Jean-Baptiste<EMAIL_ADDRESS>
# Corpus and Models for Lemmatisation and POS-tagging
of Classical French Theatre

Jean-Baptiste Camps  Centre Jean-Mabillon, École nationale des chartes,
Université Paris, Sciences & Lettres  Simon Gabay  Université de Neuchâtel
Paul Fièvre  Bibliothèque Nationale de France  Thibault Clérice  Centre Jean-
Mabillon, École nationale des chartes, Université Paris, Sciences & Lettres
Florian Cafiero  GEMASS, CNRS / Université Paris-Sorbonne

###### Abstract

This paper describes the process of building an annotated corpus and training
models for classical French literature, with a focus on theatre, and
particularly comedies in verse. It was originally developed as a preliminary
step to the stylometric analyses presented in Cafiero and Camps (2019). The
use of a recent lemmatiser based on neural networks and a CRF tagger allows to
achieve accuracies beyond the current state-of-the art on the in-domain test,
and proves to be robust during out-of-domain tests, i.e. up to 20th c. novels.

###### keywords:

Lemmatisation; POS tagging; 17th c. French; Classical Theatre.

##  1 Introduction

If many lemmatisers and POS taggers have been trained, and sometimes
conceived, for French (e.g. Tellier et al. (2012), Urieli (2013)…), they
usually focus on contemporary French and tools for Ancien Régime French remain
scarce. One important exception is the TreeTagger (Schmid, 1995) model
developed by Diwersy et al. (2017) for the Presto project (Vigier and
Blumenthal, 2013-2017). They have prepared training data with c. 60,000 tokens
annotated manually, using an adapted version of Multex (Ide and Veronis, 1994)
and Grace (Adda et al., 1998) for the parts of speech (POS), and the Lefff
(Sagot, 2010), Morphalou (Romary et al., 2004) and LGeRM (Souvay and Pierrel,
2009) for the lemmas. Unfortunately, training data are not publicly available
(yet?)111The Presto data have been entirely revised after the initial
publication of this paper (cf. Gabay et al. (2020b)) and are available online
at the following address: https://github.com/e-ditiones/LEM17. , they are made
of non-normalised texts from the 16th to the 18th c. which prevents any
comparison task, and no detailed evaluation of the model is offered by the
designers of the model.

Recent developments have renewed the field of normalisation, lemmatisation and
tagging of historic language varieties, including variation rich languages.
Particularly, deep learning methods have been used to establish a new state of
the art for Middle Dutch (Kestemont et al., 2016), Medieval Latin (Kestemont
and De Gussem, 2017), Medieval Occitan (Camps and Couffignal, 2017), Early
Irish (Dereza, 2019) or Middle High German and a variety of other languages
(Schmid, 2019; Manjavacas et al., 2019). Convolutional or (bidirectional) LSTM
networks have been put to use, in architecture involving word or character
levels representations, for instance word embeddings, and usually a sequence
to sequence approach using an encoder–decoder model (Bollmann, 2019), that
allow for taking into account the context of a token at either word or
character level.

Such architectures are now competing with character level statistical machine
translation models for the state of the art of historical text normalisations
(Tang et al., 2018; Bollmann, 2019; Gabay and Barrault, 2020). The
developments of such architectures allowed to obtain results in the 89 to 96%
range for lemmatisation of historical languages (and even up to 97.86% for
standard languages like modern French, see Manjavacas et al. (2019)), though
they have been showed to be the most dependent on the quantity and quality of
training data, outperforming more traditional (essentially, rule based)
approaches only when substantial training data is available (Bollmann, 2019).

POS and morphological taggers, especially in the case of morphologically rich
languages, have also benefited from neural architectures, with state of the
art for modern languages often being attained by recurrent neural nets (Ling
et al., 2015; Heigold et al., 2016; Schmid, 2019), with accuracies in the
range of 97-98% for POS and 92-94% for morphology (Heigold et al., 2016), and
more specifically from 90 to 98% for POS tagging of historical languages
(Schmid, 2019; Manjavacas et al., 2019).

In this paper, we present in detail our corpus, the annotation choices, the
training set up and finally the results obtained by two models. On the one
hand, an “extended” model for lemmatisation of normalised 17th c. French
theatre, i.e. tested specifically for this data, but with enough additional
training material to have it perform relatively well on modern (16th c. to
18th c.) and even contemporary (19th c. to 20th c.) French. On the other hand
a POS-tagging model including the morphology (gender, number, tense…), based
exclusively on 17th c. century training data.

These models were initially developed to allow the stylometric analyses
presented in Cafiero and Camps (2019), especially the analyses based on lemma,
lemma in rhyme position, and POS 3-grams. In this context, getting results as
accurate as possible was a necessary step to ensure the reliability of
stylometric results. This initial motivation is reflected in some of the
choices of this paper, notably the constitution of the training corpus (which
is a sample from the corpus that was to be used in the analyses)222Stylometric
results are not presented in this paper. For them, see Cafiero and Camps
(2019)..

##  2 The Théâtre Classique Corpus

Because of its cultural importance for French literature and thanks to a few
innovative pioneers, 17th c. French theatre (also called théâtre classique,
“classical theatre”) benefits from many digital editions freely available
online. Among the various projects offering texts online, the Théâtre
classique database (Fièvre, 2007; Schöch, 2018) proposes one of the most
comprehensive collections. The texts derive from digitisations of 17th c.
prints taken from Gallica (Bibliothèque nationale de France, 1997). The oldest
print is usually used, but it is not necessarily the case (for detailed
bibliographic information on 17th c. printed theatre, cf. Riffaud (2014)). The
text is normalised manually and the spelling aligned with contemporary
French333About this (strange) French tradition, cf. Gabay (2014); Duval
(2015).

Among all the plays available, 41 comedies (cf. appendix A) have been selected
to create a training corpus. They have been written by six different authors
spread over two generation: Antoine Le Métel d’Ouville (1589-1655), Jean de
Rotrou (1609-1650), Paul Scarron (1610-1660), Pierre Corneille (1606-1684),
Molière (1622-1673) and Thomas Corneille (1625-1709). All the plays have been
written between the 1630’s and the 1670’s, that is to say within c. 40 years.

##  3 Building an annotated corpus

The annotation scheme has been conceived to cope with diachronically spread
data, especially earlier periods such as middle and Renaissance French.

###  3.1 Choice of authority lists

Since annotation principles are rather complex, we have decided to publish
guidelines separately (Gabay et al., 2020a), and we will summarise here our
most important choices.

Regarding POS tags, on top of the aforementioned Multex, many possibilities
exist: Eagles (Leech and Wilson, 1996), Ud-Pos (Petrov et al., 2011) and
Cattex (Prévost et al., 2013). While EAGLES and UD-POS have been developed as
international standards, Cattex has been designed specifically for French
medieval texts.

We have decided to choose Cattex, because we are interested in a long
diachronic perspective, and we want to maintain interoperability with several
existing corpora that are already using it, such as the Base de français
médiéval (“Medieval French Database”, cf. Guillot-Barbance et al. (2017)) or
the Geste corpus (Camps, 2019)444It is to be noted that using Cattex does not
prevent, in the future, conversions to other tag-sets. For instance, the SRCMF
corpus (Prévost and Stein, 2013), originally tagged with Cattex tags, exists
in a converted version using UD-POS (Prevost Sophie, Stein Achim, SRCMF,,
2020). .

The annotation manual of Cattex09 (Guillot et al. (2013b)) offers a detailed
list of tagging rules that we strictly observed. Three options are offered to
the annotator: morphological tagging, morpho-syntactical tagging555With
morpho-syntactical tagging, categories are mainly determined in context: il
veut le bien (bien is NOMcom) vs il est bien grand (bien is ADVgen)., or both.
If adding both labels is ideal (to study processes such as adjectivisation,
substantivisation…) it remains far to costly in time, and we have therefore
opted for a simple morpho-syntactical tagging, that appeared at the time as an
interesting middle way.

Regarding lemmatisation, we have already mentioned LGeRM, the Lefff and
Morphalou. The main interest of the last one, that we have chosen, is that not
only the v3.1 includes the Lefff, but it is also used by the Frantext base –
the data of which is partially available online to be used as additional
material for our model – and the Trésor de la Langue Française informatisé.
The LGeRM lexicon is irrelevant in our case, since it is an artificially
archaised version of Morphalou to match 17th and 18th c. forms. Concerning
proper names, we built a specific reference list, thanks to the characters and
places index provided by Fièvre (2007), that we expanded when necessary.

