Operationalising the Hermeneutic Grouping Process in Corpus-assisted Discourse Studies (2024)

Abstract: We propose a framework for quantitative-qualitative research in corpus-assisted discourse studies (CADS), which operationalises the central process of manually forming groups of related words and phrases in terms of “discoursemes” and their constellations. We introduce an open-source implementation of this framework in the form of a REST API based on Corpus Workbench. Going through the workflow of a collocation analysis for fleeing and related terms in the German Federal Parliament, the paper gives details about the underlying algorithms, with available parameters and further possible choices. We also address multi-word units (which are often disregarded by CADS tools), a semantic map visualisation of collocations, and how to compute assocations between discoursemes.

## introduction and related work corpus-assisted discourse studies (cads) @xcite @xcite are a highly effective approach for exploring and understanding socio-political discourse, often building on a theoretical background rooted in critical discourse analysis @xcite . cads research focuses on interpreting, explaining, and critiquing discourses surrounding socially contentious issues, intricate historical phenomena, and dominant narratives @xcite . typical examples of the themes explored in cads include socio-economic concerns like austerity @xcite , global challenges such as climate change @xcite , and political ideologies such as right-wing or nationalistic perspectives @xcite @xcite . cads research usually relies on "low-level" corpus-linguistic techniques such as concordancing as well as keyword and collocation analyses @xcite . they are complemented by a hermeneutic interpretation of the observations that takes the wider socio-pragmatic context into account, but which is also influenced (more or less explicitly) by the intuitions and preconceptions of researchers. the use of corpora aims to mitigate such biases and the cherry-picking of examples that support them. a typical cads investigation starts with a detailed examination of keywords and collocates @xcite . keywords are lemmata foot_0 that occur with significantly higher frequency in a target corpus than in a reference corpus and indicate either important topics of the discourse (for a target corpus related to the theme of the study) or characteristic framings used by certain groups of actors (e.g. for right-wing vs. left-wing newspapers). collocates are lemmata that are statistically associated (i.e. tend to co-occur) with a particular node lemma (or set of lemmata). they might indicate, e.g., the salient framings and evaluations associated with a certain topic indicated by the node lemmata (e.g. refugee, displaced person). tentative interpretations obtained from this "distant reading" of the corpora are then confirmed and refined by "close reading" of concordances for individual lemmata, displaying their corpus occurrences in a compact tabular format with left and right context. relevant methodological research in corpus linguistics has focused on identifying suitable association measures and other parameter settings for the identification and ranking of keyword and collocation candidates @xcite @xcite . however, it has been established that there is no single "best" measure @xcite , leading researchers to advocate for the integration of multiple perspectives provided by different algorithms and parameter configurations @xcite . a crucial step in cads is the manual grouping of related keywords and collocations, which are then interpreted in terms of discursive patterns (topics, discursive strategies, positions or fragments (cf. @xcite ). this "meso level" of discourse analysis @xcite thus forms the bridge between linguistic and discursive patterns. most cads research relies on offthe-shelf concordancing tools (such as cqpweb, antconc, and #lancsbox) or saas platforms (such as sketchengine and english-corpora.org), which are limited in the parameters of quantitative analysis such as choice of association measure (depending on the specific tool used) and present keywords and collocations as tables ranked by association score (making it difficult to recognise discursive patterns among them). the grouping process invariably happens outside the concordancing tools, using spreadsheet software or pen and paper. our aim is to improve the quantitative-qualitative interface in cads research by (i) introducing an operationalisation of the grouping process in terms of "discoursemes" (see section 2) and (ii) providing better software tools that integrate discoursemes into quantitative corpus analysis. we thus stay very close to established and successful practice in cads, which at its core induces groupings and discursive patterns from the observed data in a corpus-driven fashion. this is markedly different from other ongoing research that might also contribute to the future of cads. one strand focuses on machine learning techniques leveraging human "ground truth" annotations to detect functional properties of texts (or text segments) such as emotion @xcite or sarcasm @xcite . with the advent of large language models, another line of research is now conerned with zero-shot detection of topics @xcite or narratives @xcite . in this contribution, we show both the possibilities of a discourseme-based operationalisation of cads analyses and the technical challenges that come along with it, together with recommendations for best practices. since "design and capabilities" of tools are essential to making sense of linguistic data @xcite , and off-the-shelf concordancing tools such as cqpweb @xcite do not provide any reasonable functionality to support the grouping process, we offer an opensource rest api for cads research implemented in python foot_1 with a corresponding openapi specification foot_2 . it builds on cwb @xcite for corpus storage, whose corpus query processor cqp (evert and the cwb development team, 2022) allows efficient querying of large tokenised corpora, retrieving pairs of corpus positions for match and matchend of the query, respectively. the api provides an extensive set of features designed to facilitate cads analyses, including: here, we concentrate on the workflow of defining discoursemes via the result table of a collocation analysis and the technical challenges of its implementation (section 3). some reasonable discoursemes are given as illustrative examples. note that the api can be accessed via https and thus allows analysts to combine an interactive graphical user interface (gui) with low-level api calls when forming discoursemes, then use other tools such as r for further quantitative analyses of the discoursemes and their constellations (see section 4 for a brief discussion). ## discoursemes and constellations let us start from the example of a collocation analysis in a cads investigation. in order to define the node of the collocation analysis, a researcher will manually select a set of lemmata and/or lemma sequences that identify a topic of interest such as refugees (e.g. refugee and displaced person). they will then scan the table of collocations (or multiple tables obtained with different parameter settings) to spot groups of related words that reflect common discursive patterns associated with the topic. for example, collocates like syria and lybia indicate debates about the refugees' origin, while displacement, expulsion, and famine indicate "push factors" of migration. the collocates in a group tend to be semantically related, but this is not always the case. the key criterion is whether they express the same meaning aspect within the discourse (as the example of famine shows). our approach to overcoming the current limitations of cads practice rests on understanding this central grouping step as the formation of discoursemes, which we define as (minimal) units of lexical meaning in the context of a given discourse. our goal here is to provide an operational concept that has a clear hermeneutic definition (unit of meaning in the context of a discourse) but can also be approximated via lists of lemmata and thus identified automatically in corpora, forming a link between qualitative and quantitative methods. we enclose references to discoursemes in angle brackets, e.g. ⟨origin⟩ and ⟨push factors⟩ for the groups mentioned above. the node of the collocation analysis is also understood as a discourseme ⟨refugees⟩, which just happens to be defined a priori by the researcher rather than via grouping collocations. it is worth pointing out that not all occurrences of a lemma will always belong to the corresponding discourseme. for instance, the lemma flood will typically be assigned to the metaphor discourseme ⟨flood of people⟩ in a migration context, but its occurrence in displaced families are uprooted again by severe floods does not belong to the discourseme. our operationalisation of discoursemes as manually formed groups of lemmata must thus be considered an approximation, since there will be false positives (occurrences of these items that do not in fact belong to the discourseme) and false negatives (occurrences of the discourseme that are realised through other linguistic expressions that are not frequent enough to show up among the keywords and collocations). our approach also recognises explicitly that discursive patterns do not arise from individual discoursemes (as the qualitative interpretation in traditional cads might suggest), but rather from constellations of discoursemes. the discourseme ⟨flood of people⟩ mentioned above might comprise lemmata like flood, surge, or pour into, but they only evoke the discursive pattern "migrants as a flood of people" when used in conjunction with ⟨refugees⟩, ⟨migration⟩ or a simi-lar discourseme. such constellations are often implicit in cads studies: e.g. groups of collocates form discoursemes that co-occur in a constellation with the node discourseme of the collocation analysis. we make this explicit in our approach, where the node of a collocation analysis is always a discourseme. it is noteworthy that discourseme constellations provide a partial solution to the lack of (discourse-specific) word sense disambiguation discussed above, due to the mutual disambiguation of