co-hyponyms).

The model exhibits a normalized recall rate of 0.68 for generalization, i.e.,
moderate success in identifying instances of generalization. There is a
notable amount of confusion with hyponyms (0.16), and co-hyponyms (0.11). This
asymmetry between generalization and specialization classes could be due to
the fact that going from more specific to more general definitions often
implies the use of fewer and more abstract or polysemous terms, which are
notoriously harder to represent.

The model has a low normalized recall rate of 0.17 for co-hyponymous transfer,
suggesting significant challenges in accurately classifying this type of
semantic change. The confusion is well distributed across the other classes.
with the largest misclassification being with hyponyms (0.46) and hyperonyms
(0.29). There might be two reasons for this: co-hyponymous transfer
fundamentally relies on confusion and referential ambiguity on the side of the
speaker, so it is not surprising that specifically text-based LLMs cannot
handle such confusion; in Blank’s dataset it is considered a phenomenon of
rather limited range, mainly observed in specific dialects (a third of the
examples come from Réunion Creole). The model seems to have a moderately good
grasp of auto-antonymy correctly classifying 62% of these instances. Here
there is room for improvement, in particular by increasing the number of
training examples on synchronic antonym relations.

##  7 Evaluation on Semantic Change

We will use our classification of semantic change type, introduced in Section
5, to test if we can improve on state-of-the-art LSC detection. In this
section, we use Flan-T5 XL FT presented by Giulianelli et al. (2023) to
generate definitions, which is a Flan-T5 XL Chung et al. (2022) model
finetuned on WordNet Miller (2013), the Oxford dataset Gadetsky et al. (2018)
and the CoDWoE dataset Mickus et al. (2022).

Figure 3: Ratio of human judgments for each class.

###  7.1 Semantic Relatedness and Homonymy

In this Section, we use the English portion of the SemEval-2020 Task 1 to
study the correlation between human annotated relatedness scores and word
sense relationships.

In the annotation of SemEval-2020 Task 1, (see further Section 2.1), pairs of
usages are sampled for each word. These usage pairs are either synchronic,
i.e. belong to the same time period, or diachronic, i.e. belong to different
time periods. For each pair of usages, the annotators have to assign a label
on a scale from 1 to 4 (Table 1): 1 (unrelated), 2 (distantly related), 3
(closely related), and 4 (identical). This scale draws inspiration from the
work of Blank (1997), which introduced the concept of a continuum of semantic
proximity. Specifically, Schlechtweg et al. (2018) provides a direct mapping
between the relatedness scale and the semantic proximity introduced by Blank
(1997).

For each usage in the English portion of the SemEval-2020 Task 1, we generate
a definition. We then predict word sense relationships using the definitions
for all the usage pairs for which human judgments are available. The word
sense relationships include homonymy, co-hyponymy (co-hyponymous transfer) ,
hyperonymy (generalization), hyponymy (specialization) and antonymy (auto-
antonymy). The bar graph in Figure 3 represents, for each predicted class, the
distribution of the existing human judgments based on the semantic relatedness
scale888It is important to note that the type classification model used in the
study is not designed to identify identical meanings, which corresponds to a
human judgment of 4..

The homonymy category has a high number of judgments in class 1 (Unrelated)
(60% of the overall unrelated pairs), which is consistent with the definition
of homonyms: words that are spelled the same and sound the same but have
different meanings. There are very few 3 or 4 judgments, which suggests that
humans generally do not find homonyms to be semantically related.

Overall, the distribution of human judgments reflects an understanding of
semantic relationships that aligns with the theoretical definitions of each
relationship type. Closely related judgments dominate in categories where a
semantic link is expected (co-hyponym, hyperonym, hyponym), while unrelated
judgments are more frequent for homonymy samples, where no semantic
relationship or an opposite relationship is anticipated.

###  7.2 Graded WiC Task

The model’s exceptional ability to identify unrelated pairs annotated with 1
clearly demonstrates its adequacy in modeling the homonymy class. We will use
this ability to improve models for the Graded WiC task.

