T re E n: A Multilingual Treebank Project on Environmental Discourse (2025)

Abstract: The increasing complexity of environmental discourse is directly proportional to the growing complexity of environmental debates present today in all communication media. While linguistic and communication studies have been pursued on this discourse, the development of computational linguistic tools and resources dedicated to support its analysis and interpretation is still very incipient. For one, no morphosyntactic resources specific to the environmental domain can be found on major platforms and repositories. This paper introduces TreEn, a multilingual treebank project in progress which compiles texts on environmental discourse produced in different conversational and communication contexts. In particular, it reports on the parallel component of the project and discusses issues faced during sentence-level alignment between original and translated texts, annotation of texts following UD guidelines, and labeling entities drawing on an ontology of environmental-related topics. This novel resource is expected to support environmental discourse analysis by providing morphological and syntactical data to enable cross-language and cross-cultural comparison based on the semantics of the entities annotated in the treebank.

## introduction in 1894, the swedish nobel prize-winning chemist and physicist svante arrhenius formulated a climate model that correlated rising atmospheric co 2 levels with glacier melting, pointing for the first time to human activity as the cause of global warming @xcite . more than a century later, arrhenius' model remains the subject of heated debate, not only in academia but also in communication and social media, yielding a massive amount of environmental discourse and posing a challenge to analysts and consumers alike. environmental discourse is often characterized by its grammatical intricacy @xcite , accountable for the emergence of its domainspecialized lexicon. it encompasses different genres and registers, whereby meaning is construed by different stakeholders. moreover, there is the impact of languages in contact, as translation from major into minor languages is both a frequent means of production and a main source of variation. as far as the computational analysis of environmental discourse is concerned, resources have only recently begun to be developed, driven by the poor performance of large language models (llms) in this domain @xcite and the need for dedicated pre-trained models to efficiently process such texts @xcite . however, corpora and datasets remain scarce. @xcite reviewed available datasets on climate-change discourse and highlighted the predominance of news and social media texts in nlp analysis, with approaches primarily relying on corpus linguistics (raw frequency counts, collocations) and out-of-the box tools for topic modelling, sentiment analysis and network analysis. notably, the authors did not report the existence of any annotated resources, such as treebanks. a more recent initiative, however, introduced a multimodal corpus of academic articles and texts from websites (including the international panel on climate change (ipcc), greenpeace international, and greenpeace germany), enriched with metadata and annotations in order to allow for more in-depth discourse analysis @xcite . in line with @xcite , our work pursues the development of annotated corpora to support the study of environmental discourse. we adopt a multilingual perspective which can inform both theoretical and applied studies in the fields of language comparison and typology, translation studies, discourse analysis, to name but a few. it also provides a valuable resource for computational linguists seeking to fine-tune general-purpose models and create a benchmark for evaluating llms. as part of this wider project, this paper introduces treen (treebanks for environment), the first multilingual treebank project, comprising treebanks of comparable and parallel texts on environmental discourse. more specifically, it describes the parallel component of treen currently including texts extracted from a non-governmental report in four different languages (english, brazilian portuguese, italian and romanian), annotated for morphology and syntax following the universal dependencies (ud) guidelines and enriched with entity annotations drawing on the general multilingual environmental thesaurus (gemet). 1 the remainder of the paper is organized as follows. section 2 provides a brief survey of related work. section 3 describes the data collection process for the creation of the parallel component of treen and outlines the methodological decisions made during text alignment. it also details the annotation guidelines applied to this resource, namely ud and universal named entity recognition (u-ner). section 4 presents the statistics of our parallel treebanks and characterizes each of the four languages compiled in treen, highlighting key insights from a cross-linguistic comparison. finally, section 5 presents concluding remarks and outlines directions for future work. ## related work most of the resources hitherto developed for the analysis of environmental discourse consist of raw corpora or datasets labelled for classification tasks such