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  <front>
    <journal-meta>
      <journal-title-group>
        <journal-title>V. Mangiaterra);</journal-title>
      </journal-title-group>
    </journal-meta>
    <article-meta>
      <title-group>
        <article-title>Temporal word embeddings in the study of metaphor change over time and across genres: a proof-of-concept study on English</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Veronica Mangiaterra</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Chiara Barattieri di San Pietro</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Valentina Bambini</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Laboratory of Neurolinguistics and Experimental Pragmatics (NEPLab), Department of Humanities and Life Sciences, University School for Advanced Studies IUSS</institution>
          ,
          <addr-line>Pavia</addr-line>
          ,
          <country country="IT">Italy</country>
        </aff>
      </contrib-group>
      <pub-date>
        <year>2024</year>
      </pub-date>
      <volume>000</volume>
      <fpage>0</fpage>
      <lpage>0003</lpage>
      <abstract>
        <p>Temporal word embeddings have been successfully employed in semantic change research to identify and trace shifts in the meaning of words. In a previous work, we developed an approach to study the diachrony of complex expressions, namely literary metaphors. Capitalizing on the evidence that measures of semantic similarity between the two terms of a metaphor approximate human judgments of the dificulty of the expression, we used time-locked measures of similarity to reconstruct the evolution of processing costs of literary metaphors over the past two centuries. In this work, we extend this approach previously used on Italian literary metaphors and we present a proof-of-concept study testing its crosslinguistic applicability on a set of 19th-century English literary metaphors. Our results show that the processing costs of metaphors changed as a function of textual genre but not of epoch: cosine similarity between the two terms of literary metaphors is higher in literary compared to nonliterary texts, and this diference is stable across epochs. Furthermore, we show that, depending on the metaphor structure, the diference between genres is afected by word-level variables, such as the frequency of the metaphor's vehicle and the stability of the meaning of both topic and vehicle. In a broader perspective, general considerations can be drawn about the history of literary and nonliterary English language and the semantic change of words.</p>
      </abstract>
      <kwd-group>
        <kwd>eol&gt;metaphors</kwd>
        <kwd>distributional semantics</kwd>
        <kwd>temporal embeddings</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>1. Introduction</title>
      <p>terms of linguistic structure, metaphors normally involve its dificulty and we analyze how it varies across time
two terms, the topic and the vehicle: for example, in and textual genres. We also consider the role of word
frethe metaphor ‘Sally is a chameleon’, the topic Sally is quency (WF) and vector coherence (VC), two widely used
described by the broadened vehicle chameleon, to indi- measures in the study of semantic change [29, 30], as well
cate a person who changes attitude/behavior to fit their as semantic neighborhood density (SND) in shaping the
surroundings. While metaphors are broadly used in ev- dificulty of the expression. WF and VC were considered
eryday communication, they are certainly a distinctive to assess the efect of the semantic change of the single
feature of literary texts, as long evidenced in stylistics word on the evolution of whole metaphor
understand[15]. Past studies on literary metaphors, however, report ing, while SND was considered to analyze the impact of
mixed results. The rating study by Katz et al. [16] found a measure known to synchronically impacts metaphor
no diference between literary and everyday metaphors, understanding [31, 24] on its diachronic unfolding.