We set up the evaluation as a Graded WiC task, directly comparing the semantic
proximity value provided by human annotators and the label computed by the
model. To do this, we transform the model’s computed labels into a two-value
scale: 1 (Related) and 0 (Unrelated), grouping the labels hyperonym, hyponym,
co-hyponym, and antonym under the Related class, while the homonymy class
represents the Unrelated class.

Furthermore, we investigate the combination of XL-LEXEME and Definitions +
Homonym using a weighting scheme:

$\rho(u_{1},u_{2})=\begin{cases}\cos(u_{1},u_{2}),&\text{if
$u_{1},u_{2}\>Related$}.\\\ 0,&\text{otherwise}.\end{cases}$ (1)

For the evaluation, we calculate the Spearman correlation between human
judgments and the labels computed as described. The results are presented in
Table 3, where we compare our model with XL-LEXEME, which represents the state
of the art in this task, and other strategies for calculating the distance
between definitions proposed by Giulianelli et al. (2023). As the results
show, the classification model achieves significantly better results compared
to using metrics like BLEU or cosine distance applied to definition vectors
calculated using SBERT. On the other hand, the model performs well below the
state of the art, with a 15-point difference from XL-LEXEME. However, when we
combine the classification model with XL-LEXEME, we achieve a new state-of-
the-art result, indicating that the classification model produces reliable
labels when classifying homonymy, i.e., instances annotated with 1.

Model | Correlation
---|---
Definitions + SacreBLEU | 0.108
Definitions + METEOR | 0.117
Definitions + Cosine similarity | 0.264
Definitions + Homonym | 0.472
XL-LEXEME | 0.623
Definitions + Homonym + XL-LEXEME | 0.646

Table 3: Spearman correlation of human judgments vs model predictions,
Definitions generated using the method of Giulianelli et al. (2023)

###  7.3 Binary Change Task

In SemEval-2020 Task 1, and in general for DWUGs datasets, words are annotated
as stable or changed based on the removal and/or addition of a meaning (i.e.,
cluster). In this process, annotators’ judgments are binarized, so that pairs
of usages annotated with a score lower than 2.5 represent negative edges,
while the rest of the usages represent positive edges. The optimization
algorithm then seeks to group positive edges into clusters, keeping them
separate from negative ones, with the resulting clusters representing word
senses.

Given the high correlation of the homonymy class with negative judgments (1
and 2), this represents an ideal proxy for automatically classifying words
that have changed in meaning. Specifically, to classify words that have
changed meaning in the SemEval dataset, we count for each word the occurrences
of each label returned by the classification model, specifically the counts of
hyperonym, hyponym, co-hyponym, antonym, and homonymy. We assign the label 1
(changed) to those words for which the homonymy class is the most frequent.

Model | Accuracy
---|---
Definitions + Homonym | 0.783
XL-LEXEME + 0.5 threshold | 0.761
XL-LEXEME + Opt. threshold | 0.848

Table 4: Binary task SemEval-2020 Task 1 (EN)

Table 4 shows the accuracy on SemEval-2020 Task 1 - Subtask 1 for the English
language. The results show that the Definitions + Homonym classification model
achieved 78.3% accuracy without the need for threshold adjustment. The XL-
LEXEME model reached 76.1% accuracy using the threshold of 0.5 (with which it
has been trained), while optimizing the threshold improved its accuracy
significantly to 84.8%. It is notable that the Definitions + Homonym
classification model performs well without the need to set up a threshold,
which indicate that it is robust and less sensitive to parameter tuning
compared to the XL-LEXEME model.

##  8 Conclusion

With this paper, we present a first computational approach to detecting the
type of semantic change that a word has experienced. We release the LSC-CTD
Benchmark, a first dataset for computational modeling of semantic change that
is annotated by type of change. The benchmark is based on a digitized version
of Blank’s taxonomy extended with concise sense definitions. Because the
resource is limited in size, it can only be used for testing purposes. To
train a model for classifying change types, we used WordNet with synchronic
sense definitions and relations between the synsets.

