=Paper=
{{Paper
|id=Vol-3133/paper01
|storemode=property
|title=Between Cows and Capitalism: Measuring the Abstractness of Historical Parliamentary Speeches
|pdfUrl=https://ceur-ws.org/Vol-3133/paper01.pdf
|volume=Vol-3133
|authors=Ruben Ros
|dblpUrl=https://dblp.org/rec/conf/dhn/Ros22
}}
==Between Cows and Capitalism: Measuring the Abstractness of Historical Parliamentary Speeches==
https://ceur-ws.org/Vol-3133/paper01.pdf
Between Cows and Capitalism: Measuring the
Abstractness of Historical Parliamentary Speeches
Ruben Ros1
1
Centre for Contemporary and Digital History, Luxembourg University
Abstract
This paper proposes a method for estimating the abstractness of Dutch historical parliamentary speeches.
The degree to which language is abstract or concrete has gained significant attention in the field of
cognitive science and linguistics. The paper uses proposed computational approaches to abstractness in
these fields to model and explore variation in historical parliamentary data in the context of a larger
project on technocratic rhetoric. By first scoring individual terms based on the vector averaging of an
annotated set of abstract and concrete terms the abstractness of speech paragraphs is estimated. The
paper shows that this information captures differences in rhetorical style that remain difficult to identify
with established text mining methods. Abstractness sheds light not only on what is said in parliament,
but how it is said, hereby bridging semantic and stylistic analysis in digital history.
Keywords
abstractness, political rhetoric, parliamentary history, conceptual history
1. Introduction
Parliamentary debate features both abstract and concrete forms of language. Abstract ide-
ological rhetoric alternates with practical deliberation over detailed policy measures, and
jargon-saturated legislative language appears alongside concrete calls to action by parliamen-
tary representatives. This dimension of abstractness - defined as the opposite of concreteness -
ties in with the so-called ”scientization” of democracy and parliament [1, 2, 3]. In the eyes of for
example Jü rgen Habermas,the growing role of the sciences in politics leads to the replacement
of democratic deliberation with technocratic calculation [4]. Parliamentary debate becomes
a form of technical discussion under the pressure of waning ideologies, increasingly complex
legislation and the growing dominance of expert knowledge and institutions [5].
There are several indicators that point to abstractness as a linguistic expression of these
historical processes of scientization and technocratization. Technocratic rhetoric is considered
in the literature as drawing heavily on abstract (bureaucratic and managerial) language and
historical studies point at the rise of more technical debates that are saturated with legislative
jargon [6, 7, 8]. At the same time, technocrats themselves often self-identify as “men of practice”,
shying away from (abstract) ideological politics, and instead focusing on (concrete) depoliticized
technical management [9, 10].
Digital Parliamentary Data in Action (DiPaDA 2022) workshop, Uppsala, Sweden, March 15, 2022.
Envelope-Open ruben.ros@uni.lu (R. Ros)
Orcid 0000-0002-5303-2861 (R. Ros)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
9
This paper describes a method for measuring abstractness in parliamentary debate. Drawing
on work in computational linguistics, it models abstractness in parliamentary speech paragraphs
and tries to use these abstractness signals to find forms of language variation that map onto the
issue of technocratization and technocraticness.
Abstraction has recently gained significant traction in the fields of psychology and cognitive
science. [11, 12]. Understood as a core component of human cognition, it is generally studied
through language. Our capacity for abstract reasoning reflects in the concepts we use. Con-
sequentially, the topic of abstraction has been taken up in (computational) linguistics, where
several contributions have tried to estimate the abstractness of individual words alongside other
metrics, such as age of acquisition and imagery [13, 14]. This, in turn, has led to applications
of abstractness-oriented work in sociolinguistics. In this field, lexical abstractness has been
used in research into linguistic intergroup bias. Borden et al. [15], for example, show an
increase in abstract language in times of crisis, while Dragojevic et al. [16] use abstractness to
demonstrate intersubjectivity in news coverage. These examples show that the linguistic study
of abstractness is relevant to other fields that use language difference as the basis for humanistic
research. Variation in lexical abstractness on the level of groups, genres or time periods can be
used as a basis for studying history, as the recent work in the field of Computational Literary
Studies has shown[17].