Some of our choices diverge from those made by the authors of Morphalou. We
were, for instance, more systematic in choosing the masculine singular form as
a lemma for nouns (baronne is lemmatised as baron) but not only (la (det.
def.) as le, sa or ses (poss.) as son). Concerning personal pronouns, the
singular masculine subject (when relevant) case has been used as lemma: direct
regimen forms (le, la, les, as in, il les donne) as well as indirect regimen
forms (lui, elle(s)) have been lemmatised to the subject masculine singular
(il) – one single pronoun can indeed be subject (il), reflexive (se), direct
object (le or en), indirect object (lui or y) or disjunctive (lui). Still in
line with our diachronic approach, we kept the difference between the old
partitive des (contracted form of de les) and the new non-definite plural
article des, and encoded the contracted form au(x) as a+le.

It has to be noted that, since lemmas are not numbered in Morphalou, it has
not been possible to introduce a number-based disambiguation for homographs
(e.g., son1 (poss. “his”) vs. son2 (noun “sound”)…). Such a situation is
however only partially problematic, since it remains possible to distinguish
forms thanks to the POS, or the morphology.

###  3.2 Texts preprocessing and sampling

In order to limit model biases, each play of the corpus was sampled to create
training and testing data. The text of Fièvre (2007) editions have been
tokenised using TXM (Heiden, 2010) xml import. During the import an XSL filter
was used to retain only the character’s speeches, with exclusion of all other
material (stage directions, act and scene numbering…). Out of these data, a
three-tier sample was constituted with the 2,000 first tokens of our 41 plays
for training data (hereafter train set), the 100 median tokens for validation
data (hereafter dev set), and the last 100 for testing data (hereafter test
set). Case was not normalised, in order to keep information relevant to the
identification of proper names.

The complete XML and annotation workflow is presented in fig. 1666Since the
initial publication of this article, the effects of Unicode NFKD normalisation
have also been tested, with an unclear effect on training accuracy Gabay et
al. (2020b). .

###  3.3 Annotation and correction process

The annotation has been done in three phases. A first Pie (Manjavacas et al.,
2019) lemmatisation model has been trained only on the Frantext Open Access
data (ATILF-CNRS and Université de Lorraine, 1998-2018), and has been used to
annotate a first sample of c. 40,000 tokens, in combination with an available
Pie model for POS tags trained on Old French777A recent version of the model
for Old French can be found as part of the web application Deucalion (Clérice
et al., 2019); they are also directly usable through Pyrrha’s interface
(Clérice et al., 2019). Finally, the most up-to-date version of both the Old
French and Classical French models is provided, along with functionalities to
ease tagging of new documents, as part of the simple pie-extended Python
Package (Clérice, 2020). The models can be procured using their linguistic
code (fro for Old French, fr for Classical French) by running the commands:
pie-extended download fr and texts can be annotated by pie-extended tag fr
MyFile.txt. They also are available online, on the École des chartes’
Deucalion instance, at https://dh.chartes.psl.eu/deucalion/. . After being
corrected, the same corpus has been used to train new models and annotate c.
40,000 other tokens that were, once again, corrected to create the final
training corpus.

Original XML/TEI tokenised XML/TEI TXM tokenised txtsamples Old French data
Pie POS training Frantext Open Data Pie lemma training uncorrected annotated
tsv Pyrrha corr. corrected annotated tsv training corpus Pie lemma +Marmot POS
training initial models ulterior models Annotated XML/TEI TXM reimport \+ XSL
normalisations TXM Corpus CSV export (words, lemmas, POS n-grams, etc.) To the
stylometric analyses… TXM tokenisation\+ XSL (keep only
speech)XSLsamplingphase 1phase 2-… Figure 1: Workflow for building the
training corpus and models (ellipses) and the annotated XML corpus used for
stylometric analysis (rectangles); void-arrowheads specify steps particular to
the initial model building phase

Lemma and POS-tags have all been corrected manually. This work was facilitated
by the use of Pyrrha (Clérice et al., 2019), a web-based correction interface
able to do batch correcting as well as to handle authority lists, allowing
efficient collaborative work (fig. 2). Pyrrha also keeps tracks of all changes
made on the corpus (fig. 3), and makes it possible to import, correct, share,
and download back corpora and authority lists.

Figure 2: Screenshot of the Pyrrha interface: main correction view

Figure 3: Screenshot of the Pyrrha interface: correction history and corpus
statistics

###  3.4 Expanding annotation through available resources

If POS-tags have all been systematically corrected, through both linear
reading and batch corrections, it is not the case of the morphology, which has
only been mostly batch-corrected, because of time concerns. Thus, we can
guarantee that every POS tag has been proofread at least once (and usually
multiple times), which is not the case for the morphology.

Indeed, to save time, morphological information was not added manually, but
was instead projected using the lexicon of inflected forms Morphalou (ATILF-
CNRS and Université de Lorraine, 2016). To do so, Cattex POS-tags were mapped
to Morphalou’s categories (table 1)888Punctuation and proper names were dealt
with separately and specifically, as well as Cattex combined label,
PRE.DETdef. The correspondence between some categories reflect substantial
difference in the grammatical typology used. For instance, Cattex PROord
(e.g., ‘premier’ in ‘le premier’) match Morphalou’s more generic common noun
category.For more information on Cattex categories, see Guillot et al.
(2013a). . Then, a simple algorithm looked for matching forms inside
Morphalou: when the form was unambiguous, the morphological information was
directly retrieved, otherwise the hand-corrected POS was used to assess the
correct morphological information to retrieve. If none was found, an unknown
morph tag was added.

Cattex | Morphalou
---|---
INJ | Interjection
ADVgen | Adverbe
ADVneg | Adverbe
ADVint | Adverbe
ADVsub | Adverbe
CONcoo | Conjonction
CONsub | Conjonction
VERcjg | Verbe
VERinf | Verbe
VERppe | Verbe
VERppa | Verbe
PRE | Préposition

Cattex | Morphalou
---|---
DETdef | Déterminant
DETndf | Déterminant
DETdem | Déterminant
DETpos | Déterminant
DETind | Déterminant
DETrel | Déterminant
DETint | Déterminant
DETcom | Déterminant
PROper | Pronom
PROimp | Pronom
PROadv | Pronom
PROpos | Pronom
PROdem | Pronom
PROind | Pronom

Cattex | Morphalou
---|---
PROord | Nom commun
PROrel | Pronom
PROint | Pronom
PROcom | Déterminant
ADJqua | Adjectif qualificatif
ADJind | Adjectif qualificatif
ADJord | Adjectif qualificatif
ADJpos | Adjectif qualificatif
NOMcom | Nom commun
ADJcar | Nombre
DETcar | Nombre
PROcar | Nombre

Table 1: Mapping of Cattex POS-tags to Morphalou categories

Finally, Morphalou’s information was converted back to Cattex09 format, using
the mapping presented in table 2.

Morphalou | Cattex
---|---
Mode
indicative | ind
imperative | imp
conditional | con
subjunctive | sub
infinitive | -
past | -
participle | -

Morphalou | Cattex
---|---
Temps
present | pst
imperfect | ipf
future | fut
simplePast | psp
Pers.
firstPerson | 1
secondPerson | 2
thirdPerson | 3

Morphalou | Cattex
---|---
Nomb.
singular | s
plural | p
Genre
masculine | m
feminine | f
neuter | n
Varia
- | -
invariable | x
1036442 | ERROR

Table 2: Mapping of Morphalou to Cattex flexion tags.

###  3.5 Increasing corpus generality with Frantext data

The Frantext base offers an open access version (ATILF-CNRS and Université de
Lorraine, 1998-2018), 32 texts of which have been used to increase the
training data (see appendix B). Marginal interventions have been made to
correct some systematic errors, but also to ensure its consistency with our
annotation choices. For instance, for pronouns (labelled as CLO, CLS and PRO
in Frantext data), the lemmas je, me, m’, M’, moi, Moi, were mapped to je;
likewise, ils, elle, elles, le, la, les, lui, leur, eux, Ils, Lui, Elle,
Elles, to il, etc. On the other hand, some forms of celui and cela were
originally lemmatised to il, and we changed the lemmatisation to celui and
cela. Similarly, forms of chacun (or aucun) were lemmatised to un, and we
changed it to chacun (or aucun).

We were also more systematic in the alignment of feminine and masculine forms
to a single (masculine, singular) lemma, may it be for possessives (mienne,
tienne, sienne to mien, tien, sien) or just nouns (hôtesse, amie, veuve,
captive, to hôte, ami, veuf, captif, etc).

A few minor corrections of obvious errors were also made (e.g., saurer to
savoir), especially regarding homograph forms of some lemmas (e.g., between
verbal forms of défaire, “undo’,’ and the noun défaite, “defeat”, or between
ver, “worm” and vers, “verse”). An additional adjustment has also been made
regarding the ligature œ (cœur), which has been preferred over its decomposed
form (coeur).

