=Paper=
{{Paper
|id=Vol-2364/18_paper
|storemode=property
|title=Distant reading Brazilian politics
|pdfUrl=https://ceur-ws.org/Vol-2364/18_paper.pdf
|volume=Vol-2364
|authors=Suemi Higuchi,Diana Santos,Cláudia Freitas,Alexandre Rademaker
|dblpUrl=https://dblp.org/rec/conf/dhn/HiguchiSFR19
}}
==Distant reading Brazilian politics==
https://ceur-ws.org/Vol-2364/18_paper.pdf
Distant reading Brazilian politics
Suemi Higuchi1,2,5 , Diana Santos3,4 , Cláudia Freitas5,3 , and Alexandre
Rademaker6,7
1
Capes scholarship/PDSE/Process n.88881.187002/2018-01
2
CPDOC/Fundação Getulio Vargas, Praia de Botafogo, 190, Rio de Janeiro - Brazil
3
Linguateca http://www.linguateca.pt
4
University of Oslo, HF, ILOS, Pb 1013 Blindern, Oslo, Norway
5
PUC-Rio, Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro - Brazil
6
IBM Research, Avenida Pasteur, 138, Urca, Rio de Janeiro - Brazil
7
EMAp/Fundação Getulio Vargas, Praia de Botafogo, 190, Rio de Janeiro - Brazil
suemi.higuchi@fgv.br, d.s.m.santos@ilos.uio.no,
claudiafreitas@puc-rio.br, alexrad@br.ibm.com
Abstract. In this paper we propose the use of digital humanities tools
to ”read” and obtain aggregated information on Brazilian politics. After
presenting briefly the resource and its annotation, we describe the kinds
of searches already possible, our work for grounding human entities, and
some results on family relationships among Brazilian politicians.
Keywords: information extraction · Portuguese · Brazilian history
1 Introduction
The intricate relationship between traditional practices of recording knowledge
and new technologies is the indelible mark of the Digital Humanities (DH) move-
ment. They incorporate the methods and issues developed by the human and
social sciences, while mobilizing the unique tools and perspectives opened by
digital technology [12]. In the area most closely linked to language and litera-
ture, where there are millions of digital collections to study, observations made
at a distance and from various perspectives are only possible with the aid of
computers and statistical techniques capable of reducing the literature to a set
of interesting and manipulable data. In this sense, work with annotated corpora
in order to automate (and therefore eventually obtain) information like charac-
ters, plot, or events, is becoming mainstream. In this paper, we describe some
work in this vein, concerning a Brazilian resource named Dicionário Histórico-
Biográfico Brasileiro, DHBB for short. Although coined as “dictionary”, DHBB
has an encyclopaedic format, with long entries written by experts, describing
relevant actors in Brazilian history. It is a reference work and, as such, it is
not intended to be read in a linear (or conventional) way, but to be consulted
instead. Within the scope of Digital Humanities, with its tools, methods and
resources, to get the vast amount of information spread among DHBB pages in
a structured way is a challenge as desirable as predictable. The following sections
present the strategies and results obtained so far.
191 Higuchi et al.
2 The Dicionário Histórico-Biográfico Brasileiro
DHBB is an encyclopedia developed and curated by Centro de Pesquisa e Docu-
mentação de História Contemporânea do Brasil, from Fundação Getulio Vargas
(FGV), and is an important resource for all research, nationally and internation-
ally, interested in post-1930 Brazilian politics [1]. It contains information ranging
from the life trajectory, education and career of the individuals, to the relation-
ships built between the characters and events that the country has hosted.
DHBB was first published on paper in 1984, in four volumes containing 4,500
entries. In the 2001 update, the resource was increased by one more volume
reaching a total of 6,620 entries, and in 2010 its material was made available
on the internet, with about 7,500 entries. Currently the DHBB holds ca 7,700
entries and is continually updated and improved8 . The information system has
the following structure: per entry, its designation, the kind of entry (biographical
or thematic), and the text in a text field. The process and rationale of releasing
this content from the database and converting it to full text aiming at natural
language processing are described by [8] and [9]. Each entry became a single
file that received a unique identifier, and new metadata were added, such as
the gender of the biographed and the political role s/he had. All text files are
available from github.9
In 2018 we converted DHBB into an annotated corpus, subject to syntacti-
cal analysis by PALAVRAS [2] and semantic annotation by AC/DC10 [5], and
made it available through Linguateca’s site11 .The DHBB resource was thus en-
riched with syntactic and semantic information, quite useful for doing historical
research.
