Low-Resource Techniques for Analysing the Rhetorical Structure of S wedish Historical Petitions (2023)

Abstract: Natural language processing techniques can be valuable for improving and facilitating historical research. This is also true for the analysis of petitions, a source which has been relatively little used in historical research. However, limited data resources pose challenges for mainstream natural language processing approaches based on machine learning. In this paper, we explore methods for automatically segmenting petitions according to their rhetorical structure. We find that the use of rules, word embeddings, and especially keywords can give promising results for this task.

## introduction digitisation of historical sources supports the aim of preserving cultural heritage, makes the sources more accessible, and enables the use of computational techniques to study them further. yet, the often limited data resources pose challenges for standard natural language processing methods based on machine learning. in this paper, we study the problem of automatically segmenting a specific type of historical text -petitions -using natural language processing techniques, while dealing with extreme data limitations. petitions have been a means for ordinary people in many pre-modern and pre-democratic societies to seek assistance from those in power. petitions were typically directed towards a social or economic superior, such as a court of law, parliament, landlord, or even the monarch @xcite . as a valuable historical source, petitions could shed light on the daily lives of common people in the past. despite this, petitions have rarely been used in historical research. therefore, we are participating in an interdisciplinary research project, initiated at uppsala university and funded by the swedish research council, with the aim of increasing access to and knowledge of swedish 18th century petitions. 1 the goal of the project is to use this source of information to gain insights into how people supported themselves and asserted their rights in the past. the project is entitled speaking to one's superiors: petitions as cultural heritage and sources of knowledge, and is led by the gender and work (gaw) research project at the department of history, uppsala university. the gaw project examines how women and men provided for themselves in the early modern swedish society (approximately 1550-1880). as part of the project, thousands of historical sources have been collected, classified, and stored in a unique database, which is now accessible to researchers, students, and the general public. the structure of petitions were in many parts of europe based on a classical rhetorical division. with some variations, most petitions seem to be comprised of five sections: salutatio, exordium, narratio (including argumentatio), petitio, and conclusio @xcite . this paper delves into the use of computational approaches to segment petitions by automatic means, with the main goal of facilitating and enhancing the task of information extraction for historians and other scholars interested in studying petitions. we here target two specified sections of the petitions: the introduction (salutatio) and the ending (conclusio). by categorising different sections of the petitions, this segmentation approach could help researchers target only the relevant portions and search for information more efficiently, as the type of information varies in the different parts of the petitions. however, due to significant limitations in available data resources, careful considerations are needed in the development of such ap-proaches, where our toolbox includes a combination of rules, keywords and word embeddings. previous work related to text or rhetorical structure segmentation for modern text often make use of large pre-trained language models, like in @xcite , or the creation of a domain specific corpus, for example as a list of predefined rhetorical roles applied to legal documents @xcite . however, no previous work has, to the best of our knowledge, focused on the specific task of automatically segmenting petitions, with the challenges of working with historical text and (in our case) a very limited set of available data resources. ## the petition data sets in our project, we make use of a transcribed collection of 18th century petitions submitted to the regional administration in örebro, sweden, divided into two subsets dating from 1719 and 1782, respectively. to test the generalisability of our methods, we also include a small additional set of petitions from another region in sweden, since there might exist some regional differences in how petitions were constructed. the latter data set, which we from now on describe as "out-ofdomain" (ood), is a small collection of manually transcribed petitions from västmanland, sweden. all data sets, together with some statistics, are described in as stated in the introduction, petitions were often written in five or six sections @xcite : (i) salutatio: formal greeting to the addressee; (ii) exordium: introduction of the petitioner(s); (iii) narratio: narration of the circumstances leading to the petition (often mixed with arguments (iv) argumentatio); (v) petitio: presentation of the request; and (vi) conclusio: final part, with ending and plea. we are interested in testing computational methods for segmenting the petitions automatically, with the main purpose of facilitating and enhancing the task of information extraction for historians and other scholars. as a start, we focus on the introduction (salutatio) and the ending (conclusio), since these parts mostly consist of greetings and courtesy markers, and would typically not be of much interest for the information extraction task. thus, being able to automatically re-move these parts before analysing the text, would mean less noise in the information extraction process. we divide the örebro data set into a training set (70%) and a test set (30%). in the training set, 10 petitions have been manually annotated by a historian (including all of the petition parts described above). we use this part of the training set as a validation set when needed during the development of our methods. the ood data set is included in the test set only. the test set of our collection was manually segmented (only the salutatio and conclusio parts) by two of the authors independently. the segmentation showed a high inter-annotator agreement with 36/41 petitions being segmented identically. the 5 remaining cases showed minor discrepancies, which were resolved after discussion. ## methodology: finding salutatio and conclusio working with historical data poses challenges for computational methods due to the often limited data resources. in addition to this, historical data is often noisy with variations in orthography, which makes pre-processing important. below we first describe the different pre-processing steps used and then move on to the different segmentation methods explored, as well as the evaluation procedure. ## results and discussion the results for our methods can be viewed in for a start, we conclude that even though we work with extremely reduced resources in terms of data size (and noisy data on top of that), the results indicate that several of our approaches can be potentially useful for segmenting petitions. we see that the baseline method performs strongly when extracting the salutatio part, which indicates that salutatio is easier to correctly catch in comparison to conclusio. this is not surprising, since the conclusio contains a formal farewell and is often signed with names of the petitioner(s), and the number of these may differ. since the names are often written with line separation, they are treated as several sentences by our sentence segmentation approach, which lowers the performance of the baseline method. and indeed, some of our methods are able to beat the baseline method for extracting the conclusio part, though high standard deviations also suggest that the performance for conclusio is more varied both between and within the methods. overall, the keyword method generally performs well, and gets the most consistent high results for the in-domain data set. another observation is that the gain of using spelling normalisation varies between the methods. the window embedding approach performs considerably better at extracting salutatio on the normalised data set, in comparison of their results for the un-normalised data set. also the rulebased method is helped by including spelling normalisation, both when extracting salutatio and when extracting conclusio. however, the keyword method works well both on the normalised and un-normalised data set, and in some cases the results are even better when not including spelling normalisation. to see how well our methods can generalise to unseen data, we look at the results for the ood set. however, since there are very few data points in the ood set, it is difficult to draw any certain conclusions, though we can spot some potential trends. the results for all methods are generally low when handling un-normalised data. here, normalisation seems to help quite substantially. the window embedding approach gets high results for normalised salutatio, while the performance of the keyword method drops significantly, especially for the task of extracting conclusio. the baseline method works surprisingly well for salutatio and gets the highest result for the un-normalised ood data, suggesting that the length of salutatio may be somewhat consistent between regional places. for extracting conclusio, the keword method and the rule-based method seem to perform the best, at least on the normalised data set. however, the performance for extracting conclusio is low for all methods, indicating that regional differences may affect the composition of this petition part. when performing a qualitative analysis of the extracted salutatio and conclusio parts, we can identify some areas of improvement for our methods. by inspecting the results obtained using the rule method, we found several phrases where errors in the pos tagging led to the incorrect inclusion or exclusion of these phrases. it is important to consider these findings when developing