##  4 Training setup

As previously mentioned, many tools are already available. TreeTagger (Schmid,
1995) remains one of the most widely used, even though it is outperformed by
other solutions. For the French language, TALISMAN (Urieli, 2013) or MElt
Sagot (2016) are commonly used by the NLP community, but many other solutions
are available such as Lemming (only for lemmas, cf. Müller et al. (2015)),
Marmot (only for POS, cf. Müller et al. (2013)) or Pie (mainly for lemmas, cf.
Manjavacas et al. (2019)). We have decided to use the two latter.

###  4.1 Lemmatisation

Concerning Pie as a lemmatiser, we tested three different configurations
(table 3):

  1. 1.

base (sent-lm), default configuration, based on the configuration that
achieved best accuracy described in Manjavacas et al. (2019), using sentence
context, RNN character embeddings, as well as forward and backward language
models999For this configuration, we used a config file provided to us by
Enrique Manjavacas, main developer of Pie. ;

  2. 2.

wembs the same as the previous one, but with the adjunction of word
embeddings, initialised using pretrained embeddings;

  3. 3.

bert same as the previous one, but using CamemBERT embeddings (Martin et al.,
2019), reduced from 768 to 150 dims;

  4. 4.

cnn+wembs a configuration using CNN character embeddings, with word
embeddings, based on skipgram on a larger unlemmatised corpus. This
configuration is the one used for Cafiero and Camps (2019), with limited
additional tuning.

| context | char embs | word embs | hidden size
---|---|---|---|---
base | sentence | rnn 300 dims | 0 | 150
wembs | sent.+word | rnn 300 dims | 150 dims | 150
bert | sent.+word | rnn 300 dims | 768 to 150 dims | 150
cnn+wembs | sent.+word | cnn 150 dims | 150 dims | 150
Table 3: Configurations tested with Pie

For each configuration, due to the stochastic nature of the process, five
models were trained, using early stopping with threshold 0.001 and patience 6,
and the best one was retained.

For the third configuration, word-embeddings were pretrained using Word2Vec
Python implementation, on a large corpus of 343 theatre texts from Fièvre
(2007) and those of the Frantext Open Access that we presented supra, for a
total of c. 7M tokens.

###  4.2 POS tagging

For POS-tagging, we trained both Marmot and Pie on the training data we
produced, without further augmentation. The configurations were the following:

  1. 1.

Marmot: base configuration provided with Marmot, using the dev set during
training, and the test set for final evaluation;

  2. 2.

Pie: same configurations that for lemmatisation (base (sent-lm),wembs and
bert, see above and table 3) but with a CRF output layer to predict part-of-
speech tags;

  3. 3.

+aux: for each of the POS-tagging configuration, we tried to see if we could
obtain a gain in accuracy by using auxiliary tasks (the tasks used can be seen
in table 2 in bold, with the addition of Case, mainly used for personal
pronouns). In a multi-task setting, we trained linear classifiers for each
morphological feature, but sharing weights with the main task (POS
prediction).

##  5 Results

###  5.1 Calibration and in-domain tests

Results of the Pie lemmatisation training are presented in table 4. The best
Pie model (configurations 2, wembs) achieved 99.09% accuracy on the test set.
The results obtained with Marmot and Pie for POS on the test set are presented
in table 5, and the best results are achieved by the wembs+aux configuration.
Yet, in both cases, the variation between the accuracies are relatively low,
and not substantially higher than random variations between different
iterations of the same configuration.

| base | wembs | bert | cnn-wembs | support
---|---|---|---|---|---
all | 98.80 | 99.09 | 98.95 | 98.80 | 4181
unknown tokens | 70.31 | 71.88 | 76.56 | 65.62 | 64
ambiguous tokens | 97.32 | 98.02 | 97.43 | 97.43 | 857
unknown targets | 50.00 | 57.14 | 85.71 | 57.14 | 14
Table 4: Pie lemmatisation accuracies on the test set for the best model for each configuration. “Unknown tokens” are tokens never seen during training, while “ambiguous tokens” are forms that can correspond to different lemmas. “Unknown targets” are lemmas never seen in training, but that the neural network can still sometimes accurately predict, thanks to its character level modelling. | Marmot | Pie |
---|---|---|---
| base | base | +aux | wembs | +aux | bert | +aux | support
All | 96.87 | 96.72 | 96.51 | 96.84 | 97.01 | 96.65 | 96.15 | 4181
Ambiguous tokens | NA | 91.86 | 91.43 | 92.40 | 92.29 | 91.76 | 90.36 | 934
Unknown tokens | 82.57 | 86.24 | 86.24 | 78.44 | 81.65 | 76.61 | 73.39 | 218
Table 5: Results obtained for POS by the Marmot and Pie models on the test
set. For the Pie models, we confronted trainings with or without morphology as
auxiliary tasks.

###  5.2 Out-of-domain tests

To evaluate the ability of the best models to generalise for data from other
periods, we conceived two out-of-domain corpora. Since we want to evaluate
generality with regard to diachronic, diaphasic and diagenic (i.e. gender
based) variation, we have selected samples from 16th to the 20th century
texts, either from theatre plays or from a variety of genres outside theatre,
literary or practical (administrative texts, correspondence, etc.), from male
and female authors, in order to have:

  * •

20 samples of roughly 100 tokens for each century, 10 of theatre plays, 10 of
a variety of other genres;

  * •

roughly as much tokens written by men or women for each century;

  * •

a comparable distribution of tokens by genres for each century.

In addition, for the samples concerning 17th century theatre, we excluded
verse comedies in general, and the authors from which were drawn our training
corpus. A complete list is given in appendix C.

We evaluate the best models (wembs and wembs+aux) for lemma and for POS on the
out-of-domain data (table 6 and 7).

Lemma
---
Corpus | 16th | 17th | 18th | 19th | 20th | All cent.
Theatre | 97.60 | 98.10 | 98.88 | 98.34 | 98.00 | 98.19
Not theatre | 97.78 | 98.02 | 98.06 | 96.97 | 97.39 | 97.65
Both | 97.69 | 98.06 | 98.46 | 97.66 | 97.70 | 97.92
Table 6: Lemmatisation accuracies of the best models on out-of-domain data POS
---
Corpus | 16th | 17th | 18th | 19th | 20th | All cent.
Theatre | 95.05 | 96.59 | 95.98 | 94.81 | 93.57 | 95.18
Not theatre | 92.89 | 94.27 | 96.53 | 91.87 | 91.35 | 93.42
Both | 93.93 | 95.44 | 96.27 | 93.36 | 92.48 | 94.30
Table 7: POS accuracies of the best models on out-of-domain data

The lemmatisation model proves to be relatively robust: globally, the loss of
accuracy is of roughly 1 percentage point, while it is closer to 3 percentage
points for the POS model. This difference can be explained by the difference
between the training corpora: the use of significant additional data to
improve the efficiency of the lemmatisation model seems to reflect in its
greater capacity to generalise. The same reason could also explain why the
lemmatisation models transpose better to non-dramatic texts than the POS
model.

In both cases, though, the better accuracies are reached for the 17th and 18th
centuries – and, surprisingly, more specifically for 18th century data in most
cases. It progressively decreases going backward or forward in time.

###  5.3 Improvement over previous corpus

In order to evaluate the importance of the addition of the newly annotated
data (Theatre), we set-up a second training experiment where we reuse the best
configuration found in 4.1 (wembs) over 5 iterations using the same training,
dev and test set, except for the files from the Theatre sample in train and
dev. Using the best model, we are able to measure the effect of the
introduction of Theatre data on top of the Frantext data. On lemmatization,
the results are clear: the overall accuracy with the same configuration goes
from 97.30% to 99.09%, an increase of 1.79 points. We cannot compare unknown
and ambiguous tokens as well as unknown tokens as the support from these
categories moved: -88 unknown tokens, +34 ambiguous tokens, -23 unknown
targets. The same impact can be seen on out-of-domain data (Table 8) with a
more visible impact on the Theatre corpus of the 18th century (+0.96 points).
Strangely enough, neither the 17th century or the Theatre genre benefits the
most from the inclusion of 17th century Theatre data, as the Not Theatre
corpus and the 18th century are the most improved ones.

Corpus | 16 | 17 | 18 | 19 | 20 | All cent.
---|---|---|---|---|---|---
Theatre | +0.16 | +0.30 | +0.96 | +0.00 | +0.22 | +0.32
Not theatre | +0.74 | +0.46 | +0.63 | +0.30 | +0.67 | +0.56
Both | +0.46 | +0.38 | +0.79 | +0.07 | +0.44 | +0.45
Table 8: Score improvements between Frantext corpus and Frantext+Theatre.
Reads as follow: on 17th century theatre, a model trained with
Frantext+Theatre has +0.30 points of accuracy, going from 97.80 to 98.10%.

