A lexicon for processing archaic language:
the case of XIXth century SloveneTomaž Erjavec1, Christoph Ringlstetter2, Maja Žorga3, Annette Gotscharek2
1

Department of Knowledge Technologies, Jožef Stefan InstituteJamova cesta 39, 1000 Ljubljana
tomaz.erjavec@ijs.si
2
Centre for Language and Information Processing, University of Munich
Schellingstrasse 10, 80799 Munich
kristof@cis.uni-muenchen.de, annette@cis.uni-muenchen.de
3
maja.zorga@gmail.com
Abstract
The paper presents a lexicon to support computational processing of historical Slovene texts. Historical Slovene texts are being
increasingly digitised and made available on the internet but are still underutilised as no language technology support is offered fortheir processing. Appropriate tools and resources would enable full-text searching with modern-day lemmas, modernisation of archaic
language to make it more accessible to today‟s readers, and automatic OCR correction. We discuss the lexicon needed to support
tokenisation, modernisation, lemmatisation and part-of-speech tagging of historical texts. The process of lexicon acquisition relies on a
proof-read corpus, a large lexicon of contemporary Slovene, and tools to map historical forms to their contemporary equivalents via a
set of rewrite rules, and to provide an editing environment for lexicon construction. The lexicon, currently work in progress, will bemade publicly available; it should help not only in making digital libraries more accessible but also provide a quantitative basis forlinguistic explorations of historical Slovene texts and a prototype electronic dictionary of archaic Slovene.

1. Introduction
A large number of Slovene books and periodicals fromthe XIXth century and earlier are being made available on
the internet, e.g. via the dLib.si digital library (Krstulović
and Šetinc, 2005), the Slovene literary classics project at
WikiSource and Google Books.1 Human language
technology support could bring increased functionality tosuch digital libraries, esp. for full-text search andinformation retrieval. The most obvious task is automatic
lemmatisation of text, which abstracts away from themorphological variation encountered in heavily inflecting
languages, such as Slovene. The user can thus query fore.g. mati (mother) and receive portions of text containing
this word in any of its inflected forms (matere, materi,materjo, etc.). Support for lemmatisation, as well asmorphosyntactic tagging is well-advanced for modern-day
Slovene (Erjavec & Krek, 2008). However, the situation isvery different for historical Slovene, where no suchresearch has yet been carried out for the language.
Historical Slovene2 brings with it a number ofproblems related to automatic processing: due to the low print quality, optical character
recognition (OCR) produces much worse results thanfor modern-day texts; currently, such texts must be
hand-corrected to arrive at acceptable quality levels;full-text search is difficult, as the texts are notlemmatised and use different orthographic
conventions with different archaic spellings, typicallynot familiar to the user;
 comprehension of the texts for most users can also be
problematic, esp. with texts older than 1850 which
use the Bohoričica alphabet.3
We are currently developing a tool-chain forprocessing archaic Slovene texts which should alleviate
some of these problems. The tool, called ToTrTaLe, is anextension of the ToTaLe tool (Erjavec et al., 2005), which
performs tokenisation, tagging and lemmatisation, but
extended with a transcription module: after tokenisation,
the word-forms are first modernised as regards spelling,
and only then passed on to the tagging and lemmatisation
modules. This approach follows Rayson et al. (2007) inbeing able to use the well-developed tagging (andlemmatisation) models for contemporary language rather
than having to first develop such models for historicallanguage – a very lengthy and expensive process. 4 The
approach has the further benefit of offering thecontemporary words paired with archaic ones.
This paper focuses on the transcription aspect of thisprocess which crucially depends on a lexicon or, rather, aseries of lexica for the language. In previous work
(Erjavec et al., 2010) we concentrated on the first steps
3

1

Hladnik (2009) gives a good overview of digitisation efforts
and availability of Slovene texts on the internet.
2
In this paper we concentrate on the Slovene from the XIXthcentury; the problems are, of course, worse going further back intime, but even here, due to the late development of the written
Slovene word and its spelling standardisation, there aresubstantial differences to contemporary Slovene.

