paper-with-me

홈 › Papers

Can Linguistic Distance help Language Classification? Assessing Hawrami-Zaza and Kurmanji-Sorani

2021-10-27 · Hossein Hassani

To consider Hawrami and Zaza (Zazaki) standalone languages or dialects of a language have been discussed and debated for a while among linguists active in studying Iranian languages. The question of whether those languages/dialects belong to the Kurdish language or if they are independent descendants of Iranian languages was answered by MacKenzie (1961). However, a majority of people who speak the dialects are against that answer. Their disapproval mainly seems to be based on the sociological, cultural, and historical relationship among the speakers of the dialects. While the case of Hawrami and Zaza has remained unexplored and under-examined, an almost unanimous agreement exists about the classification of Kurmanji and Sorani as Kurdish dialects. The related studies to address the mentioned cases are primarily qualitative. However, computational linguistics could approach the question from a quantitative perspective. In this research, we look into three questions from a linguistic distance point of view. First, how similar or dissimilar Hawrami and Zaza are, considering no common geographical coexistence between the two. Second, what about Kurmanji and Sorani that have geographical overlap. Finally, what is the distance among all these dialects, pair by pair? We base our computation on phonetic presentations of these dialects (languages), and we calculate various linguistic distances among the pairs. We analyze the data and discuss the results to conclude.

📄 PDF Abstract BibTeX arXiv:2110.14398

Code (1)

kurdishblark/dialect-classification 공식 구현

Similar Papers 제목 키워드 기반

Exploring language relations through syntactic distances and geographic proximity

2024-03-27 · Juan De Gregorio, Raúl Toral, David Sánchez

Languages are grouped into families that share common linguistic traits. While this approach has been successful in understanding genetic relations between diverse languages, more analyses are needed to accurately quanti…

POS

Pretrained Multilingual Transformers Reveal Quantitative Distance Between Human Languages

2026-03-18 · Yue Zhao, Jiatao Gu, Paloma Jeretič, Weijie Su arxiv

Understanding the distance between human languages is central to linguistics, anthropology, and tracing human evolutionary history. Yet, while linguistics has long provided rich qualitative accounts of cross-linguistic v…

Machine Translation

Coursebook Texts as a Helping Hand for Classifying Linguistic Complexity in Language Learners' Writings

2016-12-01 · WS 2016 12 · Ildik{\'o} Pil{\'a}n, David Alfter, Elena Volodina

We bring together knowledge from two different types of language learning data, texts learners read and texts they write, to improve linguistic complexity classification in the latter. Linguistic complexity in the foreig…

ClassificationDomain AdaptationGeneral Classification

Linguistic classification: dealing jointly with irrelevance and inconsistency

2019-09-01 · RANLP 2019 9 · Laura Franzoi, Andrea Sgarro, Anca Dinu, Liviu P. Dinu

In this paper, we present new methods for language classification which put to good use both syntax and fuzzy tools, and are capable of dealing with irrelevant linguistic features (i.e. features which should not contribu…

ClassificationGeneral Classification

Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks

2017-06-06 · Lawrence Phillips, Nathan Hodas

Increasingly, cognitive scientists have demonstrated interest in applying tools from deep learning. One use for deep learning is in language acquisition where it is useful to know if a linguistic phenomenon can be learne…

Deep LearningLanguage Acquisition