10.6092/issn.2532-8816/8632
Sir. Arthur Conan Doyle was an esteemed and highly experienced physician and much of his medical knowledge spreads into his literary works. In this paper, we propose to study the medical terminology in the stories of Sherlock Holmes through the combination of a mixed method of quantitative and qualitative analysis. Our approach is based on 1) the automatic extraction of medical terminology throughthetidytextR package for text analyses, 2) a terminological analysis by means of the model of terminological record designed fortheTriMED database, and 3) the study of collocations through the linguistic tool Sketch Engine. Thanks to this approach, we perform a linguistic analysis in order to evaluate different terminological aspects such as: the semantic variation due to temporal and historical factors, the difference of the context of use, the change of meaning based on the reference corpus, the variation of use depending on speakers/writers register and, finally, the relationship between terms and their collocations from the syntactic viewpoint.
text/html
2532-8816
8632
6
en
Giorgio Maria Di Nunzio
Umanistica Digitale
Qualitative analysis
(Not so) Elementary, my dear Watson! A different perspective on medical terminology
Text.Serial.Journal
https://umanisticadigitale.unibo.it/
Articles
2019-09-03
2018-10-16
2019-09-03
2020-05-19
Um. Dig.
Giorgio Maria Di Nunzio
2019/09/03
10.6092/issn.2532-8816/8632
University of Padua
2532-8816
6
Qualitative analysis
en
(Not so) Elementary, my dear Watson! A different perspective on medical terminology
https://umanisticadigitale.unibo.it/article/view/8632/9413
     Umanistica Digitale - ISSN:2532-8816 - n.6, 2019
F. Vezzani, G. M. Di Nunzio – (Not so) Elementary, My Dear Watson! A Different Perspective
on Medical Terminology
DOI: http://doi.org/10.6092/issn.2532-8816/8632


   (Not so) Elementary, My Dear Watson! A Different
          Perspective on Medical Terminology

                           Federica Vezzani - Giorgio Maria Di Nunzio

                                    Università degli Studi di Padova
                                federica.vezzani@phd.unipd.it
                                giorgiomaria.dinunzio@unipd.it




       Abstract. Sir Arthur Conan Doyle was an esteemed and highly experienced physician and
       much of his medical knowledge spreads into his literary works. In this paper, we propose
       to study the medical terminology in the stories of Sherlock Holmes through a mixed-
       method of quantitative and qualitative analysis. Our approach is based on 1) the
       automatic extraction of medical terminology through the tidytext R package for text
       analyses, 2) a terminological analysis by means of the model of terminological record
       designed for the TriMED database, and 3) the study of collocations through the linguistic
       tool Sketch Engine. Thanks to this approach, we perform a linguistic analysis in order to
       evaluate different terminological aspects such as: semantic variation due to temporal and
       historical factors, a term’s different contexts of use, change in meaning based on the
       reference corpus, variation of use depending on speakers’/writers’ register and, finally, the
       syntactic relationship between terms and their collocations.
       Sir Arthur Conan Doyle era un medico stimato e con grande esperienza e gran parte delle
       sue competenze mediche si riflettono nelle sue opere letterarie. In questo articolo, ci
       proponiamo di studiare la terminologia medica presente nelle storie di Sherlock Holmes
       attraverso la combinazione di un metodo misto di analisi quantitativa e qualitativa.
       L’approccio che proponiamo si basa 1) sull'estrazione automatica della terminologia
       medica attraverso il package tidytext R per l'analisi del testo, 2) su un'analisi
       terminologica tramite il modello di scheda terminologica progettata per il database
       TriMED e 3) sullo studio delle collocazioni attraverso lo strumento linguistico Sketch
       Engine. Grazie a questo approccio, conduciamo un'analisi linguistica al fine di valutare
       diversi aspetti terminologici come: la variazione semantica dovuta a fattori storici e
       temporali, la differenza del contesto di utilizzo di un termine, il cambiamento di
       significato di una parola in base al corpus di riferimento, la sua variazione di utilizzo a
       seconda del registro del parlante/scrivente e, infine, la relazione tra i termini e loro
       collocazioni dal punto di vista sintattico.