while other studies showed that the former type is less
familiar and more open-ended than the latter [17], but
literary metaphors are rated as less dificult and more 2. Methods
familiar when presented together with their original
context [18]. Moreover, the processing of literay metaphors 2.1. Dataset of metaphors
seems to be particularly efortful, given the multitude The study focuses on “classic” literary metaphors (i.e.,
of possible meanings they evoke [19]. Therefore, open metaphors found in 19th-century literary texts). In terms
questions remain regarding how literary metaphors are of metaphor structure, we focused on metaphors in the
processed. It must be also underlined that the literary form of ‘A is B’ (e.g. “Stars are dancers”) and ‘A of B’
metaphors used in previous studies were written tens or (e.g., “Clouds of melancholy”), as they clearly display
hundreds of years ago. Yet, the efect of this diachronic di- the two metaphorical elements (topic and vehicle) and
mension on their processing costs, as well as its interplay allow to avoid possible confounding factors (length of
with textual genre in which metaphors are embedded, expression, intervening words, etc.). Twenty- four (24)
remains an open question. ‘A is B’ metaphors were taken from the dataset in Katz
et al. [16] and 115 metaphors in the form ‘A of B’ were
retrieved from a collection of literary texts of the 19th
century. These latter were identified by PoS-tagging a
corpus of literary texts from the 19th century (see below)
with spaCy [32], and then extracting only the ‘NOUN of
NOUN’ constructions. The resulting list was then
further reduced by manually searching for words belonging
to known sources of metaphors, such as atmospheric
events (e.g., ‘rain’) or physical locations (e.g., ‘river’) [33],
following the methodology in Bambini et al. (2014) [18].</p>
      <p>In addition to its diachronic application, the use of
vector space models can help characterize metaphors
thanks to the ability of these models to approximate
human performance in psycholinguistic tasks. Measures
derived from vector space models were shown to be
able to approximate how humans process word meaning
[20, 21, 22] and, more specifically to correlate with how
humans perceive metaphorical expressions in terms of
metaphoricity, dificulty, and other psycholinguistic
dimensions [23, 24, 25]. In particular, semantic similarity,
operationalized in vector space models as cosine
similarity (CS) between topic and vehicle, has long been
considered relevant for metaphor studies [26] and, more
recently, for automatic metaphor identification [27].</p>
      <p>
        In a previous study on Italian [28], we developed a
novel method, employing the Temporal Word
Embeddings with a Compass (TWEC) model [
        <xref ref-type="bibr" rid="ref10">10</xref>
        ] as training
procedure, to capture the temporal dynamics of literary
metaphors. This method combines the computational
models’ abilities to approximate human judgments and
their diachronic applications, allowing to track the
diachronic evolution of how literary metaphors are
perceived by readers over the course of 200 years. In the
present proof-of-concept study, we apply this approach
to English, to test its crosslinguistic applicability and
whether it can provide language-specific insights into
the evolution of metaphors. We take the similarity
between the topic and vehicle of a metaphor as a proxy for
2.2. Corpora and training
To test whether the processing costs of metaphors
changed as a function of epoch, we collected corpora
from the 19th century and from the 21st century. We
also included diferent textual genres (literary vs.
nonliterary) of the corpora, to examine whether the dificulty
of the figurative expression is modulated by the
stylistic features of diferent types of language. Following
previous work [34], the corpora were built so as to be
representative of the language to which speakers of the
two epochs were exposed, and specifically by combining
literary, nonfiction, and journalistic language for the 19th
century, and literary and web language (which includes
sections of newspapers, blogs, and other text types that
can be found on the Internet) for the 21st century.
Specifically, we trained four diachronic vector space models on
four corpora:
• 19th-century literary corpus (32M tokens),
consisting of a collection of literary texts (both
narratives and poetry) retrieved from the Gutenberg
project (gutenberg.org);
• a 19th-century nonliterary corpus (25M tokens),
consisting of nonliterary texts, such as magazines
or scientific essays, from the same online resource
(gutenberg.org)
• a 21st-century literary corpus (16M tokens),
collected from literary texts available on the web,
employed without violating the “fair use”
principle of copyright law;
• a 21st-century nonliterary corpus (46M tokens),
collected from portions of the UMBC web- Base
corpus [35].
sparsely distributed have a lower density. It is
computed as the mean cosine similarity between
the target word and its 500 closest neighbors
(standard size from previous work, see [
        <xref ref-type="bibr" rid="ref14">38</xref>
        ]).
• Vector coherence (VC): a measure of the stability
of a word’s meaning, computed as the cosine
similarity between the target word at 1 the target
word at 2. Words with a high vector coherence
are considered to have stable meaning through
time, while a low vector coherence means that
the word’s meaning has changed.
• Word frequency (WF): computed as the logarithm
of the frequency of the target word in the
reference corpus.