The use of WordNet as a foundational resource for training our classifier
enabled us to leverage a vast repository of semantic relationships and
meanings. Our evaluation is grounded both in linguistic theory and
computational metrics. We prove that homonyms are perceived by human
annotators as the most distant semantic difference class in Blank’s continuum
of semantic proximity.

We applied our classifier to extend semantic change detection models with type
information and tested it on a standard evaluation benchmark, namely
SemEval-2020 Task 1. We aimed to provide a more refined and accurate model for
semantic change detection. We found that by incorporating change types, in
particular the notion of homonymy, we improved the state-of-the-art results
for LSC on the graded task. For the binary task, we also saw encouraging
results.

Our findings underscore the importance of distinguishing between different
types of semantic changes, such as generalization, specialization, and co-
hyponymous transfer, in understanding the dynamics of semantic change. In
future work, we will extend our resources with more examples of change types,
as well as additional synchronic resources.

##  9 Limitations

Despite the promising outcomes, we acknowledge limitations in our study,
regarding the employed resource, the limits of the distributional hypothesis
and our focus on lexical type instead of token.

First, Blank’s database is a rich lexical resource, unique in its sort, but is
nevertheless the work of one scholar working from one theoretical background.
This likely introduces bias in the selection and diversity of the examples.
Another technical limitation of the benchmark is that we we took the generated
definitions for granted as long as they were factually correct, but more
detailed control on the specific wordings (for instance: to avoid specific
lexical cues that bias the model in one or the other direction) might improve
the results in the future.

Second, from a theoretical point of view, the types of semantic change under
scrutiny do not emerge from text, but are the result of (oral) communicative
exchanges and cognitive processes, and how both of these change over time. In
other words, written input/context will always fall short of providing
semantic information that is sufficiently rich to obtain optimal sense
representations. Our choice of working with definitions partially circumvents
this problem of under-determinism, but in general the limitation pertains to
all kinds of context-based methods relying on the distributional hypothesis.
As such, also our generated definitions are probably affected by it.

Third, our initial approach to tackling the problem starts from lexical types
and their definition, for reasons of control and feasibility, but, as
discussed above, semantic change unfolds in historical contexts and were
likely, at the time of creation, selected from concrete communicative
situations. The most appropriate level for modeling these phenomena is
therefore at the level of lexical tokens. To test this in the wild, we need to
have manually annotated, historical resources in which change types are
marked. This will enable studies to go from the level of definition to the
level of usages. Our team is working on constructing such an annotated
resource, but this is both expensive and time consuming and will likely only
be at such a level that it can be used for testing purposes.

## Acknowledgments

This work has in part been funded by the project Towards Computational Lexical
Semantic Change Detection supported by the Swedish Research Council
(2019–2022; contract 2018-01184), and in part by the research program Change
is Key! supported by Riksbankens Jubileumsfond (under reference number
M21-0021) and by an FWO junior postdoctoral grant (1281222N). The
computational resources were provided by the National Academic Infrastructure
for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research
Council through grant agreement no. 2022-06725.

We would also like to thank Haim Dubossarsky for providing valuable feedback
on the preliminary draft of this work, as well as for engaging in early
discussions that contributed to the development of this research.

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| Train | Development | Test
---|---|---|---
Unique synsets | 79469 | 15994 | 15978
Definition pairs | 123034 | 12379 | 12380
Hyponyms pairs | 30000 | 3000 | 3000
Hyperonyms pairs | 30000 | 3000 | 3000
Co-hyponyms pairs | 30000 | 3000 | 3000
Homonyms pairs | 30000 | 3000 | 3000
Antonyms pairs | 3033 | 379 | 380
Noun pairs | 85084 | 8535 | 8480
Verb pairs | 20772 | 2032 | 2137
Adjective pairs | 14268 | 1491 | 1473
Adverb pairs | 2910 | 321 | 290

Table 5: WN statistics.