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     Umanistica Digitale - ISSN:2532-8816 - n.6, 2019


       Introduction

In the last two decades, researchers of different fields have started to pay attention to the
benefits deriving from the adoption, both in linguistics and in literature, of mixed approaches,
quantitative and qualitative, to the study of texts. In particular, the availability of large
quantities of electronic literary texts has increased the attractiveness of quantitative approaches
as a way of “reading” literature with benefits in terms of time and accuracy [15]. The classical
notion of “close reading” [26] is referred to the act of analyzing a small set of works upon a
deep reading and interpretation of local features and aspect of its formal structure and content.
This approach is opposed to the recent concept of “distant reading” [22] as the act of
understanding literary phenomena through the computational analysis of massive amounts of
textual data. The idea is that there are synchronic and diachronic literary features and
phenomena that are difficult to detect with traditional reading and local interpretation
methods, and which require the close examination of a huge number of texts and documents
through a computational approach. Thus, the combination of quantitative and qualitative
analyses can provide complementary insights both into the formal and rhetorical-narrative
structure and into the topics and formulation of various types of written texts.


       Corpus Stylistics Tools
In the last few years, there has been a considerable increase in studies in the new field of
research called “corpus stylistics” [28]. Corpus stylistics brings together approaches from corpus
linguistics and literary stylistics, and it is concerned with the application of corpus methods to
the analysis of literary texts by relating linguistic description with critical interpretation [20]. In
order to combine quantitative and qualitative analysis, the central linguistic tool for the analysis
of literary corpora is based on the study of concordances as a list of the occurrences of a word
extracted from a specific linguistic context [29]. The automatization of this process is provided
by software tools such as the Simple Concordance Program (SCP) 1 or the suite WordSmith
Tools.2 Moreover, it is possible to have even more refined results in a KeyWords In Context
(KWIC) format by means of domain-specific languages such as the Corpus Query Processor. 3
The development of software tools and applications which use user-friendly visualizations of
text has attracted an increasing number of researchers to corpus stylistics; these visualization
tools allow researchers to investigate the meaning of the words in context and co-text [34]. As
stated by Tognini-Bonelli [31], in order to find repeated occurrences of words, a concordance is
read “vertically” rather than horizontally, as we normally read a text. In this way, the patterns
that become visible in a concordance provide information on the meanings of words.
Furthermore, it is also possible to obtain a display of non-contiguous sequences, as with
ConcGram tool [3]. ConcGram is a software package of corpus linguistics that has been
specially designed to find all co-occurrences of words in a text or a corpus regardless of their
positional variation generated by the association of two or more words. These are called
1   http://www.textworld.com/scp/
2   https://www.lexically.net/wordsmith/
3   http://fedora.clarin-d.uni-saarland.de/teaching/Corpus_Linguistics/Tutorial_CQP_I.html


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 F. Vezzani, G. M. Di Nunzio – (Not so) Elementary, My Dear Watson! A Different Perspective
                                                                  on Medical Terminology

“concgrams”, and they are defined as “all of the permutations of constituency variation (increase in
expenditure, increase in the share of public expenditure) and positional variation (expenditure
would inevitably increase) generated by the association of two or more words”. The software
automatically finds the co-occurrences of given words: in other words, the user does not have to
set up previous search commands. ConcGram is particularly suitable for discovering the
complete phraseological profile of a text or a corpus. This software has a wide range of
applications in the fields of corpus linguistics, critical discourse analysis, speech analysis,
lexicology, lexicography, pragmatics and semantics.
The display format of a concordance may help the identification of formal schemas associated
with functions in the text; at the same time, association models can also be identified without
resorting to typical concordance visualizations, as evidenced by the various types of
measurements for location extraction or techniques for generating clusters of words, n-grams
and so on. For example, the recent GraphColl [2] tool shows that there are other types of
collocation displays compared to classical concordance lines. GraphColl is a useful tool for the
construction and exploration of links between linguistic collocations. The text in a particular
field of discourse is organized into lexical schemas, which can be visualized as networks of
words that are placed one with the other. GraphColl is a tool that builds collocation networks
starting from corpora, allowing the user to obtain important information on semantic
relationships. Finally, one of the most used and useful tools currently on the market is Sketch
Engine,4 that is a corpus manager and text analysis software developed by Lexical Computing
Limited in 2003 [18]. This tool was designed for people studying language behavior such as
lexicographers, researchers in corpus linguistics, translators or terminologists; it allows one to
conduct searches in large text collections according to complex and linguistically motivated
queries. One of its key-features is the “word sketch” function, which is a one-page summary of
the word’s grammatical and collocational behavior. It shows the word’s collocates categorized by
grammatical relations such as words that serve as an object or subject of the verb and so on.
Sketch Engine is therefore a useful instrument for text analysis in order to perform co-
occurrence analysis, term extraction or generate frequency lists which can be the basis of
qualitative-linguistic studies of the text analyzed.