discoursemes within a constellation (e.g. displacement is unlikely to refer to a car engine when used in conjunction with the discourseme ⟨migration⟩). our proposed operationalisation in terms of discoursemes and discourseme constellations offers several important advantages for future cads research: ## working with discoursemes as a running example, we will look into germa-parl 4 , a corpus of all debates of the german federal parliament. our goal is to describe discoursemes (via lists of lemmata) and to combine them into constellations that approximate discursive patterns, e.g. the framing of refugees as human beings in need for protection or questioning the legitimacy of seeking asylum. discoursemes can be created from the results of a collocation analysis, which puts them in a con-stellation with the node discourseme of the analysis. we focus on the discourse around the discourseme ⟨fleeing⟩ (our "topic discourseme") in legislative period 19 (lp19), and on the parliamentary groups bündins90/die grünen (gruene, a left-leaning environmentalist party) and alternative für deutschland (afd, a right-wing populist party). however, we understand discourseme formation as an iterative process in which (i) different parameter settings for the same analysis can be used (e.g. different association measures or context definitions), and (ii) multiple analyses can be carried out (e.g. for different node discoursemes or (sub-)corpora). we concentrate on the formation of discoursemes via collocation analysis here. the api also supports an approach via keyword analysis (with a somewhat easier implementation). of course, both approaches can be combined in a single study. as mentioned above, discourseme descriptions for a given corpus are usually obtained by manually selecting lemmata from an n-best list of keywords or collocations, but they can also include multiword units. frequency counts for discoursemes are obtained in the same way as for individual lemmata, i.e. by counting all their occurrences in the corpus; some special precautions are necessary if a discourseme contains multi-word units (see section 3.2). such frequency counts are the basis for discourseme assocations (section 3.6) as well as for further quantiative analyses. the topic discourseme plays a special role in that it has to be defined a priori, and researchers have to take care not to miss relevant lemmata (or introduce false positives). for the example at hand, a manually curated list of lemmata is used based on the cqp query additional candidates can be suggested via semantic similarity search in word embeddings @xcite , which is supported by the api. ## working with the api as outlined in the introduction, interaction with the api is possible both from dedicated guis and through low-level api calls e.g. from widespread languages such as python or r. both interaction methods operate on the same discourseme database, ensuring consistency across tools while giving users the freedom to select the most convenient option for their analysis. typically, a graphical frontend is ideal for tasks such as defining the topic discourseme, forming discoursemes based on collocation profiles or keyords, and examining concordance lines of discoursemes or individual lemmata. we have already experimented with a prototype frontend foot_10 and are currently developing an improved version, which can be found in the cwb-cads repository linked above. development of the new frontend, implemented in react, focusses on flexible selection of semantic maps, more straightforward definitions of mwus, and efficient recalculations. for operations such as analysing the distribution of discoursemes or discourseme networks across metadata variables, or exporting discourseme descriptions and individual concordance lines for research documentation, api calls are more suitable. manuals for working with the api are available in the cwb-cads repository. ## conclusion and future work we have presented a cwb-based rest api that aims to provide convenient methods for cads researchers, offering a variety of parameter choices to enable customised and comprehensive research. we have outlined the available parameters and provided guidelines on making reasonable selections. a significant contribution of our work is the conceptual and technical framework for working with manually defined semantic groups ("discoursemes"). the paper includes details on the calculation of discourseme scores and how to tackle the challenges associated with multi-word units (mwus) and overlapping discoursemes. it is worth mentioning that our approach necessarily shares the same limited perspective on discourse as classic cads. working on word or lemma types means neglecting word-sense or discourse-specific disambiguation. however, this is somewhat mitigated by the fact that discoursemes are mutually disambiguated within constellations. we plan to expand the api by adding more parameters to its endpoints, further increasing flexibility. most importantly, while the current implementation only supports pairwise associations of discoursemes, we aim to visualise these associations as discourseme networks.