The Bohoričica alphabet had different conventions in writing
various Slovene sounds, e.g. »shaloſt« is the modern-day»žalost«, which makes it confusing for today‟s readers. Of
course, there are also substantial vocabulary as well as syntactic
differences, to contemporary Slovene.
4
For example, annotating for lemma and morphosyntactic
description 300,000 words of contemporary Slovene (Erjavec etal., 2010) took about 1,500 hours of annotator time.

(tools and work-flow) involved in manually producing alexicon of historical Slovene. In this paper we report on
the already developed lexica as used in the context ofToTrTaLe.
The rest of the paper is structured as follows: Section 2
details the process of transcription, Section 3 describes thecorpora we use in our work, Section 4 the lexica that areused and being produced, Section 5 the silver-standard
lexicon, to be made publicly available, Section 6 anexperiment studying the current coverage of ToTrTaLeand Section 7 gives some conclusions and directions forfurther work.

2. Transcription
In this section we explain how modern-day equivalents
are found for words in the historical texts, as thisrepresents the main difference to processing modern-day
language. The process relies on three resources:
1. A lexicon of modern-day word-forms withassociatedlemmasandmorphosyntactic
descriptions.
2. A lexicon of archaic word-forms, with associatedmodern-day equivalent word-form(s)5.
3. A set of transcription patterns, giving mappingsfor changes in alphabets (transliteration) andcommon spelling changes.
In processing historical texts, the word-forms are firstnormalised, i.e. de-capitalised and diacritic marks overvowels removed; the latter is most likely Slovene specific,
as modern-day Slovene, unlike the language of the 19th
century, does not use vowel diacritics.
The following filtering steps are performed on thenormalised word-form: if the normalised word-form is anentry of the archaic lexicon, the equivalent modern-day
word-form has also been identified; if not, it is checkedagainst the modern-day lexicon. Obviously, if thenormalised word-form is found in the modern-day
lexicon, its modern-day equivalent has been ipso-facto
found as well. This order of searching the dictionaries isimportant, as the modern lexicon can contain word-forms
which have an incorrect meaning in the context ofhistorical texts, so the historical lexicon also serves toblock such meanings. For example, the auxiliary verb
form sem used to be written as sim – but in the modern
lexicon this is identified as a noun, i.e. the SIM card of amobile telephone.
If neither lexicon contains the word, the transcription
patterns are tried. Many historical spelling variants can betraced back to a set of rewrite rules or “patterns” that
locally explain the difference between the contemporary
and the historical spelling. For Slovene, e.g., a very
prominent pattern is r→er as exemplified by the pairbrž→berž, where the left side represents the modern andthe right the historical spelling. Patterns can also besensitive to the word boundary, as some spelling changes
occur only at the start or the end of the word, e.g.
5

The two lexica have in fact a somewhat more complicatedstructure, which is further addressed in Section 4.

žganjem→žganjam, where the inflectional ending -am haschanged into modern-day -em. To enable this functionality
the appropriate patterns make use of the special symbol,“@”, e.g. em@→am@.
By corpus inspection we have currently developed aset of about 100 such patterns. These patterns areoperationalized by the finite-state tool Vaam (Variant
aware approximate matching). Vaam (Reffle, 2011) takes
as input a historical word-form, the set of patters, and amodern-day lexicon and efficiently returns the modernday word-forms that can be computed from the archaic
one by applying one or more patterns; the output list is
ranked, preferring candidates where a small number ofpattern applications is needed for the rewrite operation.
Vaam also supports approximate matching based on edit
distance, useful for identifying (and correcting) OCR
errors; we have, however, not yet made use of thisfunctionality.
It should be noted that the above process oftranscription is non-deterministic. While this rarely
happens in practice, the historical word-form can haveseveral modern-day equivalents. More importantly, theVaam module will typically return several possible
alternative modernisations. We currently determine the“best” transcription by choosing the most frequentcontemporary word between the possible modernisations,but more advanced models are possible, which postpone
the decision of the best candidate until the tagging andlemmatisation has been performed.