       Related Works
All these tools and quantitative methods of analysis based on concordances highlight the
linguistic qualities of a text from lexico-syntactic, phraseological perspective, which can shed
light on semantic and pragmatic aspects of language use. For example, concordance tools can
generate and collect keywords of texts by comparing the frequencies of the words in the same
text with the frequencies of those words in a reference corpus. The work of Scott and Tribble
[27] illustrates a study based on keyword analysis by comparing Romeo and Juliet with a corpus
containing all of Shakespeare’s plays. Even in Culpeper [4], key words are used to analyze
Romeo and Juliet, but the author adopts a different perspective by comparing the speeches of


4   https://www.sketchengine.eu


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     Umanistica Digitale - ISSN:2532-8816 - n.6, 2019

individual characters. In this context, the work of Mahleber at al. [21] aims to study Charles
Dickens’s narrative through the development of a web application, named CLiC, in order to
bring together insights from both cognitive poetics and corpus stylistics. This tool supports the
analysis of discourse in narrative fiction with search options focused on stretches of text within
and outside quotation marks. With this kind of analysis, the authors focused on some
characteristics of the narrative of Charles Dickens regarding the use of direct discourse and its
suspensions.
Quantitative approaches are used not only to investigate larger interpretative issues like plot,
theme, genre, period or modality, but they are also frequently associated with questions of
authorship attribution and style. The study of Franzini et al. [10] fits in the context of
computational text analysis and it specifically describes an effort to automatically discern the
authorship of Jacob and Wilhelm Grimm in a corpus of correspondences. The paper describes
the impact of digitization noise on the automatic attribution of a body of letters by comparing
three different digitization outputs: (a) manual transcriptions of the original letters, (b) the
Optical Character Recognition (OCR) of a 2001 printed critical edition of the Grimm letters,
and (c) a Handwritten Text Recognition (HTR) model for the automatic transcription of the
original letters.
In the context of the problem of authorship attribution, the concept of stylometry [14], that is
the study of measurable features of literary style (such as sentence length, vocabulary richness
and various frequencies of words), has found numerous practical applications. These
applications are usually based on the hypothesis that there are some elements of personal style
that can help to detect the true author of an anonymous text. The study of Eder et al. [6]
proposed a method based on the identification of the most frequent word n-grams, using the R
programming language, in order to perform multivariate analysis to provide new insights such
as: relationships between different books by the same author, between books by different
authors or between authors differing in terms of chronology or gender. A recent contribution
in this context is the work of Tuzzi and Cortelazzo [32] looking at the case of Elena Ferrante as
the presumed pseudonym of an internationally successful Italian novelist. The authors of this
work created a reference corpus composed of 150 novels by forty different authors with the
main goal of understanding whether, amongst the authors in the corpus, there are any that can
be considered candidates for involvement in the writing of the Ferrante’s novels. The method is
based on the measurement of the degree of similarity between the novels: from these
quantitative analysis, Domenico Starnone was identified as the author who has written novels
most similar to those of Ferrante. The case of Elena Ferrante is interesting from both the
stylistic and the stylometric viewpoints, and has attracted a good number of researchers,
especially on the occasion of the Workshop “Drawing Elena Ferrante’s Profile” organized by
Cortelazzo and Tuzzi at the University of Padua.5
Another application of quantitative analysis for the study of literary texts focuses on the
vocabulary variation. Quantitative thematic analysis can trace the growth, decay, or
development of vocabulary within a thematic domain, or study how authors differ in their
expressions of a theme [9]. In Lancashire and Hirst [19], the authors aim to study the
5   http://www.padovauniversitypress.it/publications/9788869381300


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 F. Vezzani, G. M. Di Nunzio – (Not so) Elementary, My Dear Watson! A Different Perspective
                                                                  on Medical Terminology

vocabulary changes in Agatha Christie’s novels. The innovative part of this work is the use of
the linguistic analysis for the detection of indications of dementia in the author’s style. Authors
analyze the vocabulary of Christie, who, although never diagnosed, was believed to have
suffered from dementia in her final years, even as she continued to write. By studying the
concordances in the literary works of Agatha Christie, the authors concentrate on measuring
the richness in the vocabulary and on syntactic and discourse-level aspects of her texts.