      </p>
      <sec id="sec-1-1">
        <title>Each measure was collected for all the temporal slices,</title>
        <p>
          To train aligned temporal vector space models, we fol- extracted from the temporal vector space models (CS,
lowed the procedure by Di Carlo et al. [
          <xref ref-type="bibr" rid="ref10">10</xref>
          ]. The TWEC SND, and VC) or corpora (WF). To analyze how the
unmodel is implemented on top of a Continuous Bag of derstanding of metaphors changed over time and if it was
Words (CBOW) architecture [36]. The TWEC model ex- afected by genre and word-level variables, we fitted a
ploits the double representation learned by the CBOW set of Linear Mixed Models (LMMs) using the R package
model: the target matrix and the context matrix. First, a lme4 [
          <xref ref-type="bibr" rid="ref15">39</xref>
          ]. The two metaphorical structures were treated
model, the so-called “compass”, is trained on the whole separately, fitting distinct models for ‘A is B’ and ‘A of B’
corpus, creating time-independent word embeddings. metaphors.
        </p>
        <p>
          The context matrix of the compass is then maintained
ifxed to train on each corpus a time- and genre-specific The linear mixed model considers CS as dependent
target matrix from which we derive the temporal word variable and the interaction between epoch and genre
embeddings. The four sets of embeddings obtained for and word-level variables as predictors. In all models
the four corpora will represent the meaning of words Items (metaphors) were added as random variables. The
in each time slice for the two genres. To validate our resulting formula was:
models, following previous studies [
          <xref ref-type="bibr" rid="ref2">2</xref>
          ], we computed the lmer(cosine ∼ epoch * genre * (VC-topic + VC-vehicle + SND-topic + SND-vehicle +
synchronic (within time period) accuracy of each vec- WF-topic + WF-vehicle) + (1|Item).
tor space model against the MEN dataset [37], which Alpha level was set at .05.
contains 3,000 pairs of words together with a semantic
similarity score provided by humans. Finally, we tested 3. Results
whether our measure of metaphor dificulty (cosine
similarity between topic and vehicle) correlated with the
measure of dificulty in Katz et al. [16] dataset.
        </p>
      </sec>
      <sec id="sec-1-2">
        <title>First, to test the validity of the meaning representation in</title>
        <p>
          the vector space models, we correlated the human scores
of relatedness and the semantic similarity derived from
2.3. Measures of interest and analyses our word embedding for each pair of words in the MEN
dataset [37] (Table 1). These results show strong
correFor each metaphor, we collected four measures of interest, lations, comparable to the results obtained by Hamilton
at the metaphor- and word-level. et al. (2016) [
          <xref ref-type="bibr" rid="ref2">2</xref>
          ], indicating that the models accurately
mimic humans’ representation of meaning (i.e., they have
a good synchronic accuracy).
• Cosine similarity (CS): the similarity between the
two terms of the metaphor (topic and vehicle). It
is computed as the cosine of the angle between
the vectorial representations of the two words.
        </p>
        <p>CS is here considered as a proxy value of dificulty
of the metaphors.</p>
        <p>19th Literary 19th Nonliterary 21st Literary 21st Nonliterary</p>
        <p>.55 .58 .61 .59</p>
      </sec>
      <sec id="sec-1-3">
        <title>Secondly, we tested whether cosine similarity between the two terms of a metaphor correlated with the measure</title>
        <p>of dificulty from the dataset by Katz et al. [ 16].
Results showed a moderate correlation (r(26) = .49, p &lt; .05):
metaphors with higher semantic similarity between topic
and vehicle were rated with lower values of dificulty by
participants, coherently with previous studies.</p>
      </sec>
      <sec id="sec-1-4">
        <title>Thirdly, we explored whether the change in the se</title>
        <p>mantic similarity between the topics and the vehicles
of literary metaphors is driven by the interaction
between the Epoch, Genre and single-word variables. The
results of our predictors of interest are reported below.</p>
        <p>Concerning the ‘A of B’ metaphors’ mixed model,
results showed a main efect of genre (  = 0.81, t = 2.44, p
= .01) and a significant three-way interaction between
epoch, genre and vector coherence, both of the topic (
= 0.34, t = 2.018, p = .04) and of the vehicle ( = -1.715, t
= -4.954, p &lt; .001). These results indicate that the cosine
similarity of literary metaphors’ terms did not change
over time, but it changed as a function of textual genres,
resulting in greater dificulty (lower cosine similarity) in
nonliterary texts than in literary (Figure 1). As shown by
the three-way interaction between Epoch and Genre and
the single-word variables in Figure 2, the efect of VC
acted diferently in the two time points and in the two
genres. VC of the vehicle did not afect CS in literary and
non- literary texts in the past; conversely, more stable
vehicles significantly lowered CS in present literary texts
and in- creased CS in present nonliterary texts. A similar
trend can be observed for VC of the topic, where its
stability did not afect CS in the past, regardless of the literary
genres. Conversely, stability of the topic contributed to
significantly increase CS in present literary texts, but less
so in nonliterary texts.</p>
        <p>For ‘A is B’, the model revealed a significant three-way
interaction between epoch, genre, and the frequency of
the vehicle ( = 0.06, t = 2.077, p = .04), but no main efects.