2.1 General characterization of the corpus
In this section we give some figures about DHBB’s content. Some of it comes
directly from the metadata associated with the previous versions, other cases
are a direct consequence of being in an annotated form. As we are still in a
preliminary phase of work, it is possible that some of these numbers will change
with time, but they are already good indicators of the richness of the material.
The universe we are working on from the DHBB comprises 7,685 entries. Our
intention is that as new entries are included, then updated new versions of the
DHBB will be made available at Linguateca as well. So the current version (v
2.3) corresponds to 314 thousand sentences, 9.8 million words and 156 thousand
different lemmas. More than 1.6 million tokens refer to proper names, 117,993
different ones. Of those, roughly 48,500 have been analyzed as person names,
8
Oficial webpage: https://cpdoc.fgv.br/acervo/dhbb
9
Available at https://github.com/cpdoc/dhbb.
10
The AC/DC project has as goal to annotate and make public corpora in Portuguese
since 1999, and provides a search service that allows complex searches on words,
morphosyntactic and semantic information. See [10] for more information.
11
Available at https://www.linguateca.pt/acesso/corpus.php?corpus=DHBB
Brazilian political history 192
27,500 as organization names and 5,000 as places names by PALAVRAS (besides
events, holidays, titles of books and films, etc). There are 6,717 biographical
entries, the rest being thematic. Table 1 shows an overview of the roles present
in DHBB (the same person can, of course, have more than one role throughout
her life), demonstrating its relevance.
Table 1. Description of DHBB in terms of political roles.
Role or job occurrences
Presidentes do Brasil (presidents of Brazil) 26
Ministros (ministers) 776
Ministros do STF (judges of the highest court) 96
Ministros do STM (judges of the highest military court) 118
Senadores (members of the Senate) 627
Deputados Federais (members of the Chamber of Deputies) 3,835
Militares (Army officers) 704
Participantes de revoluções (revolution participants) 368
Jornalistas (journalists) 196
2.2 A rich source of information
In the late 1980s, a study conducted by Michael Conniff [3] and [4] with a sample
of 7% of the entries (about 250 biographies at the time), enabled him to locate
important changes concerning age, education, social class and geographical origin
in the Brazilian political elite by close reading all these entries.
By extracting manually the information he was after, he was able to map
several interesting features of this elite. For example, in the beginning of the
twentieth century, most Executive members were middle-aged or older men, who
typically entered political life as second career, after having had other jobs. Later
on, those who aim for a political career get increasingly younger. On average
those born before 1900 start at 55, those born between 1901 and 1920 start at
37, and the ones born after 1921 start at 32 years old. As to formal education, the
most common one is Law (44%) followed by military education (32%). Engineers
and doctors follow with 12% and 5% each. The most definite change spotted by
Conniff is the decline in military careers of politicians: while for those born before
1920, 37% had military education, for the ones born after 1920 only 10% had.
Until now, if a researcher is interested in e.g. the question of ‘how did military
politicians enter politics in Brazil, through revolution or legally?’ s/he has to
read every relevant entry. The same happens for the questions ‘what is the path
most frequently followed to attain the presidency?’ or ‘where do the highest
military judges (ministros do Superior Tribunal Militar) come from in terms of
regions/states in Brazil after 1965?’ or even ‘what is the average age for a judge
to enter the Supreme Federal Court?’
193 Higuchi et al.
By annotating the free text with morphosyntactic information and several
semantic domains, we hope to be able to get most of this information automat-
ically. In DH terms, one could describe this as distant reading [6] for history.
3 Enhancing the DHBB with further relevant information
In addition to the usual information in an AC/DC annotated corpus, we concen-
trated on named entity recognition. In particular, for this resource, the recog-
nition of person names, places, organizations and political roles. Most of this is
already provided by PALAVRAS, and we just checked whether there were sys-
tematic problems that should be corrected. (For example, names like Eugênia
Lopes de Oliveira Prestes de Macedo Soares have been wrongly tokenized as two
proper names instead of one – Eugênia Lopes de Oliveira Prestes and Macedo
Soares –, but this is easy to correct with our rule-based tools for corpus annota-
tion revision, described in [11]).
In addition, and due to the fact that the same politician can be referred
to in several ways, especially in a context where s/he has been named before,
we decided to do entity grounding: we want to assign to each person name the
entity identifier it refers to, using as unique ‘identifiers’ the entry labels (see
section 3.1 below). Also, we added information relative to family relationships
to this corpus, as yet another relevant type of semantic information. We detail
the processing done in the next subsections.
3.1 The grounding process
There are many more cases of distinct proper names than distinct human entities,
and we want to identify who is who (i.e., to which entity they refer). So we
created an attribute entidade that contains the entry identifier which describes
that person in DHBB, and we try to assign it to all proper names which do have
a “definition” in DHBB.