##  Appendix A LSC-CTD Benchmark

In Table 6, we present an excerpt from the LSC-CTD Benchmark, a manually
curated dataset of dictionary-like definition pairs for words that experienced
semantic change. It includes Blank’s original annotations detailing the target
word followed by a language tag (lt: Latin, vlt: Vulgar Latin) and German
glosses of the original, primary meaning- (Old Meaning) and of the derived,
secondary meaning (New Meaning). We translate Blank’s glosses into English
using Google Translate’s API (between double quotation marks). Following this,
we use the prompt shown in Figure 7 to generate definitions that are more akin
to those found in a dictionary, employing gpt-4 API (between single quotation
marks). This prompt includes some examples we initially set up, with the
actual generation occurring through few-shot learning. Furthermore Blank’s
dataset contains the alleged cause of the semantic change and, not shown here,
the higher-level association relation that exists at the conceptual level.
Finally, a linguist with expertise in historical linguistics and semantics
manually reviewed and refined the collection of generated translations and
definitions, replacing or modifying them as necessary.

Word |  Old Meaning |  New meaning | Cause | Type
---|---|---|---|---
*adripare:vlt |  am Ufer ankommen |  ankommen | prototype / frame | generalization
|  "arrive at the bank/shore" |  "arrive" |  |
|  ‘arrive at the bank of a river or the shore of a lake or sea’ |  ‘to reach a place, especially at the end of a journey’ |  |
necare:lt |  töten |  ertränken | socio-cultural change | specialization
|  "kill" |  "drown" |  |
|  ‘to cause the death of a living thing, typically involving an act of violence or an intention to harm.’ |  ‘to cause to die by submersion in liquid, especially by forcing the head under the water.’ |  |
*ratta |  Ratte |  Maus | referential vagueness | co-hyponymous transfer
|  "rat" |  "mouse" |  |
|  ‘a small rodent, larger than a mouse, that has a long tail and is considered to be harmful’ |  ‘a small mammal with short fur, a pointed face, and a long tail’ |  |
sacer:lt |  heilig , geheiligt |  verflucht | taboo | auto-antonymy
|  "sacred" |  "cursed" |  |
|  ‘considered to be holy and deserving respect, especially because of a connection with a god’ |  ‘experiencing bad luck caused by a magic curse’ |  |

Table 6: Snippet of the LSC Cause-Type-Definitions Benchmark

##  Appendix B Baseline

As our baselines, we consider two models. The first is naturally the base
version of roberta (roberta-base) Liu et al. , as we have utilized the large
version for the experiments reported in the paper (see Figure 5). The training
of the base model is identical to that of the large model, employing the same
hyperparameters and data. The second baseline, on the other hand, is based on
the tf-idf weighting model (see Figure 4). Specifically, we calculate weights
across all definitions present in the training set. Subsequently, we extract
vectors for each definition and use the concatenation of vectors from pairs of
definitions as features for our model. In particular, we employ a simple
classifier based on stochastic gradient descent (SGD), utilizing the
implementation provided by scikit-learn999https://scikit-
learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
with default parameters, except for class weighting where we attempt to
mitigate imbalance using the balanced parameter. The tf-idf baseline is used
to investigate trivial patterns among pairs of definitions, that is, whether
the simple co-occurrence of words can be a signal for identifying the target
class. Not many differences are observed between roberta-base and roberta-
large, both on the WN test set and the LSC-CTD benchmark. In roberta-large
there is however a relatively large improvement in the detection of auto-
antonymy (+ 0.12) and generalization (+ 0.03), but also a lower performance
for co-hyponymous transfer (-0.04). The comparison between the roberta models
and the tf-idf models is more striking, with the latter model failing on
differentiating homonymy from auto-antonymy (probably due to not taking into
account the syntactic position of negations) again on both test sets.
Surprisingly, regarding the identification of co-hyponymous transfer, the tf-
idf model performs much better (+0.16 relative to roberta-large).

##  Appendix C WN Statistics

In Table 5 we report the statistics we use to train the sense relationship
model. The table includes for each split (train, dev, and test set) the unique
number of synsets, the number of pairs of definitions, and the number of pairs
of definitions for each class (hyponym, hyperonym, co-hyponym, homonym, and
antonym). Additionally, the number of distinct pairs for each part of speech
(nouns, verbs, adjectives, and adverbs) is reported.

