A universal alphabet for experiments in comparative phonology

Hartman, Steven

doi:10.1007/bf02404201

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Some of the early studies on partially automating proto-language reconstruction belong to Covington (1998) (investigating multiple alignment for historical comparison), and Kondrak (2002) (proposing, among others, methods for cognate alignment and identification). Most of the previous approaches to producing related words relied on phonetic transcriptions (Eastlack 1977;Hartman 1981;Hewson 1974). They built on the idea that, given the phonological context, sound changes follow certain regularities across the entire vocabulary of a language.…”

Section: Production Of Related Wordsmentioning

confidence: 99%

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Automatic Identification and Production of Related Words for Historical Linguistics

Ciobanu

Dinu

2020

Computational Linguistics

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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.

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Section: Production Of Related Wordsmentioning

confidence: 99%

“…They built on the idea that, given the phonological context, sound changes follow certain regularities across the entire vocabulary of a language. The proposed methods (Hewson 1974;Eastlack 1977;Hartman 1981) required a list of known sound correspondences as input, collected from dictionaries or published studies.…”

Section: Production Of Related Wordsmentioning

confidence: 99%

Automatic Identification and Production of Related Words for Historical Linguistics

Ciobanu

Dinu

2020

Computational Linguistics

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“…Although their use in phonology would have been simpler, that seems to have begun later and remained a minor field (cf. Hartman : 81 n. 1): in 1968 Bobrow & Fraser, sponsored by the U.S. Department of Defense, wrote a ‘phonological rule tester’, a program to evaluate all the rules just published in Chomsky and Halle’s Sound pattern of English (1968), and in the same year Grimes et al. () implemented a program to evaluate Wares’s phonemic analysis of a Yuman language of the Colorado delta.…”

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confidence: 99%

“… See Damerau (: 8). I have not found any complete surveys of my topic, but the following were useful on various aspects: Hartman (); Hewson (: especially); Bird (); Kondrak (); Borin (); Kessler (; especially). Many computational approaches to language change lie outside the scope of this paper; see Baker (); Nichols & Warnow (). …”

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Mechanising Historical Phonology

Sims-Williams¹

2018

Trans of the Philol Soc

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The Neogrammarian approach to historical phonology involves propounding soundchange laws and explaining exceptions by means such as sub-laws, rearranging the relative chronology, and appeal to special factors such as analogy, borrowing, and sporadic phenomena like metathesis. Progress is mostly made manually, but in the second half of the twentieth century some linguists looked forward to the 'triumph of the electronic Neogrammarian'. Although this has not been realized yet, there are opportunities to make important advances. This paper offers a critical survey of the field since the 1950s and suggestions for the future.

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Romanian Word Production: An Orthographic Approach Based on Sequence Labeling

Dinu

Ciobanu

2018

Computational Linguistics and Intelligent Text Processing

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A universal alphabet for experiments in comparative phonology

Cited by 8 publications

References 6 publications

Automatic Identification and Production of Related Words for Historical Linguistics

Automatic Identification and Production of Related Words for Historical Linguistics

Mechanising Historical Phonology

Romanian Word Production: An Orthographic Approach Based on Sequence Labeling

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