The paper puts forward a method for estimating parliamentary speech abstractness. It shows
how abstractness can be estimated on the word level using word embeddings and an annotated
set of terms. The paper explains the way in which word-level estimates are subsequently
used to estimate parliamentary speech abstractness. It also features a brief analytical part that
concentrates on (explanations for) speaker-level differences, functioning as an example of the
type of analysis that could be performed with the method outlined in the paper. Lastly, several
shortcomings and venues for further research are discussed.
2. Data
The paper uses a corpus of Dutch (Lower House) parliamentary debates from the period between
1917 and 1986 [18]. The abstractness estimation method is performed on the full corpus. The
analytical section focuses on the period between 1917-1923. In total, the data contains 194415
paragraphs, a total of 13.979.075 tokens and 194.415 types. The text data was preprocessed by
lowercasing tokens, removing tokens with multiple digits and removing stop words. For the
topic modelling, a lemmatized version of the text data was used.
The data contains rich metadata that connect speech paragraphs to speakers, parties, roles
(government minister, MP or guest speaker) and dates. This enables the investigation of various
potential explanations for abstract language.
The quality of Optical Character Recognition, often a restraining factor in text analysis, is hard
to measure, since no accuracy scores are available. Nevertheless, the significant improvement
observed in the text that has been subjected to OCR post-correction using PICCL (compared to
the original digitized proceedings) indicates a relatively high quality [19]. Type-token ratio’s,
used as a crude measure for OCR quality, suggest significant improvement over the course of
the century [20].
10
3. Method
3.1. Estimating Word Abstractness
Previous work in computational linguistics uses manually scored vocabularies to create ab-
stractness classification models [21, 22, 23]. The lexical context of known abstract and concrete
terms is used to predict the abstractness of unseen terms, based on the assumption that abstract
and concrete terms have similar contexts [24, 25]. The resulting word-level scores are then used
to score larger units of text by means of counting (an extended list of) paradigmatic abstract
and concrete terms in a text, or averaging the word scores in the text [17].
The weakness of this method is the labour-intensive process of creating annotated data.
However, several annotated datasets are available. This paper relies on an extensive list of word
concreteness scores produced in a study by Brysbaert et al. (hereafter BBT) [26]. This data
contains concreteness ratings for over 30.000 Dutch words rated through the Amazon Mechanical
Turk platform. Compared to other sets, the BBT set covers a high number of words, is scored
by a multiple annotators is and one of the few that focuses on Dutch and does not require
translation [27, 28]. According to Brysbaert et al., annotators were asked to rate a concept’s
concreteness on a five point scale based on the extent to which a word can be experienced
directly through one of the senses.
We use the BBT data to score individual terms in a series of steps. First, we train word
embedding models on the parliamentary proceedings. We use the Skipgram Negative Sampling
(SGNS) variant of the Word2Vec algorithm implemented in the popular Gensim module in
Python [29]. Based on the work by Rabinovich et al. [25], we use a relatively small window of
three (n-grams) in the training. Moreover, words with a frequency of lower than one hundred
are excluded. We are aware that this potentially excludes terms of interest, but in light of the
volatility of low-frequency terms in the embedding models, we deem this choice justified. We
train models on both the full period and six-year time slices to take historical language variation
into account. Because the slices contain relatively little data in early time periods and individual
word vectors tend to vary in different models trained on the same data, we use bootstrapped
training and take the average word vectors over ten models for every term in all subsequent
steps [30].
As shown in earlier studies, the word embedding models allow the construction of an average
abstractness vector [17]. This is done by averaging the vectors of a list of known abstract
and concrete terms. For this study, we use only adjectives and nouns. Experiments show
that including other word forms, such as verbs and conjunctions, hinder the effectivity and
explainability of our method. Another important factor is the number of seed terms used for
training. It is not clear from the outset how many of the top abstract and concrete terms from
the BBT-data should be used. Figure 1 shows the correlation between annotated and estimated
scores plotted against different numbers of seed terms. This pattern shows that the correlation
rapidly increases when taking a higher number of terms, but eventually decreases when we
take too many. After around 480 terms, the correlation stabilizes, meaning that adding more
seed terms to the average vector does not amount to a higher correlation between estimated
and annotated abstractness scores. Based on this distribution, we take 480 abstract and concrete
nouns and adjectives (meaning 220 concrete nouns, 220 concrete adjectives, 220 abstract nouns
11
and 220 abstract adjectives) as the basis for the averaging.