The efect of WF of the vehicle showed diferent patterns
in the two time points and in the two genres (Figure
3): while WF of the vehicle did not afect CS in literary
texts both in the past and in the present, more frequent
vehicles significantly increased CS in past nonliterary
texts and lowered CS in present nonliterary texts.</p>
      </sec>
    </sec>
    <sec id="sec-2">
      <title>4. Discussion</title>
      <sec id="sec-2-1">
        <title>In this proof-of-concept study, we characterized the tem</title>
        <p>
          poral dynamics of a set of English literary metaphors to
understand whether their processing costs changed over
time. We also explored if this change was afected by the
genre of the texts, as well as by the semantic properties
of the constituting elements of the metaphors (topic and
vehicle). By leveraging on the diachronic applications of
distributional semantics and extending a method already
applied to the study of Italian literary metaphors [28],
we created a series of time-locked semantic
representations of 139 English metaphors, from which we derived
a measure of the cosine similarity between their terms
(CS), taken as a proxy of their dificulty, together with
semantic neighborhood density (SND), stability over time
(VC), and, from four diachronic corpora, frequency (WF)
of their topics and vehicles.
when found in literary contexts, compared to when
encountered in nonliterary texts. Hence, the dificulty of
these metaphors is sensitive to the style of the text in
which metaphors are found: when read in a text that has
a literary style and aesthetic intent, the metaphor is less
striking than the same metaphor in a nonliterary text.
Moreover, we found a strong efect of the stability of the
meaning of the vehicle in interaction with epoch and
genre. This suggests that ‘A of B’ metaphors with more
unstable vehicles are perceived as less dificult than ‘A of
B’ metaphors with vehicles whose meanings remained
stable over time. We interpreted this result in light of
Traugott’s [
          <xref ref-type="bibr" rid="ref17">41</xref>
          ] theory of metaphorization, according to
which the metaphorical use of a word can become one of
its stable meanings. In the context of the present study,
words that changed the most could have done so by
incorporating meanings derived from their metaphorical uses.
As a result, when these unstable and broadened vehicles
are used, metaphors appear less dificult. The reader does
not need to broaden the concept expressed by the
vehicle to interpret the metaphor, because the metaphorical
nuances have entered the standard meaning of the word.
From a qualitative observation of the data, we can notice,
for instance, that a metaphor such as “Wave of horror”,
where the vehicle wave incorporated the meaning of
‘sudden increase in a particular phenomenon’, is perceived as
less metaphorical than “Clouds of doubt”, whose vehicle
clouds has maintained its original meaning.