as sentiment analysis. nevertheless, as noted 1 https://www.eionet.europa.eu/gemet/en/about/ by @xcite , sentiment analysis resources for environmental texts are currently extremely limited in all languages. while some corpora on environmental topics are available for english, resources in other languages are scarce. even in dedicated emerging disciplines, as in ecological discourse analysis (ead), studies and resources are still at an early stage and mostly concentrated in specific countries and languages, as highlighted in a very recent systematic review by @xcite . interestingly, some of the countries that are key players in environmental debates, as is the case of brazil, are not represented in ead at all. in computational linguistics, there is growing interest in environmental data, as evidenced by the increasing number of events on this topic in recent years, such as the workshop on ecology, environment and natural language processing foot_0 (basile et al., 2025), climatenlp (2024 -2025), foot_1 and the tackling climate change with machine learning workshop, foot_2 which has been held regularly since 2019 in top-tier conferences. in addition, smallerscale challenges and data competitions (such as those hosted on platforms like kaggle foot_3 ) or individual efforts by researchers have made annotated social media corpora related to climate and environmental issues available. these corpora cover a range of tasks related to environmental discourse, with a particular focus on aspects of climate change. some works focus on verifying whether a given text contains environmental claims (or pertains to climate change) @xcite @xcite , or facilitate expert-informed retrieval from corporate climate disclosures @xcite , while others examine the topics discussed within such narratives @xcite @xcite . other studies focus on detecting and analyzing attitudes towards global warming @xcite , as well as identifying neutralization techniques (i.e., rhetorical strategies used in climate change skepticism to justify inaction or promote alternative views) @xcite . another important line of research targets climate change contrarianism, denial, and disinformation @xcite @xcite . as previously mentioned, one of the few annotated resources reported in the literature is the insightsnet climate change corpus (iccc) introduced by @xcite . the iccc comprises academic articles and texts from websites, and is annotated for metadata, morphology, syntax, and named entities. the authors used spacy and stanzacorenlp for automatic annotation and do not report any manual check of the output. while the corpus includes texts in english along with their german translations (for most of the texts), no alignment between the originals and translations was carried out. the availability of parallel texts is relevant for several nlp tasks and linguistic fields. with regard to parallel treebanks, there are few available in the ud repository. among them, the parallel universal dependencies (pud) treebanks, created for the conll 2017 shared task on multilingual parsing from raw text to universal dependencies foot_4 and including 15 languages @xcite . for english and swedish, we can cite lines @xcite , while italian, english and french are aligned in partut @xcite . the fact that these treebanks have been made compliant with the ud guidelines enhances their comparability and makes them a particularly valuable resource for linguists and computer scientists. none of these treebanks, however, is dedicated to environmental discourse. several unannotated parallel corpora are also available through the opus project platform. 7 among them, some parallel corpora can incidentally include language related to environmental topics, as is the case of europarl, 8 which includes debates on environmental issues held in the european parliament. however, these corpora are mostly unannotated and no dedicated subcorpus is readily available. another important line of work related to multilingual resources for comparability analysis is entity recognition, which is currently being promoted by the universal ner (uner) project which especially focuses on named entities, usually introduced in text by proper nouns. this community-driven initiative provides goldstandard named entity recognition annotations in a wide variety of typologically and geographically different languages, adding a consistent ner layer to ud corpora @xcite . this provides a valuable benchmark for multilingual ner, especially for low-resource languages and for testing inter-annotator agreement and tag distribution across domains. this general purpose initiative is especially inspiring for our project since a key challenge in domainspecific ner-and particularly in environmental neris the lack of annotated corpora and benchmarks dedicated to the environmental domain. as in most specific domains, entities other than persons, organizations, and locations are relevant in environmental discourse and they are realized by common nouns and noun phrases, some entities being nested inside larger ones, which are not covered by general-purpose ner tools. to map domain entities, researchers usually turn to domain ontologies, which provide a common ground for naming classes and subclasses and the relations holding between them. in the