0.54
Pearson Correlation Coefficient
0.55
(Bootstrapped) 0.56
0.57
0.58
0.59
0.60
0.61
0 250 500 750 1000 1250 1500
Number of Abstract and Concrete
Seed Terms (5 - 1500)
Figure 1: Bootstrapped Pearson’s correlation coefficients against increasing number of seed terms.
Vertical lines show stabilization at around 480 seed terms and a decline in correlation coefficients after
around 1200 terms.
To estimate term abstractness, we calculate the mean vectors of the abstract and concrete
terms and subtracted the latter from the former. The cosine similarity of a word vector to this
average abstractness vector is then used as a measure for abstractness. Figure 2 shows the
individual (estimated) word abstractness scores (derived from the bootstrapped models trained
on the full period) plotted against the BBT-annotations. Since this means that abstractness is
offsetted against concreteness, the correlation is negative. The figure shows a relatively broad
scatter and a moderate correlation of around -0.6. This means that there remains a group of
words that is annotated as concrete, while estimated as abstract and vice versa. Looking at these
words points at the issue of sense ambiguity. For example, in Table 1, that shows the top most
concrete and abstract estimated terms in the period between 1918 and 1923, the verb “stroken”
(“match”) appears as a highly abstract term. In the BBT-annotations, the term is likely to have
been understood as a highly concrete noun (“strips”). However, in light of the observed limited
number of these ambiguous terms, we believe that this problem does not fundamentally hinder
our method.
3.2. Estimating Text Abstractness
Based on the word scores generated with the seed term lists and vector averaging we calculated
abstractness scores for parliamentary speech paragraphs. Looking at overall speech abstractness
proved unworkable, since abstract and concrete words cancel each other out, leading to little
variation. We considered scoring texts on the level of sentences. However, the quality of the
12
400
200
0 1 2 3 4 5
0.7 0.7
Normalized Estimated Abstractness
pearsonr = -0.602
0.6 0.6
0.5 0.5
0.4 0.4
0.3 0.3
0.2 0.2
0.1 0.1
0.0 0.0
1 2 3 4 5 0 250
Annotated Concreteness (Brysbaert et al.)
Figure 2: Scatterplot of all words in the model trained on the full period (1917-1986) that shows the
correlation between estimates and annotations (Pearson’s R = -0.602) and the distributions of the
estimated abstractness scores and annotated concreteness scores.
Abstract Concrete
1 code of conduct (gedragslijn) sheep (schaap)
2 constitutional (staatsrechtelijk) horse (paard)
3 desideratum (desideratum) koe (koe)
4 unanimity (eenstemmigheid) closet (kast)
5 match (stroken) wit (wit)
6 character (karakter) boot (boot)
7 point of departure (uitgangspunt) wagon (wagon)
8 rationale (grondgedachte) indigestion (maag)
9 effect (uitwerking) chest (kist)
10 constitutional (grondwettelijk) neighbourhood (buurt)
Table 1
Top 10 translated most abstract and concrete terms in the period between 1917 and 1923.
sentence tokenization turned out problematic and currently the quality of the OCR does not
permit reliable improvement. In future work we hope to improve the sentence tokenization in
order to get a more fine-grained picture of the abstractness dynamics within a speech. In this
paper we use paragraph-level abstractness. We consider this an effective intermediate level in
between sentences and speeches. Paragraph abstractness scores are calculated by taking the
13
median of all individual word abstractness scores in the paragraph. Because the scores in the
paragraphs are not distributed normally, we take the median instead of the mean.
3.3. Improving Abstractness Signals
The method described so far produces abstractness scores on the level of speech paragraphs.
Given the availability of rich metadata on speakers, parties, roles and dates, this offers the
opportunity for various types of historical analysis. Temporal trends, differences between
groups, or abstractness variations in specific debates could be studied with the scores we have
extracted. However, upon looking closely at specific paragraphs and debates, we found that
two additional steps were required to improve abstractness signals.