###  5.4 Most frequent confusions

The most frequent confusions of the best models on the out-of-domain data are
presented in table 9.

Expected | Tot. Err. | Pred. | Pred. times
---|---|---|---
Lemma
monsieur | 9 | M. | 9
le | 9 | il | 9
cœur | 6 | cuur | 4
|  | crur | 2
franc | 6 | Franc | 6
Électre | 5 | éLECTRE | 4
|  | électre | 1
noble | 4 | Nobles | 4
maître | 4 | maîtresse | 4
de_le | 4 | un | 4
un | 4 | de_le | 4
dame | 3 | Dame | 2
|  | damer | 1
Phanor | 3 | phanor | 3
voir | 3 | vivre | 3
Vosges | 3 | vosge | 3
Médée | 3 | médé | 3

Expected | Tot. Err. | Pred. | Pred. times
---|---|---|---
POS
NOMcom | 182 | ADJqua | 45
|  | NOMpro | 33
|  | VERinf | 32
|  | VERcjg | 18
|  | VERppe | 11
ADJqua | 104 | NOMcom | 45
|  | VERppe | 17
|  | VERcjg | 12
NOMpro | 81 | NOMcom | 43
VERcjg | 61 | NOMcom | 18
|  | ADJqua | 12
|  | VERppe | 12
ADVgen | 58 | NOMcom | 16
|  | VERcjg | 14
PROrel | 31 | CONsub | 26

Table 9: Sample from the confusion matrix for the best Pie models on the out-
of-domain data.

Regarding lemmatisation, some errors related to homographs such as the token
le (regimen pronoun (il) or determiner (le); or the token des (plural of the
determiner un or partitive de_le). Some other errors are due to abbreviations
not present in the training data (M. for monsieur). More interestingly, for
the research of potential improvements, many errors are related to rare
characters classes in the training data, such as capital letters or ligatures
(œ).

For POS, the most frequent confusions are in nominal rather than verbal tags.
In particular, confusions between common nouns (NOMcom), proper nouns
(NOMpro), adjectives (ADJqua) and nominal forms of the verbs (participle,
VERppe, and infinitive, VERinf). Some errors are due to the morpho-syntactic
nature of our annotation, which, for instance, labels substantive adjective as
common nouns (le beau is NOMcom).

##  6 Discussion

The scores of up to 99% for in-domain lemmatisation and 97% for POS tagging
put our best models above the expected state of the art, or in it’s upper
range (see 1). Yet, these results were deserving of further investigation,
through out-of-domain tests and more qualitative inspection.

Out-of-domain tests show that, even though they were trained for 17th century
classical theatre, the model reach their best accuracies for 18th century
texts. Such a fact seems counter-intuitive, but a plausible explanation might
be as follow: the median date of the texts of the Frantext corpus is 1872, and
mean date is 1859. By adding roughly 80k tokens of 17th century texts to the
2,3M of Frantext (with mean text date 1859), we may have somehow slightly
pulled the corpus closer to the 18th century.

In any case, through the careful construction of a labelled corpus and the use
of a neural lemmatiser and tagger, we were able to develop models very
suitable to the annotation necessary for the stylometric analyses showed in
Cafiero and Camps (2019). But we think that the model are useful for the
annotation of 17th century theatre, and even beyond that, of Early Modern
French texts in normalised spelling, with encouraging results regarding
generalisation beyond the original scope of the experiments.

##  7 Further research

Looking at our results, the main lead for improvements should be a more
efficient way to deal with rare character classes, such as capital letters,
diacritics or ligatures. Several methods could be used to reduce the number of
classes (e.g., Unicode decomposed normalisation) or, alternatively, the
training set could be sufficiently extended to provide enough cases.

More generally, three possible directions could be followed in the coming
years. The first is to expand the training corpus to other dramatic genres
(tragedy, tragi-comedy…), and other genres in general (poetry, novels, short
stories…). The second would be to replace normalised tokens by non-normalised
ones, and therefore offer a new model that takes full advantage of the ability
of tools like Pie to deal with spelling variation in historical languages,
and, doing so, strengthen the ability of model to deal with older varieties of
French. The third is to expand dramatically the training with data taken from
18th or 16th c. texts. Results going in these directions and continuing the
experiments described in this paper will be presented at DTUC’20 (Gabay et
al., 2020b).

## Author contributions

P.F. encoded the corpus and all its metadata. F.C., J.-B. C. and S.G. designed
the research project. The preprocessing of the texts, the initial setup of the
Pyrrha corpus and of the authority lists were performed by J.-B. C. Correction
of the training data and expansion of the authority lists was shared equally
between F.C., J.-B. C. and S.G. Post-processing of the trained corpus,
injection of morphological data, and correction of the Frantext data was done
by J.-B. C., as well as the training, testing of the models and their further
use to annotate unseen data. T.C. programmed modifications to Pie code to
include Bert and participated in the training and benchmarking of models, as
well as additional debugging of the annotation tools. All authors contributed
to the writing of this paper.

The authors have no competing interests to declare.

## Materials and data availability

The most up-to-date version of the models can be easily obtained and used
thanks to the pie-extended Python package, available on Pypi
(https://pypi.org/project/pie-extended/), with the command pie-extended
download fr, and can be queried at https://tal.chartes.psl.eu/deucalion/.

The initial version created for Cafiero and Camps (2019) is available from the
Science Advances website since the publication of the paper, on 27th Nov. 2019
at this address:
https://advances.sciencemag.org/highwire/filestream/221312/field_highwire_adjunct_files/0/aax5489_Data_file_S1.zip.
The initial version of the models is available on Zenodo
(10.5281/zenodo.3353421).

The version of the best models described in this paper, as well as training,
validation and test data can be found on Zenodo as well, as version 3 of the
same repository (doi: 10.5281/zenodo.3828644).

Licensing is specified separately for each repository.

## Acknowledgements

We thank the DIM Science du texte et connaissances nouvelles for funding the
acquisition of a GPU server, as well as the École nationale des chartes for
providing infrastructure and support for the server. We thank Enrique
Manjavacas for his precious advice regarding lemmatisation and Pie
configuration, as well as Marie Puren for her help with the annotation of 20th
century texts.

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##  Appendix A Plays sampled to create the initial training corpus

The following plays selected from Fièvre [2007] were sampled.