        Our proposal
In this paper, we present a combined method of quantitative and qualitative analysis of literary
works of Conan Doyle, focusing on the terminological viewpoint. To our knowledge, this is the
first attempt which aims to study medical terminology in the stories of Sherlock Holmes
through the combination of a computational approach for the extraction of medical terms and
their qualitative analysis performed through a new model of terminological record. Our main
goal is the study of how a specialized terminology is represented in literary works, focusing on
vocabulary changes in terms of diachronic and register variations.
The remainder of this paper is organized as follows: in Section 2, we provide a short
background about the life of Conan Doyle in order to show the close connection between
medicine and his literary works. Then, we present the studies related to medical topics in
Conan Doyle’s narrative. In sections 2.1 and 2.2 we define our proposal by outlining our mixed
method of analysis based on 1) the extraction of medical terminology through the tidytext R
package for text analysis,6 2) a terminological analysis through a new model of terminological
record and 3) the study of collocations through the linguistic tool Sketch Engine. In Section 3,
we provide a linguistic analysis of some features resulting from our combined approach and,
finally, we provide conclusions and some hints on future work.


        Medical Terminology in Conan Doyle’s narrative

Numerous studies ([13]; [23]; [25]; [35]; [36]; [37]) reveal the amount of “medical subjects” in
Conan Doyle’s works: the author was, in fact, an esteemed and highly experienced physician
and much of his knowledge spreads into his stories. Conan Doyle received his bachelor’s degree
in Medicine at the University of Edinburgh in 1881. When he was still a student (1878-1880),
Doyle carried out some assistance activities with numerous important physicians, first in the
Scottish capital and then in England. Together with this experience, he worked as a “ship's
surgeon” for seven months in 1880 on board of an ancient whaling boat named “Hope”. In
1885 Conan Doyle left England to go to Vienna, where he began work as an ophthalmologist.
He then returned to London in 1891 to open an ophthalmology clinic in the city center. After
a short period of activity and a severe summer influenza, in August 1891 Conan Doyle decided
to devote himself to writing.


6   https://cran.r-project.org/web/packages/tidytext/


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      Umanistica Digitale - ISSN:2532-8816 - n.6, 2019

The vastness of Conan Doyle's medical experience is reflected in the wealth of medical topics
covered in the stories of Sherlock Holmes. From a terminological point of view, Key J.D. and
Rodin A.E. are the first authors to explicitly mention the number of medical terms in Conan
Doyle’s works: “[…] 68 diseases, 32 medical terms, 38 doctors” [17]. Nevertheless, very little has
been written regarding the medical terminology in this literary corpus. The most recent and
complete contribution is the study by Ernesto Damiani, an expert physician interested in
history, who wrote “Elementare Watson!” [5]: a dictionary of Italian medical terms used in the
translated version of the stories of Sherlock Holmes. The author manually extracted about 98
Italian medical terms with the aim to provide a tool for facilitating the comprehension of such
terms while reading Sherlock Holmes detective stories. Damiani offers an overview of the use of
the terms and their explanations from a historical and medical point of view.
One of the objectives of our study is to partly reproduce Damiani’s study, that is the collection
of medical terms and its subsequent analysis. While Damiani performed a manual extraction,
we propose an automatic extraction of the medical terminology by means of the tidytext R
package for text analysis.7 We expect to verify the benefits deriving from this kind of approach
in terms of efficiency and accuracy, since we can automatically process the whole literary
production of Conan Doyle in a very short time. We also believe that an automated extraction
process may help to find more terms than those identified by Damiani. Moreover, while
Damiani focused on a description of the terminology from a medical and historical perspective,
our main goal is to combine a qualitative analysis from a purely linguistic and terminological
perspective by means of a new model of terminological record, implemented for the linguistic
analysis of technical medical terms [33].
To sum up, we propose to analyze medical terminology in literary texts through the
combination of a quantitative approach (automatic extraction) and a qualitative analysis
(terminological records). Our case study is the entire collection of works on Sherlock Holmes,
starting from “A Study in Scarlet” (1887) to “The Casebook of Sherlock Holmes” (1927).


