# ForumOccitania: a Corpus of User-Generated Content for Multiple Occitan Varieties

Oriane Nédey, Juliette Janès, Rachel Bawden, Thibault Clérice, Benoît Sagot

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Oriane Nédey, Juliette Janès, Rachel Bawden, Thibault Clérice, Benoît Sagot. ForumOccitania: a Corpus of User-Generated Content for Multiple Occitan Varieties. 2025. hal-05413035

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# ForumOccitania: a Corpus of User-Generated Content for Multiple Occitan Varieties

Oriane Nédey, Juliette Janès, Rachel Bawden, Thibault Clérice, Benoît Sagot

Inria

Paris, France

{name.surname}@inria.fr

## Abstract

We introduce FORUMOCCITANIA, a new Occitan corpus of posts from an online forum, covering a range of topics and dialects. While some existing datasets for this low-resource language include labels of varieties within the dialect continuum, we go one step further by providing metadata pertaining to sociolinguistic factors of language variation (dialect, geographical location, age, proficiency), extracted from self-declared user profiles. We carry out statistical and qualitative analyses, as well as preliminary experiments on unsupervised dialect identification. Our results show that (i) most of the contents is written in Occitan, with the classical spelling conventions, and by young new speakers, (ii) posts display a strong presence of dialectal features from four major Occitan varieties (Lemosin, Lengadocian, Gascon, Provençau), and (iii) a simple topic modelling approach introduced by Kuparinen and Scherrer (2024) effectively detects salient features of these four varieties, but also reveals finer-grained diatopical variation tendencies.

**Keywords:** corpus, Occitan, low-resource language, dialect variation, language identification, user-generated content

## 1. Introduction

As increasing number of languages are supported by NLP systems (Costa-jussà et al., 2024; Kudugunta et al., 2023), research is also turning towards the processing of closely related languages, including non-standardised language varieties (Joshi et al., 2025). The term *dialect continuum* is attributed to some of these languages that are characterised by important internal variation at a diatopical (i.e. geographical) level (Chambers and Trudgill, 1998).<fnref n="1" /> Current NLP systems tend to lack robustness to dialectal variation (Faisal et al., 2024), and tasks like variety identification are of particular importance for the development of tools supporting these languages (Scherrer et al., 2025). In this paper, we focus on Occitan, a dialect continuum that extends through a large area in the south of France as well as some neighbouring valleys of Spain and Italy. While linguistic variation operates rather continuously at the lowest geographical level, linguistic studies have shown the presence of larger groupings of localities through the use of isoglosses, leading to precise delimitations of varieties within this continuum, as in Figure 1.

Previous work on Occitan has led to the creation of several datasets and tools for NLP (Miletić and Scherrer, 2022; Vergez-Couret et al., 2024; Hop-

<figure><img src="image_2.png" /><figcaption>Figure 1: The dialect classification for Occitan proposed in Sibille (2024).</figcaption></figure>

ton and Aepli, 2024; Morcillo et al., 2024). While some corpora include dialect annotations (Miletić et al., 2020), they are characterised by a large class imbalance, and only use labels corresponding to coarse-grained dialectal groupings (except for three very specific, localised varieties), thereby limiting the possibilities of research to tackle challenges related to the Occitan linguistic continuum.

The main contribution of this paper is the introduction of FORUMOCCITANIA, a novel Occitan corpus annotated with dialect labels and additional metadata, with the purpose of enabling both corpus-based linguistic studies and dialectal NLP research to account for multiple sociolinguistic dimensions in projects with Occitan data. The corpus is composed of threads from an online discussion forum,<fnref n="2" />

<footnote n="1">¹While we are aware that the distinction between *language* and *dialect* is mostly a political one, for clarity reasons, we will preferably use the word *language* to refer to the largest linguistic entities (French, Occitan), and the words *dialect* or *variety* (interchangeably) to refer to regional or local linguistic entities within a dialectal continuum.</footnote>

<footnote n="2">²https://occitania.forumactif.com/</footnote>
active between 2007 and 2012, covering multiple themes (news, music, linguistics, food, technology, books, etc.) and mainly involving new speakers of Occitan from diverse places in the Occitan-speaking area (and beyond). Through a mapping to the user profiles, we include annotations of dialect, age, Occitan proficiency, and geographical location. Moreover, as multiple languages are present in the forum (including code-switching), we provide language predictions. In order to preserve user privacy in the final dataset, we apply an anonymisation pipeline to both user metadata and post content.