First, close reading abstract paragraphs revealed that many of them dealt with parliamentary
procedure. This comes as no surprise, given the “explicitly procedural character of parliamentary
politics” described by Kari Palonen and others [31, 32]. The addressing of members, the filing of
motions and amendments and standardized types of debates (such as questions to the minister or
specialized budget talks) is structured by rules and customs that produce highly stratified forms
of language. It appears that this procedural language consists mostly of abstract terms. To verify
this suspicion, we trained a topic model with 75 topics using Mallet. The paragraph-level topic
distributions could then be compared with the paragraph abstractness scores. Figure 3 shows
the fifteen topics that have the highest negative and positive correlation with abstractness. The
distributions of the topics at the top of the figure have a correlation coefficient of around -0.2,
meaning that they (weakly) correlate with concreteness. The bottom topics have a correlation
of around 0.2, demonstrating some correlation with abstractness. Their top terms clarify these
patterns. It makes sense that topics about agriculture (“potato, farmer, stock”) housing (“house,
building, build”) correlate negatively with abstractness. The figure shows that many of the topics
that correlate positively with abstractness are procedural topics. They pertain to questions,
motion, legislative jargon and the more general language of opposition. Topics that show a
(moderate) correlation with concreteness are often about specific policy areas, such as housing,
defense or agriculture.
This topic abstractness was a first surprising finding in our research. In the subsequent
analysis, we used this information to filter out procedural language. By classifying topics as
“semantic” or “procedural” (based on their top terms), we kept only those paragraphs where
the combined topic probability for procedural topics was lower than 0.5. In this way, a better
signal was acquired because the “abstractness bias” encapsulated in procedural language was
largely removed. This is not to say that procedural language cannot be used for our analysis,
or plays no role in the overall abstractness of debates, but for this first exploratory dive in the
abstractness data, we prefered to focus on speech paragraphs that dealt with specific policy
debates.
The second step in improving the signal was the calculation of the distance between the
average paragraph abstractness and the average session abstractness. This follows from the
observed link between topics and abstractness. Because sessions are often dedicated to only a
small number of topics, local dynamics are likely to be obscured by the general abstractness of
the session. For this reason, we calculated the distance from the average session abstractness
and the paragraph abstractness.
14
potato farmer stock quantity distribution bread meat get minister fat
price cent pay profit article expensive product industry export trade
so say come go good man know show get case
man day man service main story come food let soldier
cap figure number of years amounts very average large cost
gentlemen monte loren snoeck ver vote twist rutgers henkemans duymaer
ter laan gentlemen mr mrs schaper temple groenweg albarda gerhard
hour worker labor work day labor law company week perform
military army officer service man minister war non-commissioned officer twist
house building build house rent construction new rent commission rent
ship dutch country german germany port netherlands industry england pilot
municipality city amsterdam large countryside place small country province rotterdam
Topic Terms (10)
million budget amount expenditure year minister post money cutback costs
land land farmer agriculture owner farmer small property hunting farm
water work improvement mesh plan zuiderzee dyke channel bridge port
government chamber wise say give action crit fact such so
appeal decision council law judge power judicial decision jurisdiction law
chamber government people constitution majority parliament staten-general king crown constitutional
between difference exist different relate relationship come whole place group
chairman mr point matter bring to attention consideration general speak
committee advice report investigation case council minister appoint member state
minister ask question ask questions government matter answer chamber come
motion mr chamber amendment vote adopt proposal give vote minister
law measure regulation board decree regulate generals royal statutory minister
amendment chairman mr committee mr rapporteurs word member explanation propose
keep ask position question stand say take into account justice
objection minister come to bring system good existence new system
draft bill draft chamber law amendment government come bring proposal
law art article provision amendment give stand member constitution case
0.2 0.1 0.0 0.1 0.2
Correlation with Paragraph Abstractness
Figure 3: Pearson correlation coefficients between topic distributions and abstractness scores for words
in the period between 1917 and 1923. The top 15 most negative and positively correlating topics are
shown. The (translated) terms for procedural topics are colored red. The “semantic” topics (those not
pertaining to parliamentary procedure but to specific policy areas or issues) are colored blue.
Figure 4 shows the aforementioned distances to the session mean averaged on the speaker
level plotted against the general mean abstractness per speaker. The first noteworthy feature
is the strong linear correlation. Speakers with a high average abstractness are also relatively
abstract compared to the overall abstractness of the sessions in which they speak. This tells
us that speaker-level abstractness cannot be explained by the (topic) abstractness of a session
alone. General abstractness comes with local abstractness, meaning that even when we take
into account the fact that some sessions are more abstract than others, speaker level differences
persist.