3. Corpora for lexicon building
To support our work on lexicon acquisition, we use
several corpora of Slovene; this section gives the details of
the corpora and briefly describes the concordancer used
for their inspection.

3.1.

Modern language corpora

For lexicon construction, including comparative
studies of historical language as opposed to modern
language, contemporary corpora are needed. For thispurpose we are using several corpora, all based on the
FidaPLUS6 reference corpus of modern Slovene (Arhar
and Gorjanc, 2007). FidaPLUS contains 600 million
words, where the words have been automaticallyannotated with morphosyntactic tags and lemmas. The
corpora we are using are the following, with the first two
having been developed in the JOS7 project (Erjavec et al.,2010):
 jos100k is a 100,000 word sampled corpus of modern
Slovene, with carefully hand-validated word-level
morphosyntactic and lemma annotations
 jos1M is ten times larger than jos100k but has only
partially hand-validated annotations
 fpj100M is a 100 million sample from FidaPLUS, andhas only automatically assigned annotations.

67

http://www.fidaplus.net/
http://nl.ijs.si/jos/These three corpora thus enable studying lexical
phenomena choosing either very accurate annotations, but
small dataset, or vice-versa. Which option is best depends
to a high degree on the frequency of the phenomenon(lexica item) being inspected.

3.2.

Historical language corpora

The corpus of historical language we have been mostly
using so far was compiled in the scope of the project
Deutsch-slowenische / kroatische Übersetzung 1848–1918(Prunč, 2007). The project addressed the linguistic studyof Slovene and Croatian books translated from German in
the period 1848–1918, where a large portion of the effort
went towards building a digital library (compiling a
corpus) of the Slovene translations. To this end, the books
were first scanned and OCRed, and then, for a portion of
the corpus, the transcription was hand-corrected, markedup with structural information, and, for a few books,
lemmatised; this process was supported by a web interface(Erjavec, 2007).
The sub-corpus chosen for building the historicallexicon includes all the AHLib proof-read books written
before the year 1900, where the oldest one was publishedin 1847. There are all together 71 such books, of which
the majority (56) are fiction (mostly novels) while 15 arenon-fiction (from self-help books for farmers, to textbooks on astronomy, chemistry, etc.). All together the
corpus contains approximately 2.2 million running words.
While certainly small compared to most corpora ofcontemporary language, it is large and varied enough tohave enabled us to start building the historical lexicon.
Recently, we have also collected the older materials
available from the WikiSource Slovene literary classics
project,8 led by Prof. Miran Hladnik from the Ljubljana
University. In the scope of this on-going project, the raw
OCR of books and other materials is being hand-correctedby students. We have downloaded the currently finishedtranscriptions and turned them into a uniformly encoded
corpus. Due to the lack of conventions in structuring Wiki
entries, the quality of the automatically acquired metadata is not very high, however, the corpus makes up forthis lack by its size: our current WikiSource corpus
contains over 500 publications with over 8 million words.
This corpus contains, in general, more recent texts than
AHLib, most from the late 19th and early 20th century.
Further historical materials are currently also being
hand-corrected, which are meant to extend the scope ofthe corpus, currently still lacking materials from the 18th
century, further into the past.

3.3.

The concordancer

All the collected historical corpora are being processedby the (current version) of the ToTrTaLe tool and arethen, together with the three corpora of contemporary
language, made available via a dedicated Web corpus
query interface, with CWB (Christ, 1994) as the backend.
8

http://sl.wikisource.org/wiki/Wikivir:
Slovenska_leposlovna_klasika

The concordancer enables searching and viewing thetokens, their normalised and modernised form, the usedtranscription pattern, and their computed morphosyntactic description (i.e. fine-grained PoS tag) andlemma, where the view can be either Keyword in Context(KWIC) or a frequency list. The concordancer has proved
to be very helpful in determining the status and preferredannotation of the historical lexical items.