We evaluate the dataset quality both with a manual analysis focusing on the presence of dialectal features and with preliminary experiments on unsupervised dialect identification. They show that it is possible to effectively detect some salient features of a few Occitan varieties using a simple topic modelling approach, and that the dialect subsets as declared by users closely match the computed dialect clusters.

## 2. Related work

The Occitan language is a dialect continuum with a long history and various denominations. Besides unspecified denominations such as *patois*, or regional names coined for political reasons like *Provençau* (Lieutard, 2024), linguists have proposed several internal classifications over time. The most commonly used diatopic classification distinguishes between six broad areas of the continuum, with the Gascon (GAS), Lengadocian (LAN), Provençau (PRO), Lemosin (LIM), Auvernhat (AUV) and Vivaroaupenc (VIV) varieties.

With a long-standing oral and written tradition dating back to the Middle Ages, the language has undergone profound changes over the past century, creating large differences in usage between older native speakers and newer generations who acquired it outside the family (Brennan, 2024). Moreover, while the language has no official nor widely recognized standard, two major spelling conventions emerged in the late 19th and beginning of the 20th century: the *mistralian* norm and the more widely used *classical* system, designed to standardize writing and reduce dialectal variation (Lamuela, 2024).

### 2.1. Datasets for Occitan

Various projects have helped increase the presence of contemporary Occitan in NLP datasets (Miletić et al., 2020; Penedo et al., 2025). However, the amount of data is still low, and the geographical coverage of the continuum is biased towards the Lengadocian variety.

Most of the Occitan data available for NLP purposes is included in very large, multilingual, automatically mined datasets, such as NLLB (Costajussà et al., 2024) and FineWeb-2 (Penedo et al., 2025). However, the quality of these datasets is questionable as they contain many samples erroneously labelled as Occitan.

Other significantly large corpora have their origins in Wikipedia: Wikipedia Monthly (Omar Kamali, 2025) contains dumps of plain text articles, WikiMatrix (Schwenk et al., 2019) is the product of automatically aligned parallel translations, OcWikiDisc (Miletic and Scherrer, 2022) consists of talk pages messages with LID predictions—and is therefore the first dataset of UGC in Occitan, before ours—, and OcWikiAnnot (Miletić, 2023) contains sentences with automatically predicted part-of-speech tagging and lemmatisation annotations.

However, few Occitan-specific research projects include dialect information in their datasets. Some exceptions include Tolosa Treebank (Miletić et al., 2020), which has train and test splits for each of four varieties, which were then used to discover dialectal biases in morphosyntactic tagging experiments (Miletić, 2023), and Poujade et al. (2024) provide annotations on both dialect (two varieties) and spelling system used in each document, on top of word-level morphosyntactic annotations. Various datasets with dialect labels were also published by the Occitan organisation Lo Congrès. *Occitan Corpus from Lo Congrès news* (Séguier and Lo Congrès, 2023a) contains bilingual data from the news section of the organisation, with sentence-level labels for six varieties. SoftwaresOccitanTranslations [sic.] (Séguier and Lo Congrès, 2023b) contains translations of many open source tools into Occitan, with sentence-level labels for the six major varieties as well as Niçard (NIC), a localized variety that is classified by Sibille (2024) as Provençau with some characteristics of Vivaroaupenc. *Lo Congrès websites Corpus* (Séguier and Lo Congrès, 2024) contains bilingual data from all websites belonging to the organisation, with sentence-level labels for the six major varieties as well as Aranés (the variety spoken in Spain, linguistically associated to Gascon) and Cisalpine (the French denomination for all varieties spoken in the north of Italy).

The distribution of dialect labels in existing datasets is largely imbalanced. Our contribution addresses this gap as corpus FORUMOCCITANIA contains both a high amount of self-declared Lemosin and Provençau data (largely under-represented varieties in the existing datasets), and a seemly balanced distribution between the represented varieties, according to our predictions in Section 6. Also, annotations in existing datasets pertaining to the internal variation within the Occitan language are mostly limited to the dimension of dialect varia-
tion, whereas many other linguistic (e.g. spelling
system) and sociological (e.g. age, geolocation)
factors could help conducting more in-depth com-
putational linguistics analyses, but also building
NLP applications that are more representative of
the diversity of Occitan linguistic usages.