         Automatic Term Extraction
The automated extraction of terms starts with the creation of the corpus of all the novels and
stories of Sherlock Holmes written by Conan Doyle. We used the “Complete Sherlock Holmes
Canon” website,8 since this website has one of the most complete types of documentation
about privacy policy and copyright issues about the re-use of the texts. In particular, we used
the ASCII version of the 4 novels and 56 short stories for a total of 60 text files.
We wrote the software to automatically process the files and produce the list of medical terms
with the R programming language; we followed the “tidyverse” 9 approach to process the data
and documented each step in R Markdown 10 in order to make the entire process completely


7    https://cran.r-project.org/web/packages/tidytext/
8    https://sherlock-holm.es
9    https://www.tidyverse.org
10   https://rmarkdown.rstudio.com


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F. Vezzani, G. M. Di Nunzio – (Not so) Elementary, My Dear Watson! A Different Perspective
                                                                 on Medical Terminology

reproducible by any researcher.11 We used tibbles (modern R dataframe structures) to store the
information about each book; we also used the dplyr package to make “pipelined” operations.
In particular, before any text pre-processing, we removed from each file the string “Arthur
Conan Doyle” at the beginning of the file and the copyright statement at the end of the file:
        ----------

            This text is provided to you "as-is" without any warranty. No warranties of any kind,
        expressed or implied, are made to you as to the text or any medium it may be on,
        including but not limited to warranties of merchantability or fitness for a particular
        purpose.
        This text was formatted from various free ASCII and HTML variants.
        See http://sherlock-holm.es for an electronic form of this text and additional information
        about it.
        This text comes from the collection's version 3.1.


We used the tidytext R package to perform text analyses: we created n-grams (unigrams,
bigrams, and trigrams) that are used to match medical terms. The following example shows the
source code used to generate unigrams, count the occurrence of each unigram and order the
table thus produced by decreasing frequency of occurrences:
# create unigrams
doyle_unigrams <- books %>%
    unnest_tokens(unigram, text, token = "ngrams", n = 1)
# count and sort unigrams
unigram <- doyle_unigrams %>%
 count(unigram, sort = TRUE) %>%
 rename(word = unigram, freq = n)


At the end of this initial pre-processing phase, we obtained: 19,686 unigrams, 213,656
bigrams, and 481,156 trigrams.


        Medical Termbase
In order to match and extract the medical terms from the pre-processed text, we used the
Medical Subject Headings (MeSH) database. 12 This database is a controlled vocabulary
thesaurus prepared and maintained by the US National Library of Medicine for indexing
articles in PubMED.13 In this context, the R package provided by the Bioconductor 14 website
contains a set of annotation maps describing the entire MeSH database (MeSH ORA). 15

11   https://github.com/gmdn/SherlockHolmesTidyverse
12   https://www.ncbi.nlm.nih.gov/mesh
13   https://www.ncbi.nlm.nih.gov/pubmed/
14   https://bioconductor.org
15   https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0453-z


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In order to query the database, we needed the RSQLite package. 16 The query selects all the
MeSH terms of the database as well as all the synonyms of each term. In fact, many synonyms,
near-synonyms, and closely related concepts are included as entry terms in the database to help
users find the most relevant MeSH descriptor for the concept they are seeking. Then, we
converted all the characters to lower case and remove any dot character from the text in order
to match the format of the text files (for example, St. Paul is converted to st paul). The
following code shows how mesh_terms and mesh_syns (synonyms) are generated:
# get terms and synonyms
select_mesh_syn <- "SELECT DISTINCT MESHTERM, SYNONYM FROM
DATA"
res_mesh_syn <- dbGetQuery(dbconn(MeSH.db), select_mesh_syn)
res_mesh_syn <- res_mesh_syn %>%
  mutate(SYN_CLEAN = sapply(strsplit(x =
res_mesh_syn$SYNONYM, # split syns
                                     split = "\\|"),
                            FUN = function(x) x[1])) %>% #
get first element
  dplyr::select(MESHTERM, SYN_CLEAN)

# get mesh terms and syns
mesh_terms <- tolower(unique(res_mesh_syn$MESHTERM))
# remove dots
mesh_terms <- gsub(pattern = "\\.", replacement = "", x =
mesh_terms)
mesh_syns <- tolower(res_mesh_syn$SYN_CLEAN)
# remove dots
mesh_syns <- gsub(pattern = "\\.", replacement = "", x =
mesh_syns)


At the end of this phase, we extracted 1,347 MeSH unigrams, 116 MeSH bigrams, 8 MeSH
trigrams.