(a) WN test set.

(b) LSC-CTD Benchmark.

Figure 4: SGD TF-IDF.

(a) WN test set.

(b) LSC-CTD Benchmark.

Figure 5: roberta-base.

(a) Number of cases for each type.

(b) Number of cases for each cause.

Figure 6: LSC CTD Benchmark statistics. Figure 7: Chat GPT prompt.


# Resolution studies with the DATURA beam telescope

H. Jansen${}^{\textrm{a,}}$***Corresponding author<EMAIL_ADDRESS>
${}^{\textrm{a}}$ Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany

###### Abstract

Detailed studies of the resolution of a EUDET-type beam telescope are carried
out using the DATURA beam telescope as an example. The EUDET-type beam
telescopes make use of CMOS MIMOSA 26 pixel detectors for particle tracking
allowing for precise characterisation of particle sensing devices. A profound
understanding of the performance of the beam telescope as a whole is obtained
by a detailed characterisation of the sensors themselves. The differential
intrinsic resolution as measured in a MIMOSA 26 sensor is extracted using an
iterative pull method and various quantities that depend on the size of the
cluster are discussed: the residual distribution, the intra-pixel residual-
width distribution and the intra-pixel density distribution of track incident
positions.

Keywords: CMOS pixel sensor, Beam telescope, Intrinsic resolution, Intra-pixel
studies

##  1 Introduction

In the research and development of particle detectors, beam telescopes are
indispensable instruments allowing for high precision characterisation of a
device under test. The multitude of published results using beam telescopes
reflect their importance. The integrated infrastructure initiative funded by
the EU in the 6th framework programme ’structuring the European research area’
included the EUDET project [1], which realised the EUDET-type beam telescope,
a high-resolution beam telescope using pixel detectors for test-beam studies
[2, 3]. The, in total, seven installations of EUDET-type beam telescopes are
deployed at different beam lines around the globe and rendered a multitude of
detector studies possible, cf. references [4, 5, 6, 7, 8] among others.

In this work, a study of the intrinsic resolution as a function of the cluster
size, i.e. the differential intrinsic resolution is presented Making use of
the few micrometre track resolution, the residual-width and density
distributions as a function of the reconstructed incident position within a
pixel cell is studied.

##  2 Experimental set-up

For this work, data have been taken at the DESY-II test-beam facilities [9].
The DATURA beam telescope, described in detail in reference [3], features six
pixel detector planes equipped with fine-pitch MIMOSA 26 sensors [10], the
mechanics for precise positioning of the device under test and the telescope
planes in the beam, a trigger logic unit (TLU) providing trigger capabilities
and a data acquisition system. Each MIMOSA 26 sensor consists of pixels sized
$$18.4\text{\,}\upmu\mathrm{m}$\,\times\,$18.4\text{\,}\upmu\mathrm{m}$$,
which are arranged in 1152 columns and 576 rows covering
$$21.2\text{\,}\mathrm{mm}$\,\times\,$10.6\text{\,}\mathrm{mm}$$. The average
intrinsic resolution, i.e. the average over all cluster sizes, has been
measured to be $3.24\text{\,}\upmu\mathrm{m}$ [3]. The TLU is based on a
commercial Spartan 3 board [11], and it features a coincidence unit with
discriminator boards accepting up to four input signals, e.g. stemming from
trigger scintillator devices. Providing a programmable logic, the TLU takes a
trigger decision based on its four input channels and issues asynchronous
triggers subsequently to connected sub-systems.

The chosen design meets most of the user requirements in terms of easy
integration capabilities, spatial resolution and trigger rates. The telescope
planes are designed and built to keep the amount of the material that the beam
particles traverse as low as possible in order to achieve an excellent track
resolution even at beam particle energies of a few GeV.