## 2.2. Anonymising User-Generated Content

Anonymisation of unstructured data typically in-
volves de-identifying personally identifiable infor-
mation (PII) (Pilán et al., 2022). The detection of
direct or quasi-identifiers often combines manual
and automated techniques, such as regular expres-
sions and named-entity recognition (NER) mod-
els (Adams et al., 2019). In defining which enti-
ties to anonymise, several works on UGC datasets
(Adams et al., 2019; Çetinoğlu and Schweitzer,
2022; Riabi et al., 2024) distinguish between the
names of famous and non-famous people, and
choose to leave famous people non-anonymised.
Once detected, entities are anonymised using
labelled placeholders, or category-consistent re-
placements (e.g. substituting one name for an-
other) (Çetinoğlu and Schweitzer, 2022). Pilán et al.
(2022) and Riabi et al. (2024) highlight the impor-
tance of risk assessment, in order to choose the
right balance between user privacy and data utility,
usually measured with recall and precision metrics.

## 2.3. Dialectometry

There is an important tradition of dialectology and dialectometry research for Occitan, with studies based on linguistic atlases (Léonard et al., 2024). However, to the best of our knowledge, our corpus-based dialectometry experiments–while not aimed at a detailed linguistic analysis– are the first of the kind to be conducted on Occitan.

Among previous corpus-based approaches,
Hovy and Purschke (2018) propose to train
Doc2Vec embeddings on online posts in the
German-speaking area, before applying a combi-
nation of clustering and dimensionality reduction
techniques to obtain dialect clusters. Kuparinen
and Scherrer (2023) extract clusters of dialectal
Finnish and Norwegian by applying principal com-
ponent analysis (PCA) to speaker ID embeddings
that are learned jointly with a dialect-to-standard
neural machine translation model.

Our experiments, however, are based on the clus-
tering method described in (Kuparinen and Scher-
rer, 2024), who train topic models using phonet-
ically transcribed interviews of dialectal Finnish,
Norwegian and Swiss German.

3. Dataset creation

3.1. Data collection

We scrape the forum website to collect all posts' full
contents, the forum structure and user information.<fnref n="3" />
On the forum, each post is displayed next to a user
block, containing a summary of their profile. Users
can voluntarily declare information based on the
available fields, all of which are optional. We extract
the following metadata: age (at the time of parsing,
as computed by the website), geographical location
(free text field), dialect (from the options Gascon,
Lemosin, Lengadocian, Provençau, Vivarés, or Au-
vernhat), and Occitan proficiency level.<fnref n="4" /> The forum
is structured into categories, subcategories and
topics. For each post, we extract the contents in
HTML format, the position of the post in the topic,
the parent category and subcategory, as well as
the date and time of publication.

3.2. Derived metadata

Based on the parsed metadata, we normalise the
self-declared age (grouped into buckets to reduce
the risk of identification) and geographical locations,
additionally completing values corresponding to the
country, administrative region (before and after the
administrative fusions of 2016), cultural region,<fnref n="5" />
French *département* and locality.<fnref n="6" />

We also provide estimated metadata: predictions of (i) languages and (ii) Occitan varieties used within the dataset.

For language identification (LID), we use Fast-
Text (Joulin et al., 2016)<fnref n="7" /> and the model from (NLLB
Team et al., 2022) to predict the main language(s)
used by each user, and present in each post.<fnref n="8" /> LID
is applied to all posts of five words or more, after
removal of HTML markup, quoted contents, URLs
and emails. For each post, we predict a maximum
of two languages, always selecting Occitan if it is
in the top 5 model predictions and only selecting
another language if it is the top prediction. For
each user, we process all posts individually and se-
lect a maximum of three most frequently predicted

<footnote n="3">³Code for the dataset creation, analyses and experi-
ments to be released with the final paper.</footnote>

<footnote n="4">⁴From the options (translated into English here): “I understand, I can’t speak”, “I speak a little”, “I speak it well, I only make small mistakes”, “I speak correctly”, “I am proficient, in more than one dialect”, “I speak fluently”.</footnote>

<footnote n="5">⁵An area below the administrative regional level, de-
fined by local culture, geography, or history.</footnote>

<footnote n="6">⁶We only share localities with over 30k inhabitants.</footnote>

<footnote n="7">⁷https://huggingface.co/facebook/
fasttext-language-identification</footnote>

<footnote n="8">⁸Restricting predictions to those found in a predefined
list of plausible languages: Occitan, French, Catalan,
English and Spanish.</footnote>
languages, associated with their relative frequency. For posts with two predicted languages, the second one counts for half in the label frequencies.