id |  auteur |  titre | date |  genre | inspiration |  structure | type | periode
---|---|---|---|---|---|---|---|---
CP_DONSANCHE |  CORNEILLE, Pierre |  DON SANCHE D’ARAGON, COMÉDIE HÉROÏQUE | 1649 |  Comédie héroïque | moeurs espagnoles |  5 act. | vers | 1641-1650
CP_GALERIEDUPALAIS |  CORNEILLE, Pierre |  LA GALERIE DU PALAIS ou L’AMIE RIVALE | 1637 |  Comédie | moeurs françaises |  5 act. | vers | 1631-1640
CP_ILLUSIONCOMIQUE |  CORNEILLE, Pierre |  L’ILLUSION COMIQUE, COMÉDIE | 1639 |  Comédie | moeurs françaises |  5 act. | vers | 1631-1640
CP_MELITE33 |  CORNEILLE, Pierre |  MÉLITE OU LES FAUSSES LETTRES, COMÉDIE | 1633 |  Comédie | moeurs françaises |  5 act. | vers | 1621-1630
CP_MENTEUR |  CORNEILLE, Pierre |  LE MENTEUR, COMÉDIE | 1644 |  Comédie | moeurs françaises |  5 act. | vers | 1641-1650
CP_PULCHERIE |  CORNEILLE, Pierre |  PULCHÉRIE, COMÉDIE HÉROÏQUE | 1673 |  Comédie héroïque | histoire chrétienne |  5 act. | vers | 1671-1680
CP_SUITEMENTEUR |  CORNEILLE, Pierre |  LA SUITE DU MENTEUR, COMÉDIE | 1645 |  Comédie | moeurs françaises |  5 act. | vers | 1641-1650
CP_SUIVANTE |  CORNEILLE, Pierre |  LA SUIVANTE, COMÉDIE | 1637 |  Comédie | moeurs françaises |  5 act. | vers | 1631-1640
CP_TITE |  CORNEILLE, Pierre |  TITE ET BÉRÉNICE, COMÉDIE HEROÏQUE | 1671 |  Comédie héroïque | histoire romaine |  5 act. | vers | 1661-1670
CP_VEUVE34 |  CORNEILLE, Pierre |  LA VEUVE OU LE TRAÎTRE TRAHI, COMÉDIE | 1634 |  Comédie | moeurs françaises |  5 act. | vers | 1631-1640
CT_AMOURALAMODE |  CORNEILLE, Thomas |  L’AMOUR À LA MODE, COMÉDIE. | 1651 |  Comédie | moeurs espagnoles |  5 act. | vers | 1651-1660
CT_CHARMEDELAVOIX |  CORNEILLE, Thomas |  LE CHARME DE LA VOIX, COMÉDIE | 1658 |  Comédie | moeurs italiennes |  5 act. | vers | 1651-1660
CT_COMTESSEORGUEIL |  CORNEILLE, Thomas |  LA COMTESSE D’ORGUEIL, COMÉDIE | 1690† |  Comédie | moeurs françaises |  5 act. | vers | 1651-1660
CT_DOMBERTRANDECIGARRAL |  CORNEILLE, Thomas |  DON BERTRAN DE CIGARRAL, COMÉDIE | 1709* |  Comédie | moeurs espagnoles |  5 act. | vers | 1651-1660
CT_DOMCESARDAVALOS |  CORNEILLE, Thomas |  DON CÉSAR D’AVALOS, COMÉDIE. | 1661 |  Comédie | moeurs espagnoles |  5 act. | vers | 1671-1680
CT_FEINTASTROLOGUE |  CORNEILLE, Thomas |  LE FEINT ASTROLOGUE, COMÉDIE | 1651 |  Comédie | moeurs françaises |  5 act. | vers | 1651-1660
CT_FESTINPIERRE |  CORNEILLE, Thomas |  LE FESTIN DE PIERRE, COMÉDIE | 1677 |  Comédie | moeurs espagnoles |  5 act. | vers | 1671-1680
CT_GALANTDOUBLE |  CORNEILLE, Thomas |  LE GALANT DOUBLÉ, COMÉDIE. | 1659 |  Comédie | moeurs espagnoles |  5 act. | vers | 1651-1660
CT_GEOLIERDESOISMEME |  CORNEILLE, Thomas |  LE GEÔLIER DE SOI-MÊME, COMÉDIE. | 1655 |  Comédie | moeurs italiennes |  5 act. | vers | 1651-1660
CT_ILLUSTRESENNEMIS |  CORNEILLE, Thomas |  LES ILLUSTRES ENNEMIS, COMÉDIE | 1657 |  Comédie | moeurs espagnoles |  5 act. | vers | 1651-1660
CT_INCONNU |  CORNEILLE, Thomas |  L’INCONNU, COMÉDIE. | 1675 |  Comédie | moeurs françaises |  5 act. | vers | 1671-1680
M_AMPHITRYON |  MOLIÈRE |  AMPHITRYON, COMÉDIE | 1668 |  Comédie | mythe grec |  3 act., prol. | vers | 1661-1670
M_DEPITAMOUREUX |  MOLIÈRE |  LE DÉPIT AMOUREUX | 1656 |  Comédie | moeurs françaises |  5 act. | vers | 1651-1660
M_DOMGARCIEDENAVARRE |  MOLIERE |  DON GARCIE DE NAVARRE, COMÉDIE | 1682‡ |  Comédie | moeurs espagnoles |  5 act. | vers | 1661-1670
M_ECOLEDESFEMMES |  MOLIÈRE |  L’ÉCOLE DES FEMMES, COMÉDIE. | 1663 |  Comédie | moeurs françaises |  5 act. | vers | 1661-1670
M_ETOURDI |  MOLIÈRE |  L’ÉTOURDI ou LES CONTRE-TEMPS, COMÉDIE | 1663 |  Comédie | moeurs françaises |  5 act. | vers | 1661-1670
M_FEMMESSAVANTES |  MOLIÈRE |  LES FEMMES SAVANTES, COMÉDIE | 1672 |  Comédie | moeurs françaises |  5 act. | vers | 1671-1680
M_MISANTHROPE |  MOLIÈRE |  LE MISANTHROPE ou L’ATRABILAIRE AMOUREUX, COMÉDIE | 1667 |  Comédie | moeurs françaises |  5 act. | vers | 1661-1670
M_TARTUFFE |  MOLIÈRE |  LE TARTUFFE ou L’IMPOSTEUR, COMÉDIE | 1669 |  Comédie | moeurs françaises |  5 act. | vers | 1661-1670
O_ABSENTCHEZSOI |  OUVILLE, Antoine le Métel |  L’ABSENT CHEZ SOI | 1643 |  Comédie | moeurs françaises |  5 act. | vers | 1641-1650
O_FAUSSESVERITES |  OUVILLE, Antoine le Métel |  LES FAUSSES VÉRITÉS, COMÉDIE | 1643 |  Comédie | moeurs françaises |  5 act. | vers | 1641-1650
O_SOUPCONS |  OUVILLE, Antoine le Métel |  LES SOUPÇONS SUR LES APPARENCES, COMÉDIE | 1650 |  héroïco-comédie | moeurs françaises |  5 act. | vers | 1641-1650
R_BELLEALPHREDE |  ROTROU, Jean |  LA BELLE ALPHRÈDE, COMÉDIE | 1639 |  Comédie | moeurs arabes |  5 act. | vers | 1631-1640
R_CAPTIFS |  ROTROU, Jean |  LES CAPTIFS OU LES ESCLAVES, COMÉDIE | 1640 |  Comédie | moeurs françaises |  5 act. | vers | 1631-1640
R_SOSIES |  ROTROU, Jean |  LES SOSIES, COMÉDIE | 1638 |  Comédie | mythe grec |  5 act. | vers | 1631-1640
S_DOMJAPHETDARMENIE |  SCARRON, Paul |  DON JAPHET D’ARMÉNIE, COMÉDIE. | 1653 |  Comédie | moeurs françaises |  5 act. | vers | 1651-1660
S_GARDIENDESOIMEME |  SCARRON, Paul |  LE GARDIEN DE SOI-MÊME | 1654 |  Comédie | moeurs italiennes |  5 act. | vers | 1651-1660
S_HERITIERRIDICULE |  SCARRON, Paul |  L’HÉRITIER RIDICULE OU LA DAME INTÉRESSÉE.. | 1650 |  Comédie | moeurs espagnoles |  5 act. | vers | 1641-1650
S_JODELET |  SCARRON, Paul |  LE JODELET OU LE MAÎTRE VALET, COMÉDIE. | 1648 |  Comédie | moeurs françaises |  5 act. | vers | 1641-1650
S_JODELETDUELISTE |  SCARRON, Paul |  LE JODELET DUELLISTE, COMÉDIE. | 1646 |  Comédie | moeurs françaises |  5 act. | vers | 1641-1650
S_MARQUISRIDICULE |  SCARRON, Paul |  LE MARQUIS RIDICULE, OU LA COMTESSE faite à la hâte. COMÉDIE. | 1656 |  Comédie | moeurs espagnoles |  5 act. | vers | 1651-1660

† The edition used as base is from 1690, but the play dates back to 1670.

 The edition used as base is from 1709, but the play dates back to 1652.

‡ Created in 1661, but published 1680.

##  Appendix B Annotated texts from the Frantext base

The following texts, from ATILF-CNRS and Université de Lorraine [1998-2018],
were used as complementary data for lemmatisation training.