        </p>
      </sec>
      <sec id="sec-2-2">
        <title>For ‘A is B’ metaphors, instead, the statistical model</title>
        <p>
          Results showed no efect of epoch for either ‘A is highlighted an efect of the frequency of the vehicle in
B’ or ‘A of B’ literary metaphors. Thus, no noticeable interaction with epoch and genre. In nonliterary texts,
change in CS over time was revealed, suggesting that the perceived dificulty of ‘A is B’ metaphors difered as
these metaphors come with similar processing costs for a function of the WF of their vehicle, to the point that
contemporary readers and for readers of the epoch in metaphors showed opposite patterns in the past and in
which the metaphors were created. The absence of an the present: in the past, the less frequent the vehicle,
efect of epoch can be better understood by consider- the more metaphorical the whole metaphorical
expresing the historical evolution of the English language, and sion; in the present, the less frequent the vehicle, the less
specifically its early standardization. As stated by Wyld metaphorical the metaphor. The pattern found in the
[
          <xref ref-type="bibr" rid="ref16">40</xref>
          ], literary writing as early as the 18th century was 19th-century space model is in line with previous studies
considered ‘English of our own age in all its essentials’. [
          <xref ref-type="bibr" rid="ref18">42</xref>
          ] that found that metaphors with less frequent vehicles
In line with this consideration, our results point to the are regarded as more metaphorical than those with highly
stability of the main stylistic features of the English lan- frequent vehicles, indicating that the most metaphorical
guage in the last two centuries, including those related metaphors are those in which the vehicle communicates
to metaphors. something new about the topic. Going back to Hopkins’
metaphor "The wind is a wrestler", the vehicle wrestler,
        </p>
        <p>While literary metaphors are not processed diferently as a particularly low frequency word in the 19th century,
based on the epoch, the influence of textual genre is was indeed communicating something new about the
noticeable. This factor emerged both as a main efect topic wind. As such, the metaphors might have been
perand in diferent interaction patterns with single-word ceived as more dificult and “more metaphorical”, leading
variables, varying according to the type of metaphor. to the creation of a new concept. The very same metaphor
is nowadays perceived diferently, because the frequency</p>
        <p>For ‘A of B’ metaphors, results revealed that the dif- of the vehicle has changed: wrestler has become more
ifculty of these metaphors changed as a function of the frequent, and the whole expression has lost some of its
genre. In particular, they are perceived as less dificult metaphoricity for the 21st-century readers.</p>
      </sec>
    </sec>
    <sec id="sec-3">
      <title>7. Acknowledgment</title>
      <p>Overall, our results suggest that for the English
language, metaphor processing costs are not afected by the
temporal distance between the creation of metaphors, This work received support from the European
Rewhich occurred in the 19th century, and their processing search Council under the EU’s Horizon Europe program,
by today’s readers. Instead, the key factor modulating ERC Consolidator Grant “PROcessing MEtaphors:
Neumetaphor processing costs seems to be the textual genre rochronometry, Acquisition and Decay, PROMENADE”
in which they appear. This modulation, however, occur [101045733]. The content of this article is the sole
responto a diferent extent depending on the syntactic structure sibility of the authors. The European Commission or its
of the metaphors and in interaction with single word mea- services cannot be held responsible for any use that may
sures. Indeed, we observe that in defining what drives be made of the information it contains.
the dificulty of metaphors, diferent patterns emerged
for the ‘A of B’ and ‘A is B’ structures. While for the for- References
mer, in addition to the main efect of genre, we found the
efect of vector coherence in interaction with epoch and
genre, for the latter the diachronic evolution of metaphor
processing costs is related to the interaction of word
frequency with epoch and genre.</p>
      <p>
        While these diferences might reflect genuine efects of
the syntactic structure and how it impacts metaphorical
predication [
        <xref ref-type="bibr" rid="ref19 ref20 ref21">43, 44, 45</xref>
        ], we must acknowledge that the
numerosity of the two sets of items varies and this might
obscure some of the efects in the less represented type
(A is B). Future studies are needed to further explore the
whole range of diachronic changes in processing related
to structural diferences.
      </p>
      <p>In conclusion, this proof-of-concept study proposed an
adaptation from Italian to English of a method
employing temporal word embeddings to study the evolution of
metaphors. Thanks to this approach, we could elucidate
that the processing costs of English literary metaphors
is stable over time (diferently from Italian) but is
dynamically afected by stylistic features of texts and by
single-word measures. The proposed method seems to
be sensitive to the specificities of the language under
investigation, supporting its crosslinguistic applicability.</p>
    </sec>
    <sec id="sec-4">
      <title>5. Ethic statement</title>
      <sec id="sec-4-1">
        <title>The work aims to use computational tools for the study</title>
        <p>of literature, thus enhancing the literary heritage with
innovative methods that can provide insights for scholars
from a wide range of disciplines. We are aware, however,
that the corpora used are not representative of the entire
spectrum of varieties of English, but of educated, Western
English. Hence, our results may not coincide with the
general evolution of the language but provide a partial
view of it.</p>
      </sec>
    </sec>
    <sec id="sec-5">
      <title>6. Data availability</title>
      <p>Temporal vector space models and metaphor datasets
used in the study are available at https://osf.io/j8bd7/
?view_only=4cd623d5622b4ed0bd1624c42af0f40$.
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