For Occitan dialect prediction, we use the results of our clustering and dialect identification experiments presented in Section 6 for each post associated with a high Occitan LID score.

### 3.3. De-identification

We carry out de-identification to reduce the risk of disseminating direct and quasi-identifiers (the number of online active Occitan speakers around 2010 was probably low), especially for its use in downstream NLP applications.<fnref n="9" /> Our de-identification pipeline detects PII entities (usernames, names (except public figures), phone numbers, email addresses and postal addresses) in each post, and replaces all occurrences with category-specific placeholders.

We use a combination of regular expressions and a GLiNER model<fnref n="10" /> (Zaratiana et al., 2024) specialised in the detection of PII s. We first use regular expressions and word lists to detect usernames (from all usernames on the forum), email addresses, phone numbers, and names (given a list of French and Occitan first names that can be extended to the right with a capitalised last name). We then apply GLiNER to truecased<fnref n="11" /> texts to detect postal addresses (validated with a regular expression) as well as additional person names. Longer posts are split into smaller chunks with a sliding window before being passed to the model.

Detected names (from regular expressions and GLiNER) are validated in four steps. First, entities shorter than three characters are removed. Next, a lexicon is used to remove entities containing only words that are not proper nouns. We build the lexicon by concatenating all words except for proper nouns, punctuation and numbers from Loflòc (Vergez-Couret et al., 2024) and Universal Dependencies (Zeman, 2025) corpora in Occitan, French, Spanish and Catalan<fnref n="12" />. Third, Wikipedia API<fnref n="13" /> is used to identify public figures who should not be anonymized. Finally, we apply a list of regular expressions to further eliminate false positives.

We evaluate the pipeline (except for the last post-editing step based on regular expressions) by manually annotating 200 posts, extracted randomly with weights based on the inverse number of posts by each user. Inspired by the recommendations of Pilán et al. (2022), we compute the precision, recall and F2-score on exact matches, by PII category, as well as the macro-averages for each metric.

Results presented in Table 1 show a macro-average F2-score of 75.76. Scores are high or even perfect across all categories except names, where only half of the annotated entities were detected by our pipeline, and the number of wrong predictions remains very high despite the post-filtering steps. These errors include a high number of public figures, but also common Occitan words with several variants, which we suspect is related to the limited coverage of Occitan in GLiNER.

<table><caption>Table 1: PII detection result based on 200 manually annotated samples. The totals row indicates the sum for columns with absolute values, and average for columns with relative values. #Err is the number of spans that do not exist in the golden annotation.</caption><thead><tr><th>PII</th><th>#Gold</th><th>#Err</th><th>P</th><th>R</th><th>F2</th></tr></thead><tbody><tr><td>address</td><td>3</td><td>0</td><td>100.00</td><td>66.67</td><td>71.43</td></tr><tr><td>email</td><td>4</td><td>0</td><td>100.00</td><td>100.00</td><td>100.00</td></tr><tr><td>name</td><td>30</td><td>100</td><td>12.71</td><td>50.00</td><td>31.51</td></tr><tr><td>phone</td><td>1</td><td>0</td><td>100.00</td><td>100.00</td><td>100.00</td></tr><tr><td>username</td><td>30</td><td>3</td><td>88.00</td><td>80.00</td><td>75.86</td></tr><tr><td>Totals</td><td>68</td><td>103</td><td>80.14</td><td>78.00</td><td>75.76</td></tr></tbody></table>

### 3.4. Dataset versions and splits

The dataset exists in two formats: TEI and JSONL. The TEI dataset version contains detailed information about each participant, reproduces the forum structure (main categories, subcategories, topics and posts in their original order), and is faithful to the original HTML contents of the posts. We have defined TEI guidelines<fnref n="14" /> to encode the web forum and the fine-grained, non-standard language varieties (Janès et al., 2025). We use Glottolog identifiers (Nordhoff and Hammarström, 2011) to declare languages used throughout the document, including references to specific varieties of Occitan. Converting the original HTML posts to TEI involves steps such as paragraph detection from empty lines, list detection via regular expressions (since forum lists rarely use HTML markup), preservation of image/emoji/video/audio references, and simplification of style formatting. Also, quoted replies are processed in order to retrieve the original post being quoted.

