Identifying the type of relationship between words (cognates, borrowings, inherited) provides a deeper insight into the history of a language and allows for a better characterization of language relatedness. In this paper, we propose a computational approach for discriminating between cognates and borrowings, one of the most difficult tasks in historical linguistics. We compare the discriminative power of graphic and phonetic features and we analyze the underlying linguistic factors that prove relevant in the classification task. We perform experiments for pairs of languages in the Romance language family (French, Italian, Spanish, Portuguese, and Romanian), based on a comprehensive database of Romance cognates and borrowings. To our knowledge, this is one of the first attempts of this kind and the most comprehensive in terms of covered languages.

Liviu P Dinu, Ana Sabina Uban, Alina Maria Cristea, Ioan-Bogdan Iordache, Teodor-George Marchitan, Simona Georgescu, Laurentiu Zoicas. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

The identification of cognates is a fundamental process in historical linguistics, on which any further research is based.Even though there are several cognate databases for Romance languages, they are rather scattered, incomplete, noisy, contain unreliable information, or have uncertain availability.In this paper we introduce a comprehensive database of Romance cognates and borrowings based on the etymological information provided by the dictionaries (the largest known database of this kind, in our best knowledge).We extract pairs of cognates between any two Romance languages by parsing electronic dictionaries of Romanian, Italian, Spanish, Portuguese and French.Based on this resource, we propose a strong benchmark for the automatic detection of cognates, by applying machine learning and deep learning based methods on any two pairs of Romance languages.We find that automatic identification of cognates is possible with accuracy averaging around 94% for the more difficult task formulations.

Producing related words is a key concern in historical linguistics. Given an input word, the task is to automatically produce either its proto-word, a cognate pair or a modern word derived from it. In this paper, we apply a method for producing related words based on sequence labeling, aiming to fill in the gaps in incomplete cognate sets in Romance languages with Latin etymology (producing Romanian cognates that are missing) and to reconstruct uncertified Latin words. We further investigate an ensemble-based aggregation for combining and re-ranking the word productions of multiple languages.

Language change across space and time is one of the main concerns in historical linguistics. In this article, we develop tools to assist researchers and domain experts in the study of language evolution. First, we introduce a method to automatically determine whether two words are cognates. We propose an algorithm for extracting cognates from electronic dictionaries that contain etymological information. Having built a data set of related words, we further develop machine learning methods based on orthographic alignment for identifying cognates. We use aligned subsequences as features for classification algorithms in order to infer rules for linguistic changes undergone by words when entering new languages and to discriminate between cognates and non-cognates. Second, we extend the method to a finer-grained level, to identify the type of relationship between words. Discriminating between cognates and borrowings provides a deeper insight into the history of a language and allows a better characterization of language relatedness. We show that orthographic features have discriminative power and we analyze the underlying linguistic factors that prove relevant in the classification task. To our knowledge, this is the first attempt of this kind. Third, we develop a machine learning method for automatically producing related words. We focus on reconstructing proto-words, but we also address two related sub-problems, producing modern word forms and producing cognates. The task of reconstructing proto-words consists of recreating the words in an ancient language from its modern daughter languages. Having modern word forms in multiple Romance languages, we infer the form of their common Latin ancestors. Our approach relies on the regularities that occurred when words entered the modern languages. We leverage information from several modern languages, building an ensemble system for reconstructing proto-words. We apply our method to multiple data sets, showing that our approach improves on previous results, also having the advantage of requiring less input data, which is essential in historical linguistics, where resources are generally scarce.

Proto-word reconstruction is central to the study of language evolution. It consists of recreating the words in an ancient language from its modern daughter languages. In this paper we investigate automatic word form reconstruction for Latin proto-words. Having modern word forms in multiple Romance languages (French, Italian, Spanish, Portuguese and Romanian), we infer the form of their common Latin ancestors. Our approach relies on the regularities that occurred when the Latin words entered the modern languages. We leverage information from all modern languages, building an ensemble system for proto-word reconstruction. We use conditional random fields for sequence labeling, but we conduct preliminary experiments with recurrent neural networks as well. We apply our method on multiple datasets, showing that our method improves on previous results, having also the advantage of requiring less input data, which is essential in historical linguistics, where resources are generally scarce.

We propose a sequence labeling approach to cognate production based on the orthography of the words. Our approach leverages the idea that orthographic changes represent sound correspondences to a fairly large extent. Given an input word in language L1, we seek to determine its cognate pair in language L2. To this end, we employ a sequential model which captures the intuition that orthographic changes are highly dependent on the context in which they occur. We apply our method on two pairs of languages. Finally, we investigate how second language learners perceive the orthographic changes from their mother tongue to the language they learn.

We train and evaluate two models for Romanian stress prediction: a baseline model which employs the consonant-vowel structure of the words and a cascaded model with averaged perceptron training consisting of two sequential models ‐ one for predicting syllable boundaries and another one for predicting stress placement. We show in this paper that Romanian stress is predictable, though not deterministic, by using data-driven machine learning techniques.

Vlad Niculae, Marcos Zampieri, Liviu Dinu, Alina Maria Ciobanu. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers. 2014.

In this paper we propose a computational method for determining the orthographic similarity between Romanian and related languages.We account for etymons and cognates and we investigate not only the number of related words, but also their forms, quantifying orthographic similarities.The method we propose is adaptable to any language, as far as resources are available.