4. Types of lexica
This section gives the various types of lexica used by
the program, namely: lexicon of contemporary language;
historical
word-forms
withtranscriptions
into
contemporary language equivalents; historical words
without contemporary equivalents; words missing in thecontemporary language lexicon; abbreviations; and words
which need to be re-tokenised in the modernisation step.

4.1.

Contemporary language

The lexicon of contemporary Slovene used was
extracted from the FidaPLUS corpus, where each word
was automatically annotated with its morphosyntactic
description (MSD) and lemma. The MSDs are compact
strings that represent the morphosyntactic features of theword form, and can be decomposed into features, e.g. theMSD Ncms is equivalent to Noun, Type = common,
Gender = masculine, Number = singular.

The lexicon was gathered from the corpus by
extracting all the triplets consisting of the word-form,
lemma and MSD. The word-forms were lowercased.
Using regular expressions, entries with anomalous“words” were removed, and only those lexical items witha frequency greater than 4 were retained. With this wearrived at a lexicon, which contains about 600,000 wordforms and 200,000 lemmas.
The lexicon is large enough to cover the majority ofcontemporary lexis found in historical texts, i.e. it has
good recall – however, its precision is relatively low, as it
contains many false friends. One example (sim) was
already mentioned; another case is serca, an archaic form
for srca (heart[sg,gen]), with the lemma srce. This form
exists in the modern lexicon, but with the lemma serec(horse of a grey colour). Such word-forms have to be
added to the historical lexicon, with the correctinterpretation, in order to block them being retrieved fromthe modern lexicon.

4.2.

Historical to contemporary transcriptions

The second lexicon being developed is that ofmanually verified historical word-forms. The approach is
corpus driven, so far using the AHLib corpus, andrelaying on LeXtractor (Gotscharek et al., 2010), a
specialised editor for historical lexica.

Figure 1. Example of a lexical entry in the historical dictionary
LeXtractor incorporates the Vaam pattern matching
functionality and supports both a frequency based
selection of entries to be added to the lexicon, as well as
directly annotating word tokens in corpora. As mentioned,
we give details of the manual lexicon building procedure,
as well as how LeXtractor was adapted for Slovene, in
Erjavec et al. (2010). Here, we will concentrate on thecurrent structure and content of the lexica.
The lexicon we are developing has a simple structure,
where each entry contains the following fields:
1. a word-form that has been witnessed in a proofread historical text
2. the equivalent word-form from contemporary
Slovene, possibly together with the patterns which
map the former into the latter3. the contemporary lemma of the word-form
4. the lexical morphosyntactic properties of thelemma
5. attestations of the word-form in the historical
corpusFigure 1 gives an example of such a lexical entry – theentry is formatted in HTML for the ease of illustration.
Note that the historical word-form is ambiguous, i.e. it has
two possible modern interpretations.
The intention of this manually collected lexicon is tocontain the most frequently occurring archaic words in the
texts; we have therefore applied frequency selection of theentries, so that closed class words are extensively covered,as are the most common open class words. We are also
including as many as possible of short historical words (upto 5 characters in length) as these most frequently havefalse friends in the modern lexicon, either directly or via
pattern application, as is the case of sim and serca.

4.3.