<footnote n="9">⁹Since the forum remains online, the dataset cannot be fully anonymised, as a web search of an excerpt may reveal the original content.</footnote>

<footnote n="10">¹⁰https://huggingface.co/urchade/gliner_multi_pii-v1</footnote>

<footnote n="11">¹¹We train a custom model using the Moses truecaser (Koehn et al., 2007) on the combined ForumOccitania and LoCongresNews datasets.</footnote>

<footnote n="12">¹²UD v2.17. Occitan: Tolosa Treebank. French: Sequoia. Spanish: AnCora (dev+test). Catalan: AnCora (dev+test)</footnote>

<footnote n="13">¹³Occitan, French and English encyclopedias</footnote>

<footnote n="14">¹⁴https://defi-colaf.github.io/metadata/TEI/ODD.html</footnote>
The JSONL version is meant for use in NLP applications. We parse the TEI version to extract the raw text from posts, except for quotes, along with user and LID metadata. Line breaks are added in the raw texts where indicated in the TEI, and paragraphs are separated by an empty line.

We also provide train, development, and test splits for building and evaluating downstream, dialect-aware NLP systems. The splits are based only on samples with a high Occitan LID score (above 0.9) and a dialect label, except for Vivarés and Auvernhat labels which are incorrect as shown in Section 5. The test and development sets contain 1000 samples each, leaving 7327 samples for the training set. Our splitting strategy maximises divergence between each subset by minimising shared authors and forum categories, and ensures a balanced dialect label distribution in the test set.

## 4. Dataset statistics

The dataset contains 20,005 posts (approximately 1.15M tokens), published between 2007 and 2012. They are organised in 14 main forum categories, 25 subcategories,<fnref n="15" /> and 2,583 topics (discussion threads). The names of forum categories relate to various themes: news and media, music, linguistics, education, computers and technical support, events, books, sports, movies, tourism, food, jobs. The category with the most data (*Estanquet* 'bar') is not thematic, but it entails a subcategory where many users introduced themselves.

Most of the posts are short between 12 and 59 tokens. However there are some exceptions, with posts up to 6,372 tokens, and 393 posts with 0 tokens (they originally contained only a combination of images and quoted posts).

Among the 292 extracted users, 66% provided information in at least one profile field (dialect, Occitan language level, age, geographical location), and 37% completed all fields (see Figure 2).

**Age** Age declarations show a majority of young users between 20 and 35 years old,<fnref n="16" /> although a few older users were also active. As shown by Figure 3, users who declared a higher language level tend to be older. However, the oldest users only declared a low to medium level of Occitan.

**Dialects** The self-declared language varieties encompass all six major families of Occitan (see Figure 4). Lemosin is the most represented variety in terms of numbers of posts, with a small number of

<figure><img src="image_3.png" /><figcaption>Figure 2: Proportion of declared profile fields across users and posts.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: Distribution of age grouped by language proficiency level, converted to numerical levels from 1 (low) to 6 (high). Green lines represent the median age of users for each language level.</figcaption></figure>

users contributing a large volume of posts. Gascon and Lengadocian are also well represented, with Lengadocian represented by the largest number of users. The forum also includes a non-negligible number of posts for the Provençau and Vivarés dialects. Finally, the Auvernhat dialect, which is very rare in other Occitan datasets, is represented by a single, prolific user.

**Geographical locations** Visualisations of the users' normalised geographical locations (see Figure 5) reveal that the data of FORUMOCCITANIA covers a large portion of the dialectal continuum area, including Spanish Val d'Aran (one user, among a few others in Spanish Catalonia). All French administrative regions belonging to the continuum are represented in the dataset with at least one user, however the distribution of users places most of them in the western regions – historically associated to the Gascon and Lengadocian varieties –, while the northern regions – corresponding to the low-resourced Auvernhat and Vivaroaupenc varieties – are represented by very few users. When looking into more geographical details, we notice

<footnote n="15">¹⁵Some posts belong directly to a main category, without a subcategory.</footnote>

<footnote n="16">¹⁶We compute age statistics based on the age of all users in 2009, i.e. the median year of post publication.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 4: Users and posts per declared dialect.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 5: Geographical distribution of users. Region labels show the number of users who declared a location. Percentages in parentheses indicate the share of declared locations matched both to the region and to a *département*. Hatchings mark *départements* represented by at least one user.</figcaption></figure>

that the geographical coverage decreases at the *département*-level, leaving most of the northern and eastern *départements* unrepresented.

In par with our observations of the distribution of posts per users, the geographical distribution of posts reveals a striking imbalance, with most of the data located near 3 cities: Limoges (Lemosin variety), Bordeaux (Gascon variety) and Toulouse (Lengadocian variety).