Table 2. Examples of correspondence rules, that indicate the right identification to
proper names which do not use the entry name in DHBB. Proper names with more
than one word are coded with the “=” sign instead of space in the lemma.
AC/DC lemma Full name as entry in DHBB (entidade)
Aécio=Neves Aécio Neves da Cunha
Alencar=Castelo=Branco Humberto de Alencar Castelo Branco
Anthony=Garotinho Anthony William Matheus de Oliveira
Getúlio=Vargas Getúlio Dornelles Vargas
Lula Luis Inácio da Silva
So our task is to annotate the different human proper names in the texts so
that, if they refer to someone defined in DHBB, they receive the corresponding
Brazilian political history 194
entidade. Of course, there is a lot of people (spouses, parents, etc.) which are
mentioned in a biographical entry but are not necessarily politicians with a
DHBB entry. In cases where such people have to be mentioned in rules (see
below), they are assigned the label NV, which stands for “não verbetado” (not an
entry). If some people are very often mentioned in the DHBB but have not an
entry of their own, they may be good candidates for future inclusion.
The semi-automatic grounding process is as follows. First, we annotated those
proper names which are exactly equal to the entry form (usually the full name).
This allowed us to ground at once 89,937 words. Then, we produced a (first) list
of 116 correspondance rules in the form illustrated in Table 2, and managed to
increase the number of grounded proper names to 147,085. In a second iteration,
adding 71 new correspondences, we obtained 166,059 cases.
Another problem concerning proper names is that they can refer to different
people, as Table 3 shows.
Table 3. Proper names of people including the word Vargas (excluding therefore or-
ganizations like Fundação=Getulio=Vargas).
Proper name ocurrences
Vargas 3609
Getúlio=Vargas 1735
Ivete=Vargas 96
Benjamim=Vargas 52
André=Vargas 33
Lutero=Vargas 27
Alzira=Vargas=do=Amaral=Peixoto 18
Jorge=Vargas 9
Manuel=do=Nascimento=Vargas 7
Israel=Vargas 7
Manuel=Vargas 7
Darci=Vargas 6
Viriato=Vargas 6
Alzira=Vargas 5
Protásio=Vargas 4
Getúlio=Dornelles=Vargas 4
We could explore the following heuristic for ambiguous terms: mostly a
shorter form will refer to the entry subject. For example, in the case of the
string Vargas when located within the entry José Israel Vargas, it should be
referring to this very person. Nevertheless, this is not always the full story be-
cause exceptions can occur. For instance, the entry of Alzira Vargas do Amaral
Peixoto mentions Vargas to refer to Getúlio Dornelles Vargas, a very influential
Brazilian president (in 1834-1945 and 1951-1954) and also her own father. So,
after a manual check, we have implemented a specific form of correspondance
195 Higuchi et al.
rules which includes exceptions, as displayed in table 4. The rules should be read
as “designation X receives grounding entity Y if it appears in entry Z”.
Table 4. Cases where the shortest form of the name corresponds to the entry name
and cases where it does not.
AC/DC lemma entry where it appears correspondence entry name (entidade)
Vargas Getúlio Dornelles Vargas Getúlio Dornelles Vargas
Vargas José Israel Vargas José Israel Vargas
Vargas Alzira Vargas do Amaral Peixoto Getúlio Dornelles Vargas
Vargas Benjamim Dornelles Vargas Getúlio Dornelles Vargas
Vargas Lutero Sarmanho Vargas Getúlio Dornelles Vargas
Finally, another task that we foresee is doing (easy) anaphoric reference res-
olution by taking into consideration the person who is being biographed. In the
following examples, the underlined proper names refer to the main entry, in bold.
Getúlio Dornelles Vargas nasceu em São Borja (RS) no dia 19 de abril
de 1882, filho de Manuel do Nascimento Vargas e de Cândida Dornelles
Vargas. Vargas era descendente de uma famı́lia politicamente proemi-
nente em São Borja, região de fronteira com a Argentina, palco de ru-
morosas lutas no século XIX. O pai de Getúlio, Manuel do Nascimento
Vargas, combateu na Guerra do Paraguai, distinguindo-se como herói
militar.
Getulio Dornelles Vargas was born in Sao Borja (RS) on April 19,
1882, son of Manuel do Nascimento Vargas and Candida Dornelles Var-
gas. Vargas was a descendant of a politically prominent family in Sao
Borja, a region bordering Argentina, where rumorous struggles took place
in the 19th century. Getúlio’s father, Manuel do Nascimento Vargas,
fought in the Paraguayan War, distinguishing himself as a military hero.