id | auteur | titre | date | place | publisher | N.tok
---|---|---|---|---|---|---
K639:1890:52916 | LOTI, Pierre | Le Roman d’un enfant | 1891 | Paris | Calmann-Levy | 61,417
K934:1898:35797 | ROSTAND Edmond | Cyrano de Bergerac | 1898 | Paris | Fasquelle | 47,213
K999:1899:45606 | GOURMONT Remy de | Esthétique de la langue française… | 1899 | NA | Soc. Mercure de Fr. | 53,640
L233:1895:13272 | JARRY Alfred |  Ubu Roi, Œuvres complètes, t. 4. | s.d. | Monte-Carlo | éd. Du Livre | 17,672
L266:1905:59455 | POINCARÉ Henri | La Valeur de la science | 1905 | Paris | Flammarion | 66,125
L433:1882:31745 | BECQUE Henry |  Les Corbeaux, Théâtre complet, t. 2 | 1922 | s.p. | Fasquelle | 38.813
L486:1884:68155 | HUYSMANS Joris-Karl | À rebours | 1907 | Paris | Fasquelle | 79,058
L499:1901:63336 | JAURÈS Jean | Études socialistes | 1902 | Paris | Ollendorf | 71,159
L784:1908:100993 | LEROUX Gaston | Le Parfum de la dame en noir | 1908 | Paris | L’Illustration | N.tok
L846:1857:68636 | ABOUT Edmond | Le Roi des montagnes | 1857 | s.p. | Hachette | 79,720
L884:1867:20871 | MEILHAC Henri et Ludovic HALÉVY | La Vie parisienne | 1867 | Paris | M. Levy | 27,381
M223:1873:70999 | VERNE Jules | Le Tour du monde en quatre-vingts jours | s.d. | Paris | Hetzel | 84,925
M289:1874:69484 | FROMENTIN Eugène | Un été dans le Sahara | 1877 | Paris | Plon | 80,612
M362:1825:111416 | BRILLAT-SAVARIN Jean-Anthelme | Physiologie du goût… | 1847 | Paris | Charpentier | 127,968
M374:1830:28471 | FOURIER Charles | Le Nouveau monde industriel… | 1830 | Paris | Bossange | 32,566
M425:1794:4621 | CHÉNIER André |  L’Invention, Œuvres complètes, t. 2. | 1910 | Paris | Delagrave | 5,473
M433:1807:57291 | STAËL Germaine de | Corinne ou l’Italie : t. 1 | 1807 | s.p. | Peltier | 65,012
M464:1801:72408 | DESTUTT DE TRACY | Élémens d’idéologie, 1 | 1804 | Paris | Courcier | 80,433
M468:1803:62746 | KRÜDENER Barbara Juliane von | Valérie | 1840 | Paris | Charpentier | 73,463
M473:1809:82847 | LAMARCK Jean-Baptiste | Philosophie zoologique : t. 1 | 1809 | s.p. | Dentu | 95,111
M492:1805:123259 | CUVIER Georges | Leçons d’anatomie comparée : t. 1 | 1805 | Paris | Baudouin | 138,674
M528:1798:12861 | GUILBERT DE PIXERÉCOURT | Victor ou l’Enfant de la forêt | 1798 | Paris | Barba | 16,878
M548:1832:199497 | SAY Jean-Baptiste | Traité d’économie politique | 1841 | Paris | Guillaumin | 223,038
M622:1789:24158 | SIEYÈS Emmanuel | Qu’est-ce que le Tiers état ? | 1888 | Paris | Soc. Hist. Rév. Fr. | 27,002
M629:1792:31251 | FLORIAN Jean-Pierre | Fables | 1792 | Paris | Didot | 37,172
M893:1869:61374 | GONCOURT Edmond et Jules de | Madame Gervaisais | 1876 | s.p. | Charpentier | 71,174
M939:1852:84555 | COMTE Auguste | Catéchisme positiviste… | 1909 | Paris | Garnier | 95,589
N245:1838:32486 | HUGO Victor |  Ruy Blas, Œuvres complètes. Théâtre, 3 | 1905 | Paris | Ollendorff | 41,487
N268:1802:63535 | BONALD Louis de | Législation primitive… t. 1 | 1802 | Paris | Le Clère | 72,539
N429:1778:64388 | BUFFON Georges-Louis de |  Des époques de la nature, Hist. Natur., t. 5. | 1778 | Paris | Impr. Royale | 70,866
P556:1872:177870 | VIOLLET-LE-DUC Eugène | Entretiens sur l’architecture : t. 2 | 1872 | Paris | A. Morel | 199,695
Q454:1784:53428 | RÉTIF DE LA BRETONNE Nicolas | La Paysanne pervertie…t. 1 | 1784 | s.p. | Vve Duchesne | 64,424

##  Appendix C Out-of-domain texts

Here follows the list of the texts that were sampled for building the out-of-
domain test set. Spelling was modernised when necessary.

Theatre texts were sampled from Fièvre [2007], with the exception of:

AUBIGNAC_PUCELLE

sample transcribed and modernised from the edition Paris, 1642, available on
Gallica.

GRINGORE_SAINT-LOUIS

sample transcribed and modernised from the Œuvres complètes, éd. Ch.
d’Héricault et A. Montaiglon, Paris, 1858-1877, available on Gallica.

ANOUILH_MEDEE

sample transcribed from the Nouvelles pièces noires, Paris, La Table Ronde,
1976.

GIRAUDOUX_ELECTRE

sample transcribed from the Théâtre complet, éd. J. Body, Paris, Gallimard,
1982.

CESAIRE_CHRISTOPHE

sample transcribed from Poésie, Théâtre, Essais et Discours, éd. Albert James
Arnold, Paris, CNRS Editions, 2013.

For the non-dramatic texts, the main sources, as shown in the table, are:

ELEC

Éditions en ligne de l’École des chartes, http://elec.enc.sorbonne.fr/.

GALL

Gallica, Bibliothèque nationale de France, https://gallica.bnf.fr.

WS

Wikisource: la bibliothèque libre, https://fr.wikisource.org/.

Other online editions were occasionnaly used:

Correspondance d’Isabelle de Charrière, éd. Suzan van Dijk and Madeleine van
Strien-Chardonneau, https://charriere.huygens.knaw.nl/.

Les Nouvelles Nouvelles (1663) par Jean Donneau de Visé, éd. Claude Bourqui et
Christophe Schuwey, http://www.unifr.ch/nouvellesnouvelles/.

Œuvres de Frédéric le Grand - Werke Friedrichs des Großen Digitale Ausgabe der
Universitätsbibliothek Trier, dir. Hans-Ulrich Seifert, Trier,
http://friedrich.uni-trier.de/.

Testaments de Poilus (1914-1918): transcription collaborative, dir. Florence
Clavaud, 2018-…, https://testaments-de-poilus.huma-num.fr.

The sample from MONTEGUT_ISMENE was transcribed and modernised from the
edition Paris, 1768, available on Google Books.