Finally, some users declared locations outside of the historical Occitan dialect continuum, both elsewhere in France and beyond.

**LID** Predictions reveal that most of the dataset is in Occitan. 78% of users and 89% of posts are associated with Occitan, which is the top prediction in most cases (84% of posts), or as second prediction. 11% (2233) of posts have no LID label (corresponding to empty or very short posts, or because of predictions outside of our predefined list), and other languages are also present: French

(4% or 788 posts), Catalan (141 posts), English (23 posts) and Spanish (20 posts).

## 5. Qualitative analysis

All the posts we observed use the *classical* spelling system. The few references we found of the *mis*-tralian spelling were negative opinions about it.

General as well as detailed observations of the posts organised by subsets per dialect led to the conclusion that most of the posts with labels Gascon, Lemosin, Lengadocian and Provençau present a high number of distinctive dialectal features corresponding to their label (see Table 2 for a few examples). For the Provençau subset, some of the features we observed correspond more specifically to the Niçard variety. However, the subset of posts labelled as Auvernhat features only a high number of Gascon characteristics, and we could not find any Vivaroaupenc characteristic in the Vivarés subset, which seems to contain only posts written in Lengadocian.

We noticed frequent co-occurrences of multiple languages—mostly between Occitan and French. This corresponded to some cases of code-switching, but seemingly more often to quotes of external sources of contents such as inline citations or long portions of documents, or when providing bilingual announcements for local events.

## 6. Clustering experiments

We carry out an initial series of experiments to assess the potential of our FORUMOCCITANIA dataset for both linguistic analysis and NLP applications, with a particular focus on Occitan dialect identification. Since the dataset lacks verified dialect annotations, we adapt the topic modelling approach proposed by Kuparinen and Scherrer (2024) to explore its applicability to our data. Our experiments address whether dialect clusters in the dataset match known descriptions of Occitan variation, how precisely they capture distinctions along the continuum, and what topic distributions on labelled and unlabeled samples reveal about dialect distribution.

### 6.1. Methodology

**Modelling approach** We use non-negative matrix factorisation (NMF) (Paatero and Tapper, 1994) to perform topic modelling on Occitan datasets with features including full words and character n-grams, vectorised using TF-IDF. We explore results when varying the number of topics between 4 and 15.

We preprocess all samples by lowercasing, removing accents, substituting repeated characters with a single one (when there are three or more,
<table><caption>Table 2: Examples of dialectal features found in the FORUMOCCITANIA dataset.</caption><thead><tr><th>Dialect</th><th>Selection of dialect features</th><th>Sample excerpt and translation</th></tr></thead><tbody><tr><td>Lengadocian</td><td><code>amb</code> "with"<br><code>dels</code> plural indefinite article<br><code>aquel</code> "this"</td><td>Apreni l'occitan a Montpelhièr sol <b>amb dels</b> livres e un pauc <b>amb dels</b><br>cora a l'Universitat Paul Valery (sens èsser estudiant en occitan ).<br>I'm learning Occitan in Montpellier, by myself with books and a little with<br>lessons at University Paul Valery [in Toulouse] (without being a student of<br>Occitan studies).</td></tr><tr><td>Gascon</td><td><code>har</code> "make"<br><code>ua</code> "a"/"one" (feminine)<br><code>navèth</code> "new" (masculine)</td><td>Un o dus còps lo mes, vos perpausi <b>ua</b> fotò o un text, e vos mandi de vos<br>en inspirar per <b>har</b> un tribalh d'invencion.<br>Once or twice per month, I offer you a photo or a text, and I ask you to<br>draw inspiration from it to make a work of invention.</td></tr><tr><td>Lemosin</td><td>'quò' "that"<br><code>chantaire</code> "singer"<br><code>emb</code> 'with'</td><td>Non, sens desconar, m'agrada mai <b>queu chantaire</b>, <b>vese</b> pas una sola<br>relacion <b>emb</b> l'autre, se pren pas tròp au seriós, <b>quo</b> es pas <b>meschant</b>...<br>No, without joking, I like this singer more, I don't see a single connection<br>with the other, he doesn't take himself too seriously, that is not mean...</td></tr><tr><td>Provençau</td><td><code>lei</code> "the" (plural)<br><code>emé</code> "with"<br><code>avii</code> "(I) had" – Niçard-specific</td><td>Si <b>pòu</b> atrovar dins <b>lei</b> Fnacs/virgins ?<br>Can it be found in retail chains Fnac/virgins?</td></tr></tbody></table>

typical to UGC style), as well as removing punctuation, URLs, email addresses, anonymisation-related placeholders and words corresponding to language names from a predefined list (e.g. "occitan", "lengadocian") that could lead to irrelevant topics as well as biased predictions, as mentioned in (Sousa et al., 2025).

