Detecting Lexical Borrowings from Dominant Languages in Multilingual Wordlists (2023)

Abstract: Language contact is a pervasive phenomenon reflected in the borrowing of words from donor to recipient languages. Most computational approaches to borrowing detection treat all languages under study as equally important, even though dominant languages have a stronger impact on heritage languages than vice versa. We test new methods for lexical borrowing detection in contact situations where dominant languages play an important role, applying two classical sequence comparison methods and one machine learning method to a sample of seven Latin American languages which have all borrowed extensively from Spanish. All systems perform well, with the supervised machine learning system outperforming the classical systems. A review of detection errors shows that borrowing detection could be substantially improved by taking into account donor words with divergent meanings from recipient words.

## introduction language contact is one of the most pervasive linguistic phenomena. it is the first factor that needs to be excluded when searching for genetic relations among the languages of the world, and it needs to be controlled for when searching for crosslinguistic universals. any study trying to explain how humans use language must take language contact into account. it is also a unique witness of ancient contacts in human prehistory. language contact is most prominently reflected in lexical borrowing, the transfer of words from a donor language to a recipient language. although research on the computational handling of lexical borrowing has made some progress of late, computational approaches to the investigation of language contact are still in their infancy @xcite . specifically methods that could infer the direction of borrowings have not been proposed so far. while numerous case studies investigate the influence of dominant languages on heritage languages @xcite , we lack automated methods that could be used to study the influence of particular dominant languages in linguistically diverse areas of the world. the number of standardized cross-linguistic wordlist collections has greatly increased over the past years @xcite , with standard formats @xcite accompanied by software tools that allow scholars to prepare and curate standardized wordlists in an efficient manner @xcite . it would be useful to have a computer assisted tool for linguists to detect which words in a cross-linguistic region have been borrowed from a dominant language and with further advances, a tool for inclusion in language contact assessment or in computational cladistics workflows. here, we compare the suitability of three different systems to address this task -two systems based on classical algorithms for automated sequence comparison that can be applied in supervised and unsupervised settings, and one supervised system based on extended machine learning techniques. we test these systems on a newly derived dataset of seven latin american languages (fig. 1 ) in which spanish is a dominant donor language. our results show that the supervised machine-learning system outperforms the classical systems. ## previous work although still few in number, automatic methods for borrowing detection have been increasingly applied and developed in the past years. early studies by @xcite and later @xcite compute edit distances between words from genetically unrelated languages and compare distances to thresholds, in order to detect borrowed words in multilingual wordlists. besides edit distance, which directly calculates distances between phonetic sequences, sound class based methods cluster phonetic segments into sound classes and then compute distances between sound class sequences. the sound-class based alignment method (sca) @xcite provides sound class categories, scoring functions for distance measures, and modifiable gap scores based on prosodic context. @xcite compare edit distance performance against sca @xcite distance performance, finding that sca outperforms edit distance in accuracy. @xcite build on this work, using dedicated methods for automated cognate detection applied to languages from different language families in order to identify clusters of related words resulting from lexical borrowing. @xcite expand this work further, by applying a two-stage workflow in which they first identify language-family-internal cognates, using a method specifically apt for the detection of deep cognates, and then compute sca distances between cognate sets from genetically unrelated languages in order to infer sets of words related by lexical transfer. @xcite compute language models for inherited and borrowed words for individual languages from the world loanword database (wold, haspelmath and tadmor 2009) using markov chains and recursive neural networks and compare cross-entropies for inherited and borrowed language models in order to identify borrowings from monolingual information alone. kaiping and klamer (2022) use automated methods for cognate detection @xcite on a target set of timor-alor-pantar languages. in order to infer borrowings from indonesian and tetun (not in the target set), they include both languages in their sample and treat all cognate sets that involves words from either of the two languages as borrowings. @xcite apply a similar approach to investigate borrowings in alorese. @xcite and @xcite train binary classifiers, mainly neural based, on large wordlists to predict borrowed words, and achieve f1 scores in the 0.75 to 0.85 range. their workflows seem cumbersome, compute intensive, and not minimalist, but the results are promising. ## results and discussion we tested our three methods with two distance measures in five experiments (normalized edit and sca distances individually in both closest match and cognate-based methods, and combined in the classifier-based method) using a 10-fold crossvalidation on our data and reporting precision, recall, f1 scores, accuracy, and execution times (mm:ss). f1 score is the primary result measure; accuracy and execution time are informational. the 10-fold cross-validation uses the same 10 fixed train and non-overlapping test splits for all experiments. with few parameter estimates (1 threshold each for closest match and cognate-based, 2 distance and 7 target language coefficients for classifier), a separate train split into fit/val is not necessary. all methods perform well with less than 5 points separating the highest from the lowest f1 scores. tab. 2 shows the results of the ten-fold cross validation of our three methods in five experiments. an analysis of variance, 1 with experiment as the effects variable and train-test split as the nuisance variable, shows highly significant effects for precision (f 4,36 = 25.74, @xmath0.0001) and f1 score (f 4,36 = 14.3, @xmath1.0001). ## conclusion how well can we automatically detect borrowings from dominant languages based on wordlist data? we devised three general methods to detect borrowed words from dominant languages, two based on sequence comparison workflows and one based on a classifier. the classifier-based method showed the best performance, with f1 scores of 0.81, and high precision of 0.93. this method could already prove very useful in computer-assisted workflows. our investigation of detection errors shows several opportunities for improvement. most undetected borrowings result from current methods' restriction to searching only for word forms for the same concept. we see great potential in improvements that account for borrowing accompanied by semantic shift, specifically: (1) augment donor wordlist coverage of possible forms, (2) relax "same" to "similar" concept restriction for matching forms, or (3) fit language models to wordlists and add word cross-entropy to the classifier without restriction. tests adding word cross-entropy, not reported here, look very promising, but more research is needed.