       Terminological Record
After the automatic extraction of medical terminology, we proceeded to a linguistic and
terminological analysis of such terms through the model of the terminological record set out in
a multilingual database, named TriMED [33], conceived for the analysis of scientific terms
extracted from a specialized corpus.
This tool is designed in order to tackle the problem of the complexity of medical terminology
by considering different level of communications. Indeed, there are three categories of people
that are mostly affected by the complexity of medical language and who can benefit from the
use of this resource: patients, language professionals (translators and interpreters) and
physicians.

16 https://cran.r-project.org/web/packages/RSQLite/


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 F. Vezzani, G. M. Di Nunzio – (Not so) Elementary, My Dear Watson! A Different Perspective
                                                                  on Medical Terminology

Patients: Patients, or more in general lay people, find a considerable difficulty in
understanding information, both oral and written, about their own health [16]. As a
consequence, they need to understand medical technical terms by using their equivalents in the
popular language: TriMED provides the equivalent of the technical term in the popular
language, that is, the most frequently used term (as for example fever for pyrexia) and provides
an informative definition in a non-specialized register.
Translators and Interpreters: TriMED is designed to support “language professionals” by
offering terminological records providing the translation into three languages (English, French
and Italian) of the technical term and all the linguistically relevant information for the process
of decoding and transcoding it in its oral and written forms.
Physicians: In terms of spreading new health care protocols and scientific discoveries, language
could be a barrier to service transactions among medical specialists speaking different languages
because perfect knowledge and mastery of the foreign language is not an expected outcome. In
order to overcome these language barriers and to satisfy the peer-to-peer communication,
TriMED offers the possibility to consult the translation of the technical term in the specialized
linguistic register.
For the qualitative analysis of the medical terms extracted from the literary corpus, we used the
model of terminological record designed for this tool. Terminological records are commonly
used in terminology and linguistics as a tool for the collection of linguistic data referring to a
specific concept [11]. TriMED terminological record is structured around four axes of analysis
of the technical term:
    •    Formal features
    •    Semantics
    •    Corpus
    •    References.
Regarding the formal and lexical framework of the term, we provide information such as:
gender, spelling, pronunciation in the International Phonetic Alphabet (IPA) and other
information about the etymology, such as derivation and composition of the term. In addition,
we propose the spelling variant and the related acronyms which are currently used in medical
language. Finally, based on the WordNet resource, 17 the record contains all the nouns, verbs,
adjectives, and adverbs deriving from the analyzed term and which fall into the same semantic
sphere.
The second section focuses on the semantic features of the term. First, we propose a definition
extracted from reliable resources such as Merriam-Webster Medical Dictionary 18 or
MediLexicon19 especially for acronyms and abbreviations. In addition, we provide the semic
17 https://wordnet.princeton.edu
18 https://www.merriam-webster.com
19 https://www.medilexicon.com


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     Umanistica Digitale - ISSN:2532-8816 - n.6, 2019

analysis of the term [24], that is, a methodology used in compositional semantics in order to
decompose the meaning of technical terms (lexematic or morphological unity) into minimal
units of meaning: the semes. Moreover, in order to evaluate the semantic behavior of a term, we
collect the phraseology of the term by considering collocations [8] and colligations [30].
Finally, we provide the synonymic variants of the term: in this way, we categorize terms and
their semantic relations. In the corpus section, we provide all specialized contexts where
technical terms have been extracted and then we proceed through the identification of the
domain and the register of the term (popular, slang, familiar, current or standard and
specialized). Therefore, the term and its definition take on meaning when they are connected to
a specific domain: in our analysis, we identify the domain and subdomains of the text (such as
surgery, pathology, pharmacology, etc.). We also provide references to each source, since all of
this information has been extracted from different sources.
In our study, we added two field of analysis aiming at the evaluation of the diachronic and
diastratic variations of the term. With “diachronic variation”, we mean morphological and
semantic changes to which a term is subject over time, while “diastratic variation” refers to
changes related to the different register used by any given speaker. Figure 1 shows an example
of the terminological record of the term “St. Vitus’s Dance”. Moreover, a demo of the
application realized with the Shiny R package is also available.20




Figure 1: Example of Terminological Record.