4. Speaker Abstractness in Context
With abstractness scores now available on the paragraph-level and the signals improved with
the topic filtering and session-paragraph distances, parliamentary abstractness can be analyzed
on several levels. The data allows an analysis of abstractness at the level of parties or gender,
or can be used to track abstractness through time. In this section, we take a brief look at
speaker-level abstractness as an example of the type of analysis that can be performed with this
method. The section focuses on the time period between 1917 and 1923. We do so because this
period is currently under study in the broader project, and we thus know most of this period’s
political context.
15
Average Paragraph Deviation from Session Abstractness
0.01 1. baron van Wijnbergen
2. Rutgers
3. van Schaik
4. Ossendorp 2
0.00 5. Troelstra 3 5 1 SDAP
4 CHU
ARP
0.01 ALGEMEENEBOND
PB
VRIJLIBERALEN
0.02 VDB
6. Brautigam SOPA
8 BCS
7. Schaper
8. Sannes LU
0.03 9. Weitkamp CDP
10. Braat NEUTRALEFRACTIE
7 VB
0.04 6 CPN
CSP
0.05 9
10
0.06 0.42 0.44 0.46 0.48 0.50 0.52 0.54
Average Paragraph Abstractness
Figure 4: Speaker-level mean paragraph abstractness (x-axis) plotted against the mean deviation from
session abstractness (y-axis) on the level of individual speakers in the period between 1917 and 1923.
Only speakers with > 250 paragraphs are considered. The five most abstract and concrete speakers are
annotated in the plot. Colors indicate party affiliation.
The period between 1917 and 1923 is marked by several fundamental transformations in Dutch
politics. The 1917 and 1919 constitutional reforms led to universal suffrage and proportional
representation. This left its mark on the party political divisions in parliament. Socialists and
confessional parties gained ground, while the power of the formerly dominant liberals declined.
Parliamentary debate in the period revolved to a considerable extend around these democratic
issues. It also included frequent deliberation over the new areas in which the post-First World
War administration was now active: education, welfare and labour [33].
Figure 4 shows the average abstractness and the average distance from the session mean for
all speakers with more than 250 paragraphs. Alongside the points, the names of the top most
concrete and abstract speakers are included in the plot. By investigating explanations for the
appearance of specifically these names, more becomes clear about what exactly our method
is capturing. The most abstract speakers are Van Wijnbergen (Catholic), Rutgers (Protestant),
Van Schaik (Catholic), Ossendorp and Troelstra (Socialist). The most concrete speakers are
Brautigam, Schaper, Sannes (Socialist), Weitkamp (Protestant) and Braat (Agricultural League).
A first important observation when looking at these speakers in their historical context
is the persistent influence of topics. In the previous section, it was shown how procedural
language impacts abstractness ratings. Non-procedural topics, however, also play a large role
16
in the constitution of the speaker abstractness average. In the case of Van Wijnbergen and
Ossendorp, their frequent involvement in debates about education, a generally abstract topic,
seems to explain much of their relatively high abstractness averages. Similarly, the involvement
of Brautigam and Sannes in debates about food distribution, housing and shipping, and the
involvement of Weitkamp and Braat in agricultural debates shows the close link between what
policy area is discussed by a member and how concrete or abstract his (parliament hosted
mostly men in this period) average score is. The focus of specific speakers on a limited number
of policy areas thus logically explains their high or low average scores.
However, thematic preferences measured through topics, do not explain all individual differ-
ences. In fact, a closer look at the topics and individual speeches (subjected to close reading)
indicates that the abstractness scores also go beyond thematic preference. Looking at the
speeches of the ten members mentioned in Figure 4 and taking into account their individual
background and reputation suggests that their abstract and concrete averages might also stem
rhetorical style. Troelstra, the famous socialist leader, was known for his ideological style. His
speeches are hardly about housing or agriculture, but about capitalism and revolution, and
when he discusses concrete matters, he still does so in a relatively abstract way. Similarly,
Victor Rutgers, the leader of the protestant A.R.P. (Anti-Revolutionary Party) seems to talk in a
relatively abstract way. Even his most concrete paragraphs display a large number of abstract
terms. In a similar fashion, Weitkamp and Braat, whose concreteness mainly stems from their
involvement in agricultural debates (Braat was the only representative of the populist“Agricul-
tural League”), also seem to speak in concrete ways in other areas. Both speakers were known
as atypical members, not conforming to the parliamentary etiquette and even maintaining a
way of speaking that was considered rude by their colleagues. Especially the latter, nicknamed
“Boer Braat” (“Farmer Braat”) was considered a disgrace to parliamentary dignity due to his
“un-parliamentary” manners [34]. This potential biographical explanation of concrete style also
brings us to the overall concrete socialists, whose most concrete members (Sannes, Schaper and
Brautigam) all had a working-class background could have produced a rhetorical style that is
clearly different from aristocratic conservatives and liberals, and hence more concrete. Future
work should include a more comprehensive and structure examination of these individual
factors, but the result of this first exploration already show interesting similarities that cut
through party political divisions.