###  C.1 Theatre

id |  author |  title | date |  genre | inspiration |  structure | type | period
---|---|---|---|---|---|---|---|---
ANONYME_PARDONNEUR |  anonyme |  FARCE NOUVELLE TRÈS BONNE ET FORT JOYEUSE À TROIS PERSONNAGES, FARCE | 1500 c. |  Farce | Sermon joyeux |  1 act. | vers | 1501-1600
ANONYME_PONTAUXANES |  anonyme |  FARCE NOUVELLE FORT JOYEUSE DU PONT AUX ÂNES | 1500 c. |  Farce | Sermon joyeux |  1 act. | vers | 1501-1600
ANONYME_RESURRECTIONJENINLANDORE |  anonyme |  FARCE NOUVELLE TRÈS BONNE ET FORT JOYEUSE DE LA RESURRECTION DE JENIN LANDORE, FARCE | 1500 c. |  Farce | Sermon joyeux |  1 act. | vers | 1501-1600
ANONYME_SERMONJOYEUX |  anonyme |  SERMON JOYEUX DE BIEN BOIRE, FARCE | 1500 c. |  Farce | Sermon joyeux |  1 act. | vers | 1501-1600
BEZE_ABRAHAM |  BEZE, Théodore |  ABRAHAM SACRIFIANT, TRAGÉDIE. | 1550 |  Tragédie | bible |  1 act. | vers | 1541-1550
GRINGORE_SAINT-LOUIS |  GRINGORE, Pierre |  Mystère de Saint Louis | 1514 |  mystère | histoire médiévale |  8 livres | vers | 1511-1520
JODELLE_CLEOPATRE |  JODELLE, Étienne |  CLÉOPÂTRE CAPTIVE, TRAGÉDIE. | 1574 |  Tragédie | histoire romaine |  5 act. | vers | 1571-1580
JODELLE_DIDON |  JODELLE, Étienne |  DIDON SE SACRIFIANT, TRAGÉDIE. | 1574 |  Tragédie | mythe grec |  5 act. | vers | 1571-1580
JODELLE_EUGENE |  JODELLE, Étienne |  L’EUGÈNE, COMÉDIE. | 1574 |  Comédie | moeurs françaises |  5 act. | vers | 1571-1580
TURNEBE_CONTENTS |  TURNÈBE, Odet de |  LES CONTENTS, COMÉDIE NOUVELLE EN PROSE FRANÇAISE | 1584 |  Comédie | moeurs françaises |  5 act. | prose | 1581-1590
AUBIGNAC_PUCELLE |  AUBIGNAC, François Hédelin |  La Pucelle d’Orléans | 1642 |  Tragédie | histoire médiévale |  | prose | 1641-1650
CHAMPREPUS_ULYSSE |  CHAMPREPUS, Jacques de |  ULYSSE, TRAGÉDIE. | 1603 |  Tragédie | mythe grec |  5 act. | vers | 1601-1610
CHAPPUZEAU_ARMETZAR |  Samuel Chappuzeau (1625-1701) |  ARMETZAR OU LES AMIS ENNEMIS, TRAGICOMÉDIE. | 1656 |  Tragi-comédie | histoire turque |  5 act. | vers | 1651-1660
DESHOULIERES_GENSERIC |  DESHOULIÈRES, Antoinette du Ligier de la Garde |  GENSERIC, TRAGÉDIE | 1680 |  Tragédie | histoire romaine |  5 act. | vers | 1671-1680
DESJARDINS_FAVORI |  DESJARDINS, Marie-Catherine-Hortense dite de Villedieu |  LE FAVORI, TRAGICOMÉDIE. | 1665 |  Tragi-comédie | moeurs espagnoles |  5 act. | vers | 1661-1670
DURANT_OISIVETE |  DURANT, Catherine |  OISIVITÉ EST MÈRE DE TOUS LES VICES, PROVERBE. | 1699 |  Proverbe | moeurs françaises |  1 act. | prose | 1691-1700
MATHIEU_MAGICIENNEETRANGERE |  MATTHIEU, Pierre |  LA MAGICIENNE ÉTRANGÈRE, TRAGÉDIE. | 1617 |  Tragédie | histoire française |  4 act. | vers | 1611-1620
SCUDERY_LIGDAMONELIDIAS |  SCUDERY, Georges de |  LIGDAMON ET LIDIAS, TRAGI-COMÉDIE | 1631 |  Tragi-comédie | histoire médiévale |  5 act. | vers | 1631-1640
URFE_SYLVANIRE |  URFÉ, Honoré d’ |  SYLVANIRE ou la MORTE VIVE, FABLE BOCAGÈRE | 1627 |  Pastorale héroïque | pastorale |  5 act., un prologue | vers | 1621-1630
VONDREBECK-ALARD_FORCESDELAMOUR |  VONDREBECK, Maurice et ALARD, Charles |  LES FORCES DE L’AMOUR ET DE LA MAGIE, DIVERTISSEMENT | 1678 |  Divertissement | moeurs françaises |  3 act. | prose | 1671-1680
BERGASSE_JOURNEEDESDUPES_1790 |  BENSERADE, Isaac de |  LA JOURNÉE DES DUPES, TRAGI-COMÉDIE | 1790 |  Tragi-comédie | histoire française |  4 act. | prose | 1781-1790
BIEVRE_VERCINGENTORIXE |  BIÈVRE, François-Georges Mareschal de |  VERCINGENTORIXE, TRAGÉDIE. | 1770 |  Tragédie | histoire française |  1 act. | vers | 1761-1770
BOISSY_VIEESTUNSONGE |  BOISSY, Louis de |  LA VIE EST UN SONGE, COMÉDIE-HÉROÏQUE. | 1732 |  Comédie héroïque | moeurs polonaises |  5 act. | vers | 1731-1740
DANCOURT_SANCHO |  DANCOURT, Florent CARTON dit |  SANCHO PANÇA, GOUVERNEUR, COMÉDIE | 1712 |  Comédie | moeurs françaises |  5 act. | vers | 1711-1720
DIDEROT_FILSNATUREL |  DIDEROT, Denis |  LE FILS NATUREL ou Les ÉPREUVES DE LA VERTU. | 1757 |  Tragi-comédie | moeurs françaises |  5 act. | prose | 1751-1760
DUFRESNY_MARIAGEFAITETROMPU |  DUFRESNY, Charles |  LE MARIAGE FAIT ET ROMPU, COMÉDIE | 1721 |  Comédie | moeurs françaises |  3 act. | vers | 1721-1730
GUDIN_LOTHAIRE |  GUDIN de la BRENELLERIE, Paul-Philippe |  LOTHAIRE, ROI DE LORRAINE, TRAGÉDIE | 1759 |  Tragédie | histoire française |  5 act. | vers | 1751-1760
MARIVAUX_PEREPRUDENT |  MARIVAUX, Pierre de |  LE PÈRE PRUDENT ET ÉQUITABLE, COMÉDIE | 1712 |  Comédie | moeurs françaises |  1 act. | vers | 1711-1720
REGNARD_MENECHMES |  REGNARD, Jean-François |  LES MÉNECHMES, ou LES JUMEAUX, COMÉDIE | 1705 |  Comédie | moeurs françaises |  5 act. | vers | 1701-1710
VOLTAIRE_MORTDECESAR |  VOLTAIRE |  LA MORT DE CÉSAR, TRAGÉDIE EN TROIS ACTES | 1736 |  Tragédie | histoire romaine |  3 act. | vers | 1731-1740
ALLAIS_BONBOUGRE |  ALLAIS, Alphonse |  LE PAUVRE BOUGRE ET LE BON GÉNIE, FÉÉRIE EN UN ACTE. | 1889 |  Féérie | moeurs françaises |  1 act. | prose | 1881-1890
AUDE_ECOLETRAGIQUE |  AUDE, Joseph |  L’ÉCOLE TRAGIQUE, OU CADET ROUSSEL MAITRE DE DÉCLAMATION COMÉDIE | 1802 |  Comédie | moeurs françaises |  1 act. | mixte | 1801-1810
BANVILLE_ANCIENPIERROT |  BANVILLE, Théodore de |  ANCIEN PIERROT, MONOLOGUE. | 1877 |  Monologue | moeurs françaises |  1 act. | prose | 1871-1880
BONNETAIN_APRESLEDIVORCE |  BONNETAIN, Paul |  APRÈS LE DIVORCE, PIÈCE | 1890 |  Comédie | moeurs françaises |  1 act. | prose | 1881-1890
CONSTANT_WALLSTEIN |  CONSTANT, Benjamin |  WALLSTEIN, TRAGÉDIE | 1809 |  Tragédie | histoire allemande |  5 act. | vers | 1801-1810
DUMAS_DONJUAN |  DUMAS, Alexandre |  DON JUAN DE MARANA, MYSTÈRE | 1836 |  Drame | moeurs espagnoles |  5 act. | mixte | 1831-1840
GENLIS_BELLEETLABETE |  GENLIS, Stéphanie-Félicité Du Crest de |  LA BELLE ET LA BÊTE, COMÉDIE | 1829 |  Comédie | moeurs françaises |  2 act. | prose | 1821-1830
HUGO_HERNANI |  HUGO, Victor |  HERNANI, OU L’HONNEUR CASTILLAN | 1830 |  Drame | histoire espagnole |  5 act. | vers | 1821-1830
JARRY_UBUROI |  JARRY, Alfred |  UBU ROI, DRAME | 1896 |  Drame | Fantaisie historique |  5 act. | mixte | 1881-1890
SAND_MOLIERE |  SAND, George |  MOLIÈRE, DRAME. | 1851 |  Comédie | histoire littéraire |  2 act. | prose | 1851-1860
ANOUILH_MEDEE |  ANOUILH, Jean |  Médée | 1946 |  tragédie | mythe grec |  NA | prose | 1941-1950
BERNARDT_PARTIEDEBRIDGE |  BERNARD, Tristan |  LA PARTIE DE BRIDGE, COMÉDIE | 1930 |  Comédie | moeurs françaises |  1 act. | prose | 1921-1930
BERTON_HOMMEQUIATUELAMORT |  BERTON, René |  L’HOMME QUI A TUÉ LA MORT, DRAME | 1928 |  Pièce dramatique | moeurs françaises |  2 act. | prose | 1921-1930
CESAIRE_CHRISTOPHE |  CÉSAIRE, Aimé |  La Tragédie du roi Christophe | 1963 |  tragédie | histoire haïtienne |  3 act. | prose | 1961-1970
COURTELINE_MONSIEURBADIN |  COURTELINE, Georges |  MONSIEUR BADIN, COMÉDIE. | 1904 |  Saynète | moeurs françaises |  1 act. | prose | 1891-1900
GIRAUDOUX_ELECTRE |  GIRAUDOUX, Jean |  Électre | 1937 |  tragédie bourgeoise | mythe grec |  2 act. | prose | 1931-1940
HUGUES_CENDRILLON |  HUGUES, Clovis |  CENDRILLON, COMÉDIE. | 1906 |  Comédie | conte de fées |  1 act. | vers | 1901-1910
HUGUES_TYL |  HUGUES, Clovis |  TYL L’ESPIEGLE, COMÉDIE. | 1906 |  Comédie | moeurs flamande |  1 act. | vers | 1901-1910
LESENNE_REVEILCORNEILLE |  LE SENNE, Camille |  LE RÉVEIL DE CORNEILLE, POÈME DRAMATIQUE. | 1916 |  Dialogue des morts | histoire littéraire |  1 act. | vers | 1911-1920
RENARD_MAITRESSE |  RENARD, Jules |  LA MAÎTRESSE, COMÉDIE. | 1927 |  Comédie | moeurs françaises |  2 act. | prose | 1921-1930