20 https://github.com/gmdn/SherlockHolmesTriMED


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 F. Vezzani, G. M. Di Nunzio – (Not so) Elementary, My Dear Watson! A Different Perspective
                                                                  on Medical Terminology


        Linguistic Analysis

In this section, we describe the findings of our analysis. Firstly, the terms extracted belong to
the grammatical category of nouns. These nouns can be a single-word term (unigram) or a
multi-word term (bigram or trigram), that is a complex lexical unit composed of more than one
element. The unigrams extracted, when compared against MeSH terms as the reference corpus,
belong to the domain of the human body. Many terms are related to body parts with a different
index of frequency in the literary corpus: hand (745 occurrences), eyes (642 occurrences), head
(452 occurrences), etc. Other unigrams extracted referred to various type of disease or medical
condition such as wheezing, breathlessness, apoplexy, blindness etc. At the same time, there are a
few bigrams relating to human anatomy and physiology such as: lower limbs, upper arms, mitral
valve, occipital bone, parietal bone, subclavian artery, and carotid artery etc. Moreover, most of
the bigrams extracted refer to different types of disease such as rheumatic fever, typhoid fever,
yellow fever, enteric fever, or medical conditions as cataleptic attacks, brain injury or chronic
disease. Regarding trigrams extracted the most two relevant multi-word terms in the medical
field are the movement disorder St. Vitus’s Dance and the surgical procedure post-mortem
examination.
Thanks to terminological records, we were able to assign each term to a more specific medical
subfield. Table 1 shoes some examples of our classification.


 Medical subfield         Terminology
 Cardiology               Aneurism, Heart Disease, Wheezing, Shock, Rheumatic Fever,
                          Sudden Disease, etc.
 Pathology                Consumption, Diphtheria, Enteric Fever, Yellow Fever, Tapanuli
 (infectious diseases)    fever, Leprosy, etc.
 Anatomy                  Hand, Eye, Head, Heart, Arm, Shoulder, Finger, Brain, Neck, Nose,
                          etc.
 Neurology                St. Vitus’s Dance, Catalepsy, Convulsion, Headache, Brain Fever,
                          Stroke, Syncope etc.
Table 1: Example of medical subfields identified in Sherlock Holmes.


Moreover, terminological records allowed us to collect a variety of information for the lexico-
semantic framing of each technical terms extracted for the medical field. The semantics of the
term is useful for the detection of synonymic variants and their relations. For example:
“Post-mortem examination” is in a synonymic relation to autopsy, obduction, necropsy, or
autopsia cadaverum. The term autopsy is more frequent than the term post-mortem, when
considering the occurrences of both terms on the Web:
autopsy -> 23,200.000 occurrences,



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post-mortem examination -> 8,090.000 occurrences.
Moreover, thanks to the semic analysis of the terms we could see that necropsy is usually used
for animals, while autopsy is used for human beings.
“Typhoid fever” and “Enteric fever” are considered by Damiani as synonyms, while according
to other sources enteric fever is a collective term that refers to both typhoid and paratyphoid
fever.21
“Yellow fever”, as an acute febrile illness of tropical regions, is the result of a diachronic
variation in terms of linguistic use of three obsolete synonyms: black vomit, yellow jack, Bulam
fever.
The section related to the diachronic variation of a term make it possible to compare and
contrast the use of the term in the past and its current use. For example:
“Consumption” used to indicate the process of general decay of the organism in place of the
current cachexia or wasting syndrome.
“Brain fever” is an obsolete term used until the first half of the nineteenth century to indicate
the association between an irregular set of neurological symptoms and it is now replaced by
technical terms such as encephalitis, meningitis, cerebritis.
The section related to the diastratic variation of the terms also indicates the level of technicality
of a term. In our study, we categorized those terms that are mostly used by lay people such as
“popular terms” from the “specialized terms” such the exact terminology used by experts in
medicine. We divided popular terms from technical ones such as “St. Vitus's dance” for Còrea
minor, “Heart Disease” for Cardiopathy or “Nosebleed” for Epistaxis. In this way, we analyzed
changes in the linguistic register by focusing on the diastratic variation resulting from the
specialized-popular dualism in order to bridge the gaps between various registers.
For our linguistic analysis we used the previously mentioned software Sketch Engine in order to
evaluate the syntactic behavior of the technical terms in their literary context. Our preliminary
analysis, carried out through the use of the concordance tool, allowed us to notice the frequent
use of some phrasal verbs associated to medical terms (e.g. “to break out” with “fever”).
Moreover, with our quantitative approach we extracted the technical term “brainwave”. The
term refers both to the medical domain as any rhythmic fluctuations of electric potential
between parts of the brain, especially those seen on an electroencephalogram, and to a
phraseological expression indicating a sudden idea, understanding, or inspiration. Considering
the context in which the term appears with a frequency equal to 1, we excluded the term from
our analysis of medical terminology because it was relevant to a phraseological expression:


       "I fear that we have." "Surely you do yourself an injustice. One more coruscation, my
       dear Watson--yet another brain-wave!”
       (The Valley of Fear, 1914)

21 https://www.uptodate.com/contents/treatment-and-prevention-of-enteric-typhoid-and-paratyphoid-
   fever


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 F. Vezzani, G. M. Di Nunzio – (Not so) Elementary, My Dear Watson! A Different Perspective
                                                                  on Medical Terminology



Finally, another interesting consideration emerged from the study of the collocational behavior
of terms is the association of the psychological condition of “depression” to the domain of
colors. The examples below show that adjective “black” is frequently associated to depression
with a metaphorical connotation suggesting a serious mood disorder.


       “He was bright, eager, and in excellent spirits,--a mood which in his case alternated with
       fits of the blackest depression.”
        (The Sign of Four, 1890)

       “His bright humor marked the reaction from his black depression of the preceding days.
       Athelney Jones proved to be a sociable soul in his hours of relaxation and face his dinner
       with the air of a bon vivant.”
        (The Sign of Four, 1890)


       “Even the triumphant issue of his labors could not save him from reaction after so terrible
       an exertion, and at a time when Europe was ringing with his name and when his room
       was literally ankle-deep with congratulatory telegrams I found him a prey to the blackest
       depression.”
       (The Memoirs of Sherlock Holmes - The Reigate Squire, 1893)


       Conclusions and Future Work

In this paper, we presented a preliminary study of medical terminology in the entire collection
of works on Sherlock Holmes, starting from “A Study in Scarlet” (1887) to “The Casebook of
Sherlock Holmes” (1927). The method proposed is based on the combination of i) a
computational approach for the extraction of technical terms through the R programming
language and ii) the qualitative analysis of medical terminology performed through the model
of terminological record designed for the TriMED database. One of the objectives of our study
was to reproduce the analysis proposed by Damiani (consisting in the manual extraction of
medical terms) by adopting an automated approach. We were able to extract the medical
terminology through the identification of all the MeSH terms in the stories of Sherlock
Holmes with efficiency and accuracy. In addition, we extracted a larger number of terms than
those identified by Damiani: 1,347 MeSH unigrams, 116 MeSH bigrams, 8 MeSH trigrams.
Moreover, in order to favor the reproducibility of our experiments, we give access to the source
code and the data used in the analyses of this paper.
Regarding the qualitative analysis performed by means of the TriMED terminological record,
we considered some interesting linguistic features from the terminological viewpoint such as:
synonymic relation, semantic behavior and terminological variation. In particular, sections
related to diachronic and diastratic variation allow to lay the foundations regarding two specific
aspects of technical terminology and stylistics. First, we aimed at demonstrating that technical


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terms designating concepts may change over time. This means that concepts are not defined
and crystallized entities, but they can change depending on the historical era and culture.
Second, we were interested in Conan Doyle’s stylistics and, in particular, to which extent his
profession as a physician affects his writing. Thanks to the diastratic variation section, we will
be able to conduct future studies in order to detect if the author tends to use a specialized
terminology, while describing medical facts, or if he tends to use a “reader-oriented”
terminology, that is a more understandable language adapted to an audience of potentially non-
medical experts. In this sense, our future research question is to evaluate how much Conan
Doyle the physician affects Conan Doyle the writer.
For the moment, the preliminary results of this analysis showed that the model of
terminological record we propose can constitute a valid digital and systematic support, in
different contexts and corpora, both in specialized and literary fields. Moreover, we tested the
use of the concordance tool Sketch Engine in order to study the collocational behavior of
technical terms in literary context. As future work, we intend to take advantage from other
functions of the Sketch Engine tool, such as “Sketch Difference” in order to compare and
contrast two words by analyzing their collocations and by displaying the collocates divided into
categories based on grammatical relations. Finally, as terminological records are also designed
for language professionals, we intend to focus on the translation viewpoint in order to conduct
a comparative study between English medical terms extracted from the original texts of Conan
Doyle and their Italian equivalents in the translated version.


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