Words without descendants

The other type of historical lexicon concerns wordforms that are missing a modern-day descendant, i.e. they

do not have a corresponding contemporary lemma. For
such words, LeXtractor does not currently have thefunctionality to enter a structured entry, apart from acomment and the attestations. Since we decided it useful
to further analyse such entries, we currently enter in thecomment space the following information:
1. historical lemma, as it would be written today
2. the closest contemporary Slovene synonym(s)3. the PoS of the historical lemma
4. the source (dictionary, corpus) on the basis ofwhich the synonyms were chosen
5. potential comments
The reasons that we are adding this information are
twofold. First, by providing the “virtual” modern wordform, we are increasing the possibility of a user finding
this word, even though unsure about its archaic spelling;similarly, the tagger has a greater chance of assigning thecorrect MSD to such a word. Secondly, while the lexiconof transcribed words is necessary for computational
processing of historical texts, it is, in general, not very
interesting for humans, esp. the pattern derived entries.
But the words without descendants are exactly those thatthe modern-day reader will most likely not understand atall. So, as long as they have been identified, it isworthwhile assigning them their near-synonyms andgiving the source where further information about themcan be found. Such a lexicon could then also represent a
prototype “bilingual” historical to modern dictionary,
which is still lacking for Slovene.

4.4.

Missing contemporary words

In order to improve the functionality of the tool andthe filter cascade, the maintenance of the modern lexicon
is crucial. Rather than modifying this lexicon directly, we,as discussed, either block inappropriate modern words by
including them in the historical lexicon, or add missing
words via a special lexicon. Of course, there will always

be words missing from the lexicon, and it is not ourintention to add all possible contemporary words that
could appear in historical texts, esp. as both the tagger andlemmatiser are able to handle unknown words. However,certain words have a rather unpredictable morphology,
which causes either the tagger or lemmatiser tomisinterpret them – when such cases are noticed they areadded to the lexicon of missing contemporary words.
Rather than adding word-forms individually, we have
implemented a Web application that is able to generate thecomplete inflectional paradigm given the lemma and partof-speech. Constructing exact paradigms on the basis of
this information is, in the general case, not possible, so theintention is for the lexicographer to automaticallyconstruct such a paradigm, and then edit by hand theerroneous word-forms.

4.5.

Abbreviations

A lexicon very important for correct tokenisation andsentence segmentation is that of abbreviations. The
tokenisation module of ToTrTaLe takes a list of
abbreviations, i.e. strings ending with a full-stop, which,however do not (necessarily) end a sentence; furthermore,
the period should be taken as a part of the abbreviation
token. Historical language uses some abbreviations notpresent anymore in contemporary language – these areincluded to the lexicon of historical abbreviations, andthen added to the tokeniser resource file. The lexicon also
includes for each abbreviation its expanded form(s),although these are not currently used by the program.

4.6.

Token translations

There is a final type of satellite lexicon that we use in
ToTrTaLe, which is interesting from a computational
perspective. In historical Slovene certain words ormorphemes were written apart or together, where it is now
the other way around. The most prevalent and productive
example is the prefix that forms the superlative degree ofadjectives: what used to be written nar boljši is now
najboljši. As (word) tokens in text processing represent
the basic division of characters into linguistic units, which
are then further annotated, having a mismatch between
archaic and contemporary Slovene at this level ofdescription is difficult to process and encode; from being astring transcription and classification problem, themapping of old to new language becomes one of machinetranslation. This is an interesting problem, esp. as it is by
no means confined to historical language varieties; thesame phenomenon can be found in contemporary Slovene(and other languages) where, in informal or “badly
written” language people often write certain words apart,or run separate words together.
Luckily, in historical Slovene, apart from thesuperlative prefix, and a few other minor cases, only awell-defined set of closed-class words have changed theirtokenisation. The tokeniser used by ToTrTaLe uses
various classes of special lexica; one of these covers
compounds, and the other “clitics”, i.e. where a prefix or
suffix should be split from the word, such as -lo in Italian.
We have identified all (or most) of the closed classcompounds and splits, and have also taken all thesuperlative adjectives found in the AHLib corpus into thecompounds list. At least for these latter, this is only a stopgap measure; in the case of Slovene superlatives, a simple
regular expression (nar .*) would cover almost all
situations; as mentioned, the general case is, however,much more complicated.
The tokenisation lexicon thus contains two types oftokens, those that should be kept as one token (about 400),
and those that should be split (10); in processing, thesetokens are given special flags, which are retained in the
output. Vaam patterns are also needed to modernise suchcases, e.g. @naj→@nar_, where the underscore
represents the space character.
entries