This article uses a right-handed coordinate system with the $z$-axis along the
beam axis and the $y$-axis pointing downwards. The origin of the coordinate
systems is located at the centre of plane 0. A narrow set-up with an
equidistant plane spacing was chosen for this study, cf. figure 1, with
$\textrm{d}z=$20\text{\,}\mathrm{mm}$$. The sensor threshold per plane is
tunable in multiples $\xi_{\textrm{n}}$ of their RMS noise. A sensor threshold
setting of $\xi_{\textrm{n}}$ therefore implies a collected charge in a fired
pixel of at least $\xi_{\textrm{n}}$ times the noise.

Figure 1: Sketch of the EUDET-type beam telescope with six MIMOSA 26 planes.

##  3 Results & discussion

The results reported here are based on data obtained using the DATURA beam
telescope in the test-beam area 21 at DESY with a beam kinetic energy of 6
GeV. The threshold of the MIMOSA 26 sensors were set to $\xi_{\textrm{n}}=6$.
The EUDAQ data acquisition framework and the EUTelescope analysis framework
were used for data taking and offline data analysis, respectively. The
clusters are formed by a sparse pixel clustering algorithm connecting all
adjoining pixel hits. Due to the lack of pulse height information from the
binary MIMOSA 26 readout, a simple geometrical interpolation of the cluster
centre is performed. Tracking was done using the concept of general broken
lines [12, 13], which accounts for multiple scattering in the material
traversed by the beam particles. Tracks have been formed in two ways, biased
and unbiased, to be used for different purposes. Biased track fits use hits
from all six pixel planes, i.e. also the hit information for the plane under
investigation, whereas an unbiased track fit excludes the hit information for
a given plane. More details about the analysis flow are presented in reference
[3].

### Cluster size distribution

Beam particles traversing the MIMOSA 26 sensors produce a variety of hit
patterns therein. The measurement accuracy of the incident position depends on
the size and the shape of the pattern. A hit with a cluster of size equal to
one (CS1) is a hit with a single fired pixel in the cluster, a cluster size 2
(CS2) hit contains information from two adjacent fired pixels, and accordingly
for larger cluster sizes. In a CMOS sensor, free charges with low kinetic
energy created close to or within the high-resistivity region are collected by
the nearest or next-to-nearest read-out electrode. Therefore, with
perpendicular incident, most of the produced cluster patterns are of size 1 to
4. A larger cluster size might e.g. arise from one or more additional noise
pixels, by delta-electrons or a second beam particle passing in the vicinity
of the first during a read-out cycle. The distribution of cluster size for a
MIMOSA 26 sensor of the DATURA beam telescope is shown in figure 2. The
distribution peaks for CS2 at about 28 %, the fraction of clusters larger than
4 decreases towards larger clusters. Clusters larger than six make up for less
than 7 % of the the total distribution, the mean cluster size amounts to 3.28.
The mean for cluster sizes 1 to 4 yields 2.54.

(a)(b)

Figure 2: The normalised cluster size distribution on (a) a linear and (b) a
logarithmic scale is shown. Error bars have been omitted as they are
negligible for cluster sizes $<15$.

(a)(b)
(c)(d)

Figure 3: The biased residual distribution at a threshold of
$\xi_{\textrm{n}}=6$ for cluster sizes 1 to 4 is shown in (a) to (d),
respectively.

### Differential intrinsic resolution

Following the discussion from reference [3], an iterative pull analysis has
been performed differentiating between cluster sizes. For each cluster size a
pull distribution with a width equal to one is thereby obtained, the resulting
biased residuals are shown in figure 3. The selection on the cluster size has
only been applied to the plane under investigation. An intrinsic resolution of
$(3.60\,\pm\,0.10\,(\textrm{sys.}))\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$,
$(3.16\,\pm\,0.09\,(\textrm{sys.}))\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$,
$(2.86\,\pm\,0.08\,(\textrm{sys.}))\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$,
$(3.40\,\pm\,0.09\,(\textrm{sys.}))\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$
for cluster sizes 1 to 4, respectively, is found. The procedure of estimating
the systematic uncertainties is discussed in reference [3]. The present
geometry of the set-up combined with the given intrinsic resolution, a biased
(unbiased) track resolution on plane 3 of about
$1.5\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$
($1.7\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$) is derived [14], which allows
for detailed intra-pixel studies.