3.2 Family relationships
One semantic domain that we are especially interested in can be illustrated
by the generic question ’How many politicians in the last decades belong to a
family of politicians?’ In Brazil there are powerful families since the colonial
period which can be said to form political dynasties. By pushing their children
and relatives to the parliament and the senate, they have been analysed as
strong power-maintaining devices [13], [7]. Has this phenomenon increased, or
decreased, lately? Does this practice only concern rich families of the periphery,
or has it also pervaded other less traditional groups? We know this information is
diluted in the thousands of DHBB entries, and we have started to add semantic
annotation on family relations in order to deal with it.
Brazilian political history 196
In AC/DC there are currently several domains that have been subject to thor-
ough annotation (colour, body, emotions, health, clothing), and for DHBB we
added family. We created a list of family-denoting words which were integrated
in the semantic annotation process, and we are currently creating rules (following
the explanation in [11]) to improve and correct the annotation. The lists include
50 family-denoting nouns, 10 family-related verbs and 9 other family-related
terms so far.
Even though this is in a preliminary stage, Table 5 shows the most common
family relationships in DHBB, while Figure 1 shows in context several cases of
family relationships among grounded politicians, using a simple search command.
Table 5. Most frequent family ties in DHBB. The second translation refers to the possi-
ble meaning of the plural. Eg. the plural forms filhos and irmãos can mean respectively
children (sons and daughters), or siblings.
Lemma occurrences
filho (son, child) 9444
pai (father, parent) 1488
irmão (brother, sibling) 1342
filha (daughter) 1144
mulher (wife) 523
tio (uncle) 312
primo (cousin) 287
esposa (wife) 248
mãe (mother) 230
sobrinho (nephew) 186
parente (relative) 172
irmã (sister) 131
marido (husband) 130
avô (grandfather) 116
cunhado (brother in law, in-law) 102
4 Some distant reading
In addition to the family relationships just shown, and by concatenating in a
single query the political role conveyed in the metadata, simple lexicosyntactic
patterns, and semantic information, it is feasible to search for things such as:
a) formal education of the federal deputies (deputados federais) elected by a
specific location – for example, the state of Rio de Janeiro (Figure 2); or b) their
birthplaces (Figure 3).
The results show that we have so far in DHBB 333 politicians who held the
position of deputy by Rio de Janeiro at least once, were born in 117 different
cities and their most common education background is: law (65), engineering
197 Higuchi et al.
Fig. 1. Getting family relations among grounded entities in AC/DC.
This print screen brings in context some of the found relations of kin-
ship among politicians included in the DHBB, using the following search
expression: [entidade="[1-9][0-9]*"]+ "," [sema="parentesco"] "de"
[entidade="[1-9][0-9]*"]+ [: entidade!="[1-9][0-9]*" :]
(15), medicine (11), economics (7) and business school (5), followed by theology
(4) and geography(4). When we contrast these results with the formal education
of all Brazilian federal deputies, it is interesting to note how close they seem to
be or not: geography, for instance, is not a common background in the sum of all
deputies, despite appearing in the profile of some of those who held the position
in Rio de Janeiro; philosophy, on the contrary, is well represented in the general
framework, but not in the deputies from that state.
5 Future work
One of the goals of presenting this resource to a DH community is to get input
as to further developments and intelligent ways of reading it distantly.
We plan to extract all sorts of information from DHBB and crosscheck the
data with small probes done by close reading.
We plan to annotate other semantic domains that appear relevant to studies
of Brazilian politics and that are brought to light by the users, things like political
parties, governments and alliances. And, in a longer perspective, we also envisage
map-based and chronological visualization capabilities, to endow DHBB users
with different ways of interacting, and comprehending the data.
Brazilian political history 198
Fig. 2. Printscreen with some results in context of the formal
education of the federal deputies elected by the state of Rio
de Janeiro. Syntactic search expression: [cargos=".*depfedRJ.*"
lema="formar.*|licenciar.*|bacharelar.*|graduar.*|cursar|estudar"][word="em"]*
@[pos="N|PROP.*"]
Fig. 3. Distribution with the most common birthplaces of the federal deputies elected
by the state of Rio de Janeiro. Syntactic search expression: [cargos=".*depfedRJ.*"
lema="nascer"] [lema="em(.*)*"] ([lema="estado|cidade|municı́pio"]
[lema="de(.*)*"])* @[pos="PROP.*"] [pos="PROP.*"]* [: pos!="PROP.*":]
199 Higuchi et al.
Fig. 4. Formal education of Brazilian federal deputies
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