environmental domain, gemet (general multilingual environmental thesaurus) foot_7 provides structured multilingual identifiers and relations between environmental concepts (european environment agency, 2024). gemet has been used for tasks such as concept mapping and normalization of environmental terminology; however, its integration into ner pipelines is still experimental. nevertheless, gemet is a solid framework for multilingual annotation, which accounts for our decision to adopt it in our treebank annotation, as detailed in the following section. ## corpus compilation and annotation to compile our parallel corpus, we downloaded the original version of wwf's foot_8 2024 living planet report, available in multiple languages, including english, foot_9 brazilian portuguese, 12 italian, 13 and romanian, foot_12 the languages currently annotated in for example, in colombia, a 2019 law requires that solar and wind projects transfer a percentage of their sales to communities within the project's area of influence while the philippines' renewable energy act requires that 80% of project royalties be directed toward subsidizing power costs in affected communities. por exemplo, na colômbia, uma lei de 2019 exige que os projetos de energia solar e eólica transfiram um percentual de suas vendas para as comunidades dentro da "área de influência" do projeto. já a lei das energias renováveis das filipinas exige que 80% dos royalties dos projetos sejam direcionados para subsidiar os custos de energia nas comunidades afetadas. ## results we computed the number of sentences, words, and lemmas in the wwf24 parallel corpora using a custom python pipeline built on pandas and spacy language-specific models @xcite . as previously mentioned, some of the sentences in the original text in english were split in the translated texts, which accounts for the higher number of sentences in italian and much higher in brazilian portuguese when compared with the source text in english. regarding number of tokens, the translated texts exhibit a higher number, also accountable for by lexical rendition of some english terms and typological differences between the languages, such as the strong reliance of romance languages on determiners and prepositions to construe noun phrases. this can be seen, for instance, in terms such as "greenhouse gas emissions" (3 tokens) translated into italian as "le emissioni di gas serra" (5 tokens), brazilian portuguese as "as emissões de gases de efeito estufa" (7 tokens) and romanian as "emisiile de gaze cu efect de serȃ" (7 tokens). the higher number of unique words and lemmas in the translated texts is also impacted by typological differences as well as by translators' choices in rendering the same word and lemma in english by different synonyms of the equivalent word in their languages. with regard to average sentence length, results point to relatively long sentences, which can be explored as a variable contributing to the overall complexity of this kind of discourse. ## conclusion and future work this paper presented the first steps in the development of a novel set of aligned treebanks within the multilingual treebank project treen, which compiles texts on environmental discourse produced by different stakeholders (institutions, citizens and activists) in different communication contexts. the data included in the treebanks were extracted from the wwf's 2024 living planet report in english, italian, romanian and brazilian portuguese. they were annotated for morphology and syntax in ud format and enriched with the annotation of the entities involved in the discourse according to the gemet ontology for environmental topics. this methodology is designed to apply to other texts in the ongoing expansion of treen also considering e.g. data alignment at fine-grained levels and an ontology matching that takes into account form variation (such as synonyms or paraphrases). these treebanks and the future development of the project are intended to fill a gap regarding resources dedicated to the environmental discourse in order to offer the community the possibility of conducting a more in-depth analysis of environmental discourse. this is expected to open new perspectives to more reliably study the different narratives present in different languages, mirroring different cultures and perceptions in the discourse about environmental issues. the availability of morphological and syntactic annotation can be very helpful for detecting different narratives, e.g. allowing the identification of different tenses and forms of verbs as indicators of different ways of narrating events (realis vs. irealis). complementarily, annotating named entities and linking the syntactic structures in the different languages to a common ontological framework such as gemet helps us in the semantics oriented characterization of the discourse about the environment in different cultures. last but not least, we expect to use morphological and syntactic annotation and a deeper analysis of the annotated data according to several different perspectives to explore the level of intricacy and complexity of environmental discourse produced by the different stakeholders, which can yield insightful and actionable information to develop more inclusive forms of environmental discourse.