### Intra-pixel density distribution

(a)(b)
(c)(d)

Figure 4: The unbiased intra-pixel density distribution at plane 3 for cluster
sizes 1 to 4 is shown in (a) to (d), respectively.

Figure 4 shows the density of the unbiased, reconstructed incident position as
a function of the $x$\- and $y$-coordinate on plane 3 within a single pixel
cell. In order to acquire sufficient statistics, tracks from the entire sensor
have been mapped into a single pixel. This is done within the local coordinate
system of plane 3 performing a modulo operation on the track incident position
by multiples of the pixel size along the $x$\- and $y$-axis. The origin of the
local coordinate system coincides with the lower left corner of a physical
pixel. For CS1, the distribution peaks in the centre of the pixel and falls
off towards all sides. CS2-hits populate most likely the edges of the pixel
cell, whereas CS4-hits usually result from tracks with an incident position
close to a pixel corner. The density of CS3-hits peaks on the diagonal through
the pixel cell at about $4\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$ along the
$x$\- and $y$-direction in the lower left quadrant, and accordingly in the
others. Hence, the position of the peak density within a pixel can clearly be
separated between the different cluster sizes.

### Intra-pixel residuals

With a track resolution considerably smaller than the pixel size the
construction of meaningful track residuals as a function of the track incident
position are rendered possible. Figure 5 depicts the residual-width as a
function of the $x$-coordinate of biased tracks. CS1-hits origin from
incidents close to the pixel centre, as shown in figure 4 (a). Since for
CS1-hits the assumed position during tracking is the pixel centre, the
residual-width is expected to have a minimum at the centre of the pixel, which
is confirmed in figure 5 (a). Accordingly, CS2-hits have their minima at the
edges of the pixel – for clusters of size 2 along $x$ – and in the middle –
for clusters of size 1 along $x$. For cluster sizes 3 and 4, larger
uncertainties towards the centre of the pixel are observed, which results from
the fact that only few tracks, that point to the centre of the pixel, result
in hits with cluster size larger two. A discrepancy is observed in the
position of the minimum for CS3: The unbiased peak position from figure 4 (c)
is at about $4\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$ along the
$x$-coordinate, but figure 5 (c) shows a minimum at about
$3\,\mathrm{\SIUnitSymbolMicro}\mathrm{m}$. The assumed position of the
CS3-hit within a pixel cell needs therefore to be corrected for this offset
during tracking.

(a)(b)
(c)(d)

Figure 5: The biased intra-pixel residual-width distribution at plane 3 for
cluster sizes 1 to 4 is shown in (a) to (d), respectively.

##  4 Conclusion

An analysis of test-beam data taken with the DATURA beam telescope was
presented discriminating between different sizes of clusters. As clusters
produced by traversing particles differ from one another in terms of size and
shape, also the accuracy of the reconstructed incident position depends on the
original cluster. Investigating the different clustersizes individually allows
for the extraction of the differential intrinsic resolution. With a biased
track resolution of about $1.5\text{\,}\upmu\mathrm{m}$ on the telescope
plane, intra-pixel studies revealed clearly distinguishable peak positions in
density for different clustersizes and allowed for the analysis of the
residual-width as a function of the track incident position within a pixel
cell.

## Acknowledgements

The test-beam support at DESY is highly appreciated. This work is supported by
the Commission of the European Communities under the 6th Framework Programme
’structuring the European research area’, contract number RII3-026126.
Furthermore, strong support from the Helmholtz Association and the BMBF is
acknowledged.

## Data and materials

The datasets supporting the conclusions of this article are available from
reference [15]. The software used is available from the github repositories:
1) https://github.com/eutelescope/eutelescope, 2)
https://github.com/simonspa/eutelescope/, branch scattering and 3)
https://github.com/simonspa/resolution-simulator.

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