Another way of substantiating this rhetorical dimension of abstractness is to look at abstract-
ness averages on the speaker-topic level. By calculating the mean abstractness for every topic
and for every speaker, and subsequently ranking speaker topics based on their abstractness
we observe that the general topic abstractness differed significantly from speaker-level topic
abstractness. In other words, housing is a concrete topic overall, but in the case of Schaper
it is highly abstract. Similarly, a topic on party politics (“party, politics, cabinet, rightwing”)
is generally abstract, but in the case of Weitkamp and Braat highly concrete. This is a final
indication that our method registers not only variation in the things speakers talked about, but
also differences in the way they spoke.
Overall, it remains difficult to disentangle and balance the various potential explanations for
speaker abstractness. However, the discussion of the most abstract and concrete members shows
that the method proposed in this paper is able to point at new axis of difference. Abstractness
cuts through the party political lines that often form the basis for historical inquiry. The
17
fact that we find socialists among conservative protestants as abstract speakers, and other
socialists among farmer representatives shows that much remains unknown when it comes to
parliamentary rhetoric.
5. Discussion
All in all, our method is able to capture difference in abstractness on the level of parliamentary
speech paragraphs. However, our research has brought forward several issues that should
be addressed in further research. First, the dependency on individual word scores inevitably
leads to the issue of semantic change. Words change meaning, and so does their abstractness,
something which hinders applications of the method to historical data. However, the paper has
shown that the embedding procedure, based on a robust number of seed terms, is unexpectedly
good in identifying corpus-specific abstract and concrete terms. Moreover, comparing terms
between time periods can help in identifying the historically changing abstractness of terms.
This issue also relates to word sense and figurative speech. The word “bank” has multiple
senses and the level of abstractness for “ocean bank” and “savings bank” is large. In the current
method, multi-sense terms are not tackled. Here, more recent embedding methods that are able
to produce contextualized embeddings will be of great value.
Another pressing issue is the unit of analysis: the speech (paragraph). A first obvious
drawback is the fact that internal variation is not seen by averaging word-abstractness in
paragraphs. Speech paragraphs with highly abstract and concrete parts go unnoticed. Measuring
lower-level linguistic units (such as sentences) might mitigate this effect.
The impact of thematic preferences, mapped through topic modelling, remains high, even
when removing procedural language. The issue of differentiating between the what and the how
of parliamentary speech remains difficult. Still, the paper has shown how several techniques can
be used to mitigate this thematic bias. This has resulted in several indications that abstractness
also tells us more about rhetorical style: the how of parliamentary debate.
Lastly, in relation to our broader concern with technocratic rhetoric it goes without saying
that there is no one-to-one correspondence between abstractness and “technocraticness”. Still,
this method greatly benefits our research. The brief excursion into speaker-level differences
shows the potential of the method in tracing unlikely familiarities and exploring stylistic
differences that complement close reading analysis of speeches and histories of political thought.
Using the differences and similarities between speakers, we were pointed at parliamentary
representatives that had gone unnoticed before. In this way, our method might not yet be
optimal for macro-level statistical analysis, but does help in contextualizing other forms of
historical research.
6. Conclusion
This paper has proposed a method for measuring abstractness in parliamentary speeches.
Based on an annotated set of words, the abstractness levels of unseen corpus-specific terms are
estimated. Subsequently, speech paragraphs are scored by taking median scores. The paper has
subsequently discussed the use of these scores in historical research and several approaches to
18
improving the abstractness signals. How abstract a member’s speeches are depends to a large
extent on their preferred themes and topics. However, individual rhetorical style also plays
a role. Balancing these complex historical factors and “variables” has proven to be difficult,
yet productive. The paper has demonstrated how methods from computational linguistics can
be leveraged in historical research and how this amounts to forms of linguistic variation that
remain hard to capture with established computational methods.
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