**Data** We train NMF models on FORUMOCCITANIA non-anonymized samples, with and without dialect labels, filtered by Occitan LID score (≥0.9) and by number of tokens (≥30 tokens). For evaluation, we take samples filtered by LID score only. To calculate the performance, we use the samples with a dialect label, except for labels Auvernhat and Vivarés, which are mislabelled as shown in Section 5. Unlabelled samples are used separately and in combination with labelled ones for other analyses. Table 3 shows some basic statistics of the data passed to the NMF preprocessing and training pipeline.

**Evaluation** To evaluate, we predict the dominant topic of each sample of the test data. The subset with self-declared dialect labels is used to compute automated metric scores, but we also use the predictions on unlabelled samples for a manual analysis based on a map visualisation.

For the automated evaluation, we use the following popular clustering metrics: homogeneity (used to measure to what extent all predictions of the same topic belong to samples of the same dialect subset), completeness (used to measure to what extent all samples of the same dialect subset have the same topic prediction), and V-score (the harmonic mean between the two metrics above). Since the dialect classes are very imbalanced in

the test sets, we duplicate the predictions of under-represented classes when computing the scores above, so that the difference of each class to the most represented one is below 5%. We compute these metrics to compare predicted subsets per dominant topic with reference subsets from self-declared dialect labels, focusing on the V-score value. We also use the completeness metric to compare the same predicted subsets with subsets by user ID, to ascertain to what extent all samples of the same user are assigned to the same topic.

## 6.2. Results

Our NMF clustering model trained for 4 topics (NMF-t4) resulted in the best balanced V-score of 64.43 when evaluated on dialect labels, and a clear one-to-one topic-to-dialect mapping (see Figure 6). With this model, users are generally assigned a low number of topics; the completeness score in comparison to user IDs is 72.74.

This model has a good potential for dialect identification applications as the macro-average F1-score of dialect predictions compared to reference labels is 85.50. While the distribution of dialect labels in the labelled data is imbalanced, with an over-representation of Lemosin, the predictions of the unlabelled samples are characterised by a strong under-representation of this variety, which results in a fairly balanced distribution of the 4 major varieties within the dataset, frequencies ranging from 18% (Provençau) to 30% (Lengadocian).

Several of the top features per topic from this NMF model (see Table 4) clearly relate to salient features of the dialects they were mapped to. For instance, in the Lengadocian topic, features such as *del*, *al* and *l* could relate to the [l] phone in final position, which has been maintained throughout
<table><caption>Table 3: Statistics on the training and test data used during our clustering experiments. Each cell shows the number of samples (#s) and tokens (#t), for each subset based on self-declared dialect. Values abbreviated with 'k' are in thousands.</caption><thead><tr><th rowspan="2"></th><th colspan="2">AUV</th><th colspan="2">GAS</th><th colspan="2">LAN</th><th colspan="2">LIM</th><th colspan="2">PRO</th><th colspan="2">VIV</th><th colspan="2">unk</th><th colspan="2">Total</th></tr><tr><th>#s</th><th>#t</th><th>#s</th><th>#t</th><th>#s</th><th>#t</th><th>#s</th><th>#t</th><th>#s</th><th>#t</th><th>#s</th><th>#t</th><th>#s</th><th>#t</th><th>#s</th><th>#t</th></tr></thead><tbody><tr><td>Training</td><td>81</td><td>13k</td><td>915</td><td>85k</td><td>897</td><td>72k</td><td>2402</td><td>240k</td><td>531</td><td>46k</td><td>413</td><td>39k</td><td>2653</td><td>276k</td><td>8478</td><td>820k</td></tr><tr><td>Test - all</td><td>109</td><td>13k</td><td>2120</td><td>104k</td><td>2093</td><td>90k</td><td>4199</td><td>269k</td><td>1056</td><td>55k</td><td>788</td><td>45k</td><td>4845</td><td>312k</td><td>16281</td><td>945k</td></tr><tr><td>Test - w/ dialect</td><td>0</td><td>0</td><td>2120</td><td>104k</td><td>2093</td><td>90k</td><td>4199</td><td>269k</td><td>1056</td><td>55k</td><td>0</td><td>0</td><td>-</td><td>-</td><td>9468</td><td>518k</td></tr><tr><td>Test - w/o dialect</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>4845</td><td>312k</td><td>4845</td><td>312k</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>Figure 6: Predicted NMF-t4 topic distribution for each labelled dialect subset.</figcaption></figure>