77,783words

63,447lemmas

18,940modern entries

73,736

historical

3,181

no descendant

529

blocked modern

230

abbreviations

63

merged44

Table 1. The size of the silver standard historical lexicon

5. Silver standard lexicon
From the partial and heterogeneous lexica we created a“silver standard” historical lexicon, which, in addition to
the hand-gathered lexica also contains automaticallycollected “safe” modern words attested in the historical
corpus. The AHLib corpus was annotated with ToTrTaLe,
and the lemmas of all the contemporary words wereverified against a lexicon composed of the lexicon derived
from the jos100k corpus and the large Slovenemonolingual dictionary SSKJ. If the automaticallyassigned annotations matched those in this lexicon thenthe entry was included in the silver standard lexicon. Thisapproach yields highly reliable lexical entries.
Table 1 gives the size of the current lexicon, where an
entry is taken to be the 4-tuple (normalised word-form,
modern word-form, modern-lemma, PoS/MSD). The mainpart of the lexicon is contributed by modern words, while
the manually collected part of historical forms currently
has about 4,000 entries.
The silver standard lexicon is encoded against aslightly enhanced schema of the LeXtractor lexicon dump
XML. As illustrated in Figure 2, each entry is given atype, and is headed by the (normalised) word-form. The
entry can have several analyses, each giving themodernised form, lemma, PoS, possibly modern nearsynonyms and attestations. Entries for the tokenisation
lexicon are recognised by having a white-space in theword-form or modern derivation.

<entry type="no_descendant"><wordform>alipak</wordform><note>kontekst</note><analyses><analysis><lemma>alipak</lemma><pos>C</pos><derivations><derivation>ali pak</derivation></derivations><synonyms><synonym>ali</synonym><synonym>ali pa</synonym></synonyms><attestations><attestation
src="korpora/FPG06523.txt"
position="58207"><pre>— ali na suhi zemlji gdè v
Ameriki ,</pre><word>alipak</word><post>le na kterem ostrovi , šedozdaj ni vedel</post>r</attestation>…
Figure 2 XML encoding of the lexicon.

down to 54% with the historical one. The third line gives
the number of modern words found in the silver-standard
dictionary derived lexicon; again, the number of types
drops from 83% to 54%. Comparing the “Modern” and“Dictionary” number of the Wiki corpus processed withand without transcription, we note that the numbers
obtained with transcriptions are slightly lower; the reason
is that some words from the modern lexicon are, whenusing transcription, blocked by the historical lexicon.
The next line gives the number of unknown words; if
Spook has about 16% unknown word types, Wiki withouttranscriptions has over 45%. With transcription thisnumber drops to 39%, i.e. while we do experience somegain, we are still far from reaching modern-day
recognition rates. The decrease of unknown words whenusing transcription can be mostly attributed to the use ofpatterns; they help in recognising almost 6% of word
types, which is, however, only 0.5% of word tokens; andeven here we have to take into account that there is no
guarantee that the found modern word is in fact the correctone. The rest of the decrease in unknown words is due tothe lexicon of historical words. Out of about 4,000 entries
currently in the historical lexicon 2,200 were used; this isunder 1% of the lexical types, i.e. much less than coveredby the patterns, but, conversely, the number of tokens
covered by the historical lexicon (0.76%) is greater thanthat covered by the patterns (0.49%).
Spook

