A Probabilistic Approach in Historical Linguistics Word Order Change in Infinitival Clauses: from Latin to Old French

• URL: http://arxiv.org/abs/2011.08262v1
• Date: Mon, 16 Nov 2020 20:30:31 GMT
• Title: A Probabilistic Approach in Historical Linguistics Word Order Change in Infinitival Clauses: from Latin to Old French
• Authors: Olga Scrivner
• Abstract summary: This thesis investigates word order change in infinitival clauses in the history of Latin and Old French. I examine a synchronic word order variation in each stage of language change, from which I infer the character, periodization and constraints of diachronic variation. I present a three-stage probabilistic model of word order change, which also conforms to traditional language change patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research offers a new interdisciplinary approach to the field of Linguistics by using Computational Linguistics, NLP, Bayesian Statistics and Sociolinguistics methods. This thesis investigates word order change in infinitival clauses from Object-Verb (OV) to Verb-Object (VO) in the history of Latin and Old French. By applying a variationist approach, I examine a synchronic word order variation in each stage of language change, from which I infer the character, periodization and constraints of diachronic variation. I also show that in discourse-configurational languages, such as Latin and Early Old French, it is possible to identify pragmatically neutral contexts by using information structure annotation. I further argue that by mapping pragmatic categories into a syntactic structure, we can detect how word order change unfolds. For this investigation, the data are extracted from annotated corpora spanning several centuries of Latin and Old French and from additional resources created by using computational linguistic methods. The data are then further codified for various pragmatic, semantic, syntactic and sociolinguistic factors. This study also evaluates previous factors proposed to account for word order alternation and change. I show how information structure and syntactic constraints change over time and propose a method that allows researchers to differentiate a stable word order alternation from alternation indicating a change. Finally, I present a three-stage probabilistic model of word order change, which also conforms to traditional language change patterns.

Related papers

• Syntactic Language Change in English and German: Metrics, Parsers, and Convergences [56.47832275431858]
  The current paper looks at diachronic trends in syntactic language change in both English and German, using corpora of parliamentary debates from the last c. 160 years. We base our observations on five dependencys, including the widely used Stanford Core as well as 4 newer alternatives. We show that changes in syntactic measures seem to be more frequent at the tails of sentence length distributions.
  arXiv  Detail & Related papers  (2024-02-18T11:46:16Z)
• Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivity [68.8204255655161]
  We present the semantic notion of agentivity as a case study for probing such interactions. This suggests LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery.
  arXiv  Detail & Related papers  (2023-05-29T16:24:01Z)
• Reliable Detection and Quantification of Selective Forces in Language Change [3.55026004901472]
  We apply a recently-introduced method to corpus data to quantify the strength of selection in specific instances of historical language change. We show that this method is more reliable and interpretable than similar methods that have previously been applied.
  arXiv  Detail & Related papers  (2023-05-25T10:20:15Z)
• Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis [3.515619810213763]
  We propose using automatically generated natural language definitions of contextualised word usages as interpretable word and word sense representations. We demonstrate how the resulting sense labels can make existing approaches to semantic change analysis more interpretable.
  arXiv  Detail & Related papers  (2023-05-19T20:36:21Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Contextualized language models for semantic change detection: lessons learned [4.436724861363513]
  We present a qualitative analysis of the outputs of contextualized embedding-based methods for detecting diachronic semantic change. Our findings show that contextualized methods can often predict high change scores for words which are not undergoing any real diachronic semantic shift. Our conclusion is that pre-trained contextualized language models are prone to confound changes in lexicographic senses and changes in contextual variance.
  arXiv  Detail & Related papers  (2022-08-31T23:35:24Z)
• Computational valency lexica and Homeric formularity [1.6346069386394704]
  We present AGVaLex, a lexicon for ancient Greek automatically extracted from the Ancient Greek Dependency Treebank. It contains quantitative corpus-driven morphological, syntactic and lexical information about verbs and their arguments. It has a wide range of applications for the study of the language of ancient Greek authors.
  arXiv  Detail & Related papers  (2022-08-23T08:03:16Z)
• Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks [58.87961226278285]
  We propose a self-supervised approach to model lexical semantic change. We show that our method can be used for the detection of semantic change with any alignment method. We illustrate the utility of our techniques using experimental results on three different datasets.
  arXiv  Detail & Related papers  (2021-01-30T18:59:43Z)
• Lexical semantic change for Ancient Greek and Latin [61.69697586178796]
  Associating a word's correct meaning in its historical context is a central challenge in diachronic research. We build on a recent computational approach to semantic change based on a dynamic Bayesian mixture model. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models.
  arXiv  Detail & Related papers  (2021-01-22T12:04:08Z)
• On the Importance of Word Order Information in Cross-lingual Sequence Labeling [80.65425412067464]
  Cross-lingual models that fit into the word order of the source language might fail to handle target languages. We investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
  arXiv  Detail & Related papers  (2020-01-30T03:35:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.