<table><caption>Table 4: Top 10 features per topic for model NMF-t4. The dialect column reflects the observed one-to-one topic-dialect mapping.</caption><thead><tr><th>ID</th><th>Dialect</th><th>Feats</th></tr></thead><tbody><tr><td>0</td><td>Lengadocian</td><td>la que a del e es lo d amb una pas se un per l al me mas en aquo</td></tr><tr><td>1</td><td>Gascon</td><td>ei mes que ua qu qui pas deu e n a mei dab la lo ne dens un en com</td></tr><tr><td>2</td><td>Lemosin</td><td>quo mas es dau daus pas per la que un a los lo las e l i en qu na</td></tr><tr><td>3</td><td>Provençau</td><td>lei que es mai l lo la si a per d e mi en un pas una ben dei li</td></tr></tbody></table>

Lengadocian's evolution from Latin, in contrast to most other varieties where it is frequently vocalised to [w]–as in the topic features such as *deu* (Gascon) and *dau/daus* (Lemosin). As another example, the most weighted feature that maps to Provençau-lei–relates to the specific form of the definite plural article used in southern Provençal varieties, as opposed to *los/las*, more common in other varieties.

The map of topics distribution per *département* (see Figure 7) reveals that the topics associated with each dialect broadly follow the linguistic maps of the Occitan continuum, with Provençau predictions in the South-East, Lengadocian in the central South, Limousin in the North-West, and Gascon in the South-West. Looking in more detail, we notice that *départements* in the Provençau area that have borders in contact with other varieties (Lengadocian and Vivarés) display a lower ratio of the Provençau topic among the predicted samples. However we do not observe such a tendency in the Lengadocian-speaking area, although it is described in (Léonard et al., 2024). Zooming in on the Gascon area, we observe that the ratio for this

<figure><img src="image_8.png" /><figcaption>Figure 7: Map with the distribution of NMF-t4 topics per *département*, in or close to the Occitan-speaking area (France only). Background colours indicate the number of samples predicted in that *département* (log scale). The size of each pie chart is proportional to the number of users represented in that *département*'s samples, also on a log scale.</figcaption></figure>

topic differs between *départements*,<fnref n="17" /> in a way that is similar to the description of what is called *gasconity gradient field* in (Sibille, 2024), which shows a maximal expression of Gascon dialectal features in the Pyreneans—as we can see in *départements* 64 and 65—, and on the contrary a very low expression in the north of the area-compatible with the ratio of Gascon topic predictions in *département* 33, near Bordeaux. Our experiments with NMF models trained for at least 8 topics reflect this internal variation of the Gascon variety with more details, as an additional topic emerges with top features that clearly relate to some of the most important features of the Pyrenean Gascon, such as the use of the masculine singular article *eth*.

## 7. Conclusion

In this paper, we introduced the FORUMOCCITANIA corpus, containing authentic UGC texts in Occitan written using the classical spelling, and accompanied by user-provided metadata and LID predictions.

<footnote n="17">¹⁷We ignore *départements* 31 and 09 as we suspect them to include both Gascon and Lengadocian samples, as most isoglosses between these varieties are crossing these *départements*.</footnote>
tions.

Statistical and qualitative analyses, along with
preliminary dialectometry experiments, show that
the dataset has strong potential for linguistic re-
search and NLP, owing to rich sociolinguistic meta-
data (dialect, age, proficiency, location). Most fo-
rum users who produced posts are new speakers of
Occitan, although some are older fluent speakers.

The corpus spans most of the Occitan-speaking
area. Although Lemosin is over-represented
among self-declared dialects, clustering on both
labelled and unlabelled posts results in a fairly
balanced distribution across four major varieties
(Lengadocian, Lemosin, Gascon, Provençau). Our
best NMF-based topic model, trained on four topics,
reached a balanced V-score of 64.43 and a classi-
fication F1-score of 85.50. Furthermore, geograph-
ical analysis of clusters further reveals fine-grained
variation, e.g. gradual shifts within Gascon.

The dataset will be made available in XML-TEI and JSONL formats for research purposes, including train/dev/test splits made for dialect-aware NLP experiments.

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