Automated creation of parallel Bible corpora with cross-lingual semantic concordance

Abstract

Here we present a novel approach for automated creation of parallel New Testament corpora with cross-lingual semantic concordance based on Strong's numbers. As scientific editions and translations of Bible texts are often not free to use for scientific purposes and are rarely free to use, and due to the fact that the annotation, curation and quality control of alignments between these texts are quite expensive, there is a lack of available Biblical resources for scholars. We present two approaches to tackle the problem, a dictionary-based approach and a Conditional Random Field (CRF) model and a detailed evaluation on annotated and non-annotated translations. We discuss a proof-of-concept based on English and German New Testament translations. The results presented in this paper are novel and according to our knowledge unique. They present promising performance, although further research is necessary.

Automated annotation of parallel bible corpora with cross-lingual semantic concordance

“…See Table 3 for a detailed overview. Here, the same target and source texts lead to a significantly better result than the results presented in Dörpinghaus and Düing (2021). Most interestingly, the F 1 score is high not only for the same input and output but also for the other texts.…”

“…However, their work based on the Berkeley Word Aligner required 70 volunteers to complete the work. Problems with AI methods on biblical texts have also been identified by Dörpinghaus and Düing (2021). Only a little research has been done on the Qur'an (see Muhammad 2012).…”

“…For example, if L is ancient Greek, it is unclear whether this approach will work for languages such as English or German. Also, there are no language models for Ancient Greek or Hebrew, see Dörpinghaus and Düing (2021) and the survey provided by Sommerschield et al (2023). • If L and L are the same language and annotated texts exist, the task is reduced to finding the match for a given part of speech.…”

“…In our case, we extracted all the translations used by a particular Bible for a given Strong's number, since Dörpinghaus and Düing (2021) showed several difficulties when working with existing dictionaries. Although further research could be done using resources such as the lexicalsemantic network for German "GermaNet" (see Kunze and Wagner 2001), or the lexical database "WordNet" (see Miller 1995).…”

“…Our approach is limited to the mapping between translated words, given both the translation with or without further information, and the Greek source with morphological information. This paper is a revised, improved, and extended version of Dörpinghaus and Düing (2021). Here, it was shown that AI approaches based on CRFs do not perform as well as rule-based algorithmic approaches, for example, with an F 1 -score of 0.13 compared to 0.84 for Luther 1912.…”