Tokens

%

Types

%

Words

1,825,692

100.00120,723100.006. Lexicon coverage

Modern1,773,019

97.11

100,954

83.62

We performed an experiment in which we evaluate thecoverage of the ToTrTaLe given the current lexicon(s)and pattern set. As AHLib served as the development
data-set, we took for the experiment the Wiki corpus and,as the modern-day baseline, the Slovene part of the
SPOOK parallel corpus of recently translated novels.9
Both corpora were annotated with ToTaLe, and the Wiki
corpus also with ToTrTaLe. We were interested in how
the annotations of the two corpora differ when processedwith the same model, and how the historical corpus
annotations differ when processed without or with thetranscription.
Table 2 gives the number and proportions ofannotation classes, depending on the corpus and mode ofprocessing. The first row gives the number of word tokens
and (normalised) word types. The number of types, i.e. thesize of the lexicon needed to completely cover the
corpora, is quite high, but it of course also includes all thetypos etc. from the source corpora.
The second row shows how many words were found in
the modern FidaPLUS lexicon. The percentage issignificantly lower with the Wiki corpus, esp. if wecompare the number of types; from 83% with modern text

Dictionary

1,708,764

93.60

85,852

71.11

Unknown

52,673

2.89

19,76916.38

No lemma

920

0.05

584

0.48

9

This parallel corpus is being developed in the scope of the
SPOOK project, http://lojze.lugos.si/spook/

Wiki without transcription
Words

8,219,093

100.00249,262100.00Modern7,868,823

95.74

135,490

54.36

Dictionary

7,522,562

91.53

109,549

43.95

Unknown

350,270

4.26

113,772

45.64

No lemma

15,796

0.19

5,623

2.26

Wiki with transcription
Modern7,858,325

95.61

135,490

54.36

Dictionary

7,512,988

91.41

109,550

43.95

Historical

62,822

0.76

2,231

0.90

Pattern

39,9020.49

14,560

5.84

Unknown

258,044

3.14

97,73239.21

No lemma

9,767

0.12

4,398

1.76

Table 2. Coverage of lexica over modern-day SPOOK
corpus and 19th century Wiki corpus with and withouttranscription.

The last line in all three tables gives that number ofwords that could not be lemmatised. These words areinteresting, as they point to the morphological changes
that occurred over time; in the modern corpus there are
only 0.5% of such word types, while the Wiki withouttranscription has 5 times more, 2.26%; transcription
lowers this number to 1.76%. Such words which cannot belemmatised with the model for modern Slovene are very
consistently true archaic words, i.e. good candidates forinclusion into the historical lexicon.

7. Conclusions
The paper presented our methodology of building alexicon to help process historical language, in particular
the Slovene of the XIXth century in the context of the
ToTrTaLe tool. The background resources of this work
are a historical corpus, a contemporary lexicon of
Slovene, spelling variation patterns, and the Vaam and
LeXtractor software.
In further work we plan to significantly enlarge thehistorical lexicon; now that the tools have been set-up andwe have elaborated the methodology of the lexicographical work, we will engage more people to work on
the lexicon, with the target size between 10 and 20
thousand entries. We plan to move from the frequency
based word selection to annotating corpus tokens directly– this work also connects to our intention of compiling agold-standard historical corpus with hand validated
annotations. Such a corpus is useful for evaluating the
precision/recall of various computational annotation
methods and underlying resources, say the transcription
rules and, of course, the lexicon. As mentioned, we will
also extend the corpus with new materials, esp.
newspapers and older books.
Current work has also been exclusively empiricallydriven, i.e. we addressed only issues that directly arose outof the lexical items found in the corpus. In the future weplan to take into account the linguistic research on
historical Slovene that has been done so far , as discussed
e.g. in Orožen (1996). Hopefully, our computational
approach might also reveal new quantitative andqualitative linguistic insights into the language as used in
XIXth century Slovenia.
The concordancer to the corpora is already publicly
available at http://nl2.ijs.si/ahlib.html. We will also makethe produced corpus and lexicon available under aCreative Commons licence, in the hope that it will
facilitate further studies of Slovene historical language.

Acknowledgements
The authors would like to thank the anonymous
reviewers for their helpful suggestions. All errors in thepaper of course remain our own. The work presented inthis paper was supported by the EU FP7 ICT project
IMPACT, “Improving Access to Text”.

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