Basreh or Basra? Geoparsing Historical Locations in the Svoboda Diaries (2024)

Abstract: Geoparsing, the task of assigning coordinates to locations extracted from free text, is invaluable in enabling us to place locations in time and space. In the historical domain, many geoparsing corpora are from large news collections. We examine the Svoboda Diaries, a small historical corpus written primarily in English, with many location names in transliterated Arabic. We develop a pipeline employing named entity recognition for geotagging, and a map-based generate-and-rank approach incorporating candidate name augmentation and clustering of location context words for geocoding. Our system outperforms existing map-based geoparsers in terms of accuracy, lowest mean distance error, and number of locations correctly identified. As location names may vary from those in knowledge bases, we find that augmented candidate generation is instrumental in the system’s performance. Among our candidate generation methods, the generation of transliterated names contributed the most to increased location matches in the knowledge base. Our main contribution is proposing an integrated pipeline for geoparsing of historical corpora using augmented candidate location name generation and clustering methods – an approach that can be generalized to other texts with foreign or non-standard spellings.

## introduction in the digital humanities, natural language processing tasks such as named entity recognition (ner) and named entity linking (nel) are valuable for connecting historical information to present-day knowledge. the digitization of historical documents involves additional challenges that can impact the quality of ner and nel results, including patterns of word usage that can differ from modern-day language, and bias from optical character recognition (ocr) in the digitization process @xcite . personal documents such as diaries present additional challenges in language processing, such as the personal shorthand of diary authors. geoparsing, the task of assigning coordinates to locations extracted from free text, is an important task in the digital humanities for understanding the geospatial information embedded in documents. however, geoparsing usually requires large and heterogeneous data sources @xcite . historical ner corpora @xcite and recommended geoparsing datasets wiktor, local global lexicon (lgl), tr-news, and geoweb-news @xcite are predominantly news corpora that involve a large volume of data. frequently used historical geoparsing corpora such as the war of the rebellion (wotr) @xcite , a collection of official records, and corpus of lake district writing (cldw) @xcite , are also large. geoparsing systems designed for the digital humanities, such as the edinburgh geoparser @xcite , require large historical gazetteers or news collections for location data @xcite . many geoparsers focus on disambiguation, which involves identifying the correct location to match a name from a pool of potential candidates. a challenge is that data may not exist for some locations. this is especially relevant for historical documents because the location name may have changed or is too fine-grained to be identified. documents can also incorporate foreign words or use non-standard spellings, exacerbating the challenge of retrieving correct entries from gazetteers. we explore the following primary research questions: how do geoparsing methods perform on a small historical corpus? how can data augmentation via candidate name generation increase the effectiveness of geoparsing methods? we examine geoparsing in a small corpus of personal diaries by joseph svoboda. the svoboda diaries pose a unique challenge in that they are written primarily in english, but most of the location names originate from arabic. in combination with the personal nature of the document, the author is likely to spell names differently from modern-day standards for location names in english, making it challenging to successfully retrieve coordinate data from modernday gazetteers. we tackle this task by generating candidates from knowledge bases and ranking them, assigning the highest-ranking coordinates to the target location. in this paper, we perform the following tasks: 1) perform geotagging using an ner + nel pipeline adapted from extant literature; 2) develop a mapbased generate-and-rank geoparsing method with enhanced candidate generation and compare it to other methods; and 3) examine the candidate generation portion in our proposed method to elucidate the most important contributors to its performance. we demonstrate that candidate name generation through the generation of orthographic variants of toponym names and clustering of toponym context together, can serve as a powerful approach to identifying suitable coordinates in historical corpora containing location names from other languages. ## methods we develop a map-based generate-and-rank approach for geoparsing in a small historical corpus of diary entries from ottoman iraq written primarily in english but including transliterated arabic locations. we incorporate a data augmentation step in the pipeline, involving generation of candidate names that could increase potential matches against a knowledge base. additionally, as most of the toponyms in the corpus are limited to a particular geographical region, we employ a clustering approach, using context words to prefer likely spatial regions for the locations. the small size and restricted geographic scope of the corpus pose severe limitations in terms of suitable training data. as deep learning methods require large amounts of labelled coordinate data or are trained on more global and generalized corpora, we did not find them to be effective for retrieving or inferring viable coordinates for toponyms. foot_1 ## conclusion we present an approach to address the challenge of geoparsing on a small historical corpus. our approach combines named entity recognition and coreference resolution to identify location entities, then augments candidate name generation using multiple methods, and lastly, employs a map-based cluster-and-rank approach to identify appropriate geographic coordinates. compared to existing systems, our approach substantively increases viable candidate locations generated, and in doing so, facilitates the identification of appropriate coordinates for finer-grained entities. aside from the substantive performance increase over existing methods, our approach holds some promise in terms of translation to other contexts. as society becomes increasingly globalized, with more communication between people of different cultures, more foreign words are included in text and exchanged. language also appears to become more standardized, but minority variants of language remain important to include in natural language processing tasks. the geocoding approach that we used can be leveraged in the context of other research involving identification of non-standard spellings of locations, particularly in situations where data is limited. in addition, the methods we employed in candidate name generation can make working with texts including words of foreign origin easier, which is particularly important in an increasingly multilingual society.