###  C.2 Varia

ID |  auteur |  titre | genre | forme | date | source |  ed.
---|---|---|---|---|---|---|---
ANONYME_PAIX-BERGERAC |  Chancellerie royale |  Paix de Bergerac. Édit de Poitiers. Poitiers, septembre 1577. | légal | prose | 1577 | ELEC |  éd. Bernard Barbiche, École nationale des chartes.
ANONYME_REITRES |  anonyme |  Le vray discours sur la route et admirable desconfiture des Reistres… | discours | prose | 1587 | WS |  éd. Éd. Fournier, Var. hist. et litt., t. 9
ARBEAU_BELLE |  Thoinot Arbeau |  Belle qui tiens ma vie | chanson | vers | 1588 | WS |  manq.
FRANCOISPREMIER_CORRESP |  François Ier |  La correspondance du chancelier Antoine Du Bourg (1535-1538) | correspondance | prose | 1537 | ELEC |  éd. Olivier Poncet
LENONCOURT-MARTIN_HYPNEROTOMACHIE |  Robert de Lenoncourt, Jean Martin (trads) |  Hypnérotomachie, ou Discours du songe de Poliphile… | discours | prose | 1546 | WS |  Jacques Kerver, 1546
LIEBAUT_MISERES |  Liébaut,Nicole Estienne |  Les Misères de la Femme mariée… | poésie morale | vers | 1587 | WS |  éd. Éd. Fournier, Var. hist. et litt., 3, 1855.
LOUISELABE_ELEGIE |  Louise Labé |  Élégie I | poésie lyrique | vers | 1555 | WS |  éd. Charles Boy, 1887.
MARGUERITENAVARRE_MIROIR |  Marguerite de Navarre |  Miroir de l’âme pécheresse | miroir | vers | 1529 | WS |  éd. Félix Frank, 1873
RABELAIS_PANTAGRUEL |  Rabelais |  Pantagruel | roman | prose | 1532 | WS |  éd. Marty-Laveaux, 1868
RONSARD_SONNETS |  Ronsard, Pierre de |  Je plante en ta faveur cet arbre de Cybèle | poésie | vers | 1578 | WS |  éd. Roger Sorg,1921
ANNEROHAN_PORTRAIT |  Anne de Rohan-Soubise |  Portrait de feue la duchesse de Nevers… | poésie | vers | 1629 | GALL |  éd. Éd. de Barthélemy, Paris, 1862
ANONYME_AIRABOIRE |  anonyme |  Air à boire | chanson | vers | 16… | WS |  La Chanson française du XVe au XXe siècle, éd. Jean Gillequin, 1910.
ANONYME_ARREST |  anonyme |  Arrest notable donné au profit des femmes contre l’impuissance des maris | légal | prose | 1626 | WS |  éd. Éd. Fournier, Var. hist. et litt., 6, 1856
BOILEAU_SATIRE |  Nicolas Boileau |  satires (satire I) | satire | vers | 1660 | WS |  Paris, 1872
DONNEAU_NOUVELLES |  Jean Donneau de Visé |  Nouvelles nouvelles | actualité | prose | 1663 | other |  éd. Bourqui & Schuwey
ELISABETHBOHEME_CORRESPONDANCE |  Élisabeth de Bohême |  Correspondance avec René Descartes | correspondance | prose | 1643 | WS |  manq.
PERRAULT_PETIT-CHAPERON |  Charles Perrault |  Le petit Chaperon rouge | conte | prose | 1697 | WS |  Paris, 1902
POULLAIN_EGALITE |  François Poullain de La Barre |  De l’Égalité des deux sexes, Discours phisique et moral | discours | prose | 1679 | GALL |  Paris, 1679
SCUDERY_ARTAMENE |  Madeleine de Scudéry |  Artamène ou le Grand Cyrus | roman | prose | 1654 | WS |  Auguste Courbé, 1654.
VERVILLE_UN-JOUR |  Béroalde de Verville |  « Un jour reconnaissant que je suis incapable » | poésie | vers | av. 1626 | WS |  manq.
ANONYME_DECLARATION |  anonyme |  Déclaration des Droits de l’Homme et du Citoyen de 1793 | légal | prose | 1793 | WS |  manq.
CHARRIERE_LETTRE |  Isabelle de Charrière |  lettre | correspondance | prose | 1755 | other |  éd. S. van Dijk et M. van Strien-Chardonneau
CHATELET_DISCOURS |  Émilie du Châtelet |  Discours sur le bonheur | discours | prose | 1744 | WS |  éd. Bourlet de Vauxcelles, 1796
CHENIER_ODE |  André Chénier |  Ode IX (à Charlotte Corday) | poésie | vers | 1793 | WS |  éd. H. de Latouche, 1819
FREDERICDEUX_DIVINEEMILIE |  Frédéric II de Prusse |  À la Divine Émilie | poésie | vers | 1737 | FG |  Œuvres de Frédéric le Grand, Berlin, 1850
MERICOURT_DISCOURS |  Méricourt, Théroigne de |  Discours devant la Société fraternelle des Minimes, 25 mars 1792 | discours | prose | 1792 | GALL |  Paris, 1792
MONTEGUT_ISMENE |  Jeanne de Montégut-Ségla |  Ismene, élégie | poésie | vers | 1739 | other |  Paris, 1768
ROUGET_MARSEILLAISE |  Rouget de Lisle |  La Marseillaise | chanson | vers | 1792 | WS |  manq.
TENCIN_SIEGE |  Madame de Tencin |  Le Siège de Calais | roman | prose | 1739 | WS |  éd. L.-S. Auger, Paris, 1820.
VOLTAIRE_ZADIG |  Voltaire |  Zadig | nouvelle | prose | 1747 | WS |  Paris, 1877.
CMH_PV |  Commission des monuments historiques |  PV 29 février 1884 | administratif | prose | 1884 | ELEC |  éd. J.-D. Pariset.
DELESCLUZE_LETTRE |  Henri Delescluze |  Lettre à Charles Delescluse (Carnets de prison) | correspondance | prose | 1851 | ELEC |  éd. Chr. Nougaret et Fl.Clavaud
DEROULEDE_CHANTS |  Paul Déroulède |  Nouveaux chants du soldat | chanson | vers | 1883 | GALL |  Paris, 1883
DESBORDESVALMORE_INSOMNIE |  Marceline Desbordes-Valmore |  L’Insomnie | poésie | vers | 1860 | WS |  Paris, 1860.
HEREDIA_EPEE |  Hérédia, José Maria de |  L’Épée | poésie | vers | 1893 | WS |  Paris, 1893.
MERIMEE_ACADEMIE |  Prosper Mérimée |  Discours de réception à l’Académie française | discours | prose | 1845 | other |  Site de l’Académie française
POTTIER_INTERNATIONALE |  Eugène Pottier |  L’Internationale | chanson | vers | 1871 | WS |  Paris, 1908.
SAND_MARIANNE |  George Sand |  Marianne | roman | prose | 1877 | WS |  Paris, 1877
SEGUR_BOSSU |  Comtesse de Ségur |  François le Bossu | roman | prose | 1864 | WS |  Paris, 1901
STAEL_ALLEMAGNE |  Mme de Staël |  De l’Allemagne | essai | prose | 1810 | WS |  Paris, 1814
ANONYME_CONSCRITS |  anonyme |  Les Conscrits insoumis | chanson | vers | 1902 | WS |  manq.
BLUM_FRONTPOP |  Léon Blum |  « Nous sommes un gouvernement de front populaire » | discours | prose | 1936 | GALL |  J. O.de la Rép. fr. 7 juin 1936
BRASILLACH_JEANNE |  Robert Brasillach |  Le Procès de Jeanne d’Arc | étude historique | prose | 1941 | GALL |  Paris, 1941
BRIMONT_MIRAGES |  Renée de Brimont |  Mirages | poésie | vers | 1919 | WS |  Paris, 1919
COLETTE_MAISON |  Colette |  La Maison de Claudine | nouvelle | prose | 1922 | WS |  manq.
LAHIRE_ROUE |  Jean de La Hire |  La Roue fulgurante | feuilleton SF | prose | 1908 | GALL |  Le Matin, 11 avril 1908
LASTEYRIE_TESTAMENT |  Gaspard Louis Guy de Lasteyrie marquis du Saillant |  Testament | légal | prose | 1915 | other |  Testaments de Poilus
LONDRES_BAGNE |  Albert Londres |  Au bagne | reportage | prose | 1924 | WS |  Paris, 1924
NOAILLES_COEUR |  Anna de Noailles |  Le Coeur innombrable | poésie | vers | 1901 | WS |  Paris, 1901
RACHILDE_GRENOUILLES |  Rachilde |  Le Tueur de Grenouilles | nouvelle | prose | 1900 | WS |  Mercure de France