Diachronic Trends in Syntactic Dependency Structure of English and German Political Debates

Core Concepts

Syntactic dependency structure in English and German political debates has become more optimized for lower complexity and higher communication efficiency over the past 160 years, as evidenced by decreasing mean dependency distance and other graph-based metrics.

Abstract

The paper investigates diachronic trends in syntactic language change in English and German using parliamentary debate corpora spanning the last 160 years. The analysis goes beyond just mean dependency distance and explores 15 metrics relevant to dependency distance minimization (DDM) and/or based on tree graph properties.
Key highlights:

The authors use 5 different dependency parsers, including the widely used Stanford CoreNLP as well as 4 newer alternatives, to analyze the syntactic changes.
They find that results of syntactic language change are sensitive to the parsers involved, cautioning against using a single parser as done in previous work.
Syntactic language change over the time period is largely similar between English and German, with only 4% of cases yielding opposite conclusions.
Changes in syntactic measures seem to be more frequent at the tails of sentence length distributions.
This is the most comprehensive analysis of syntactic language change using modern NLP technology in recent corpora of English and German.

Stats

"Many studies have shown that human languages tend to optimize for lower complexity and increased communication efficiency."
"Syntactic dependency distance, which measures the linear distance between dependent words, is often considered a key indicator of language processing difficulty and working memory load."
"We base our observations on five dependency parsers, including the widely used Stanford CoreNLP as well as 4 newer alternatives."
"Even though we have evidence that recent parsers trained on modern treebanks are not heavily affected by data 'noise' such as spelling changes and OCR errors in our historic data, we find that results of syntactic language change are sensitive to the parsers involved."
"Syntactic language change over the time period investigated is largely similar between English and German for the different metrics explored: only 4% of cases we examine yield opposite conclusions regarding upwards and downtrends of syntactic metrics across German and English."
"Changes in syntactic measures seem to be more frequent at the tails of sentence length distributions."

Quotes

"Many studies have shown that human languages tend to optimize for lower complexity and increased communication efficiency."
"Syntactic dependency distance, which measures the linear distance between dependent words, is often considered a key indicator of language processing difficulty and working memory load."
"Even though we have evidence that recent parsers trained on modern treebanks are not heavily affected by data 'noise' such as spelling changes and OCR errors in our historic data, we find that results of syntactic language change are sensitive to the parsers involved."

Key Insights Distilled From

Syntactic Language Change in English and German

by Yanran Chen,... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2402.11549.pdf

Deeper Inquiries

How do the observed trends in syntactic dependency structure relate to broader changes in language use, such as shifts in lexical complexity, information density, or rhetorical style over time?

The observed trends in syntactic dependency structure can provide valuable insights into broader changes in language use over time. Changes in dependency distance, mean dependency distance, and other syntactic metrics can reflect shifts in lexical complexity, information density, and rhetorical style.

Lexical Complexity: As languages evolve, there may be changes in the complexity of vocabulary used. An increase or decrease in dependency distance could indicate changes in how complex or specialized the language has become. For example, a decrease in dependency distance may suggest a simplification of language or a shift towards more straightforward expressions.

Information Density: Changes in syntactic structure can also reflect alterations in how information is conveyed in a language. A decrease in dependency distance could indicate a more efficient way of conveying information, leading to higher information density in sentences. On the other hand, an increase in dependency distance may suggest a more elaborate or detailed way of expressing ideas.

Rhetorical Style: Syntactic changes can be linked to shifts in rhetorical style over time. For instance, a trend towards shorter dependency distances may indicate a preference for more direct and concise communication, reflecting a change in rhetorical style towards brevity. Conversely, an increase in dependency distance could signal a shift towards more elaborate or nuanced rhetorical techniques.

By analyzing syntactic language change, researchers can gain insights into how language has evolved in terms of lexical complexity, information density, and rhetorical style over time.

How do the differences in syntactic change between English and German reflect underlying differences in the grammatical structures or language evolution trajectories of the two languages?

The differences in syntactic change between English and German can be attributed to underlying dissimilarities in their grammatical structures and language evolution trajectories. These differences can manifest in various ways:

Grammatical Structures: English and German have distinct grammatical structures, such as word order, case systems, and verb conjugations. These structural differences can influence how syntactic dependencies are formed and expressed in sentences. For example, German has a more flexible word order due to its case system, which can impact dependency distance and syntactic complexity.

Language Evolution Trajectories: The historical development and evolution of English and German have followed different trajectories. English has undergone significant simplification in its grammar and syntax over time, leading to a reduction in inflectional endings and a more analytic structure. In contrast, German has retained a more complex inflectional system and maintains a higher degree of grammatical precision.

Cultural and Linguistic Influences: Cultural factors and linguistic influences can also shape syntactic change in English and German. For instance, the influence of other languages, historical events, and literary traditions can impact the evolution of syntactic structures in each language.

Overall, the differences in syntactic change between English and German reflect the unique grammatical structures and language evolution paths of each language, highlighting the diverse ways in which languages develop and adapt over time.

What other linguistic or cognitive factors, beyond dependency distance minimization, might influence the observed changes in syntactic dependency structure over time?

In addition to dependency distance minimization, several other linguistic and cognitive factors can influence the observed changes in syntactic dependency structure over time:

Language Contact and Borrowing: Contact with other languages can introduce new syntactic structures or influence existing ones. Borrowing syntactic patterns from other languages can lead to changes in dependency structures over time.

Language Register and Style: Variations in language register and style, such as formal versus informal language, can impact syntactic structures. Changes in stylistic preferences or shifts in register usage can influence the syntactic complexity of sentences.

Language Acquisition and Learning: Changes in how languages are acquired and learned by speakers can affect syntactic structures. Evolution in language teaching methods or educational practices can lead to shifts in syntactic patterns over generations.

Cognitive Processing: Cognitive factors, such as working memory capacity and processing efficiency, can influence syntactic structures. Changes in cognitive abilities or processing strategies among speakers can impact the syntactic complexity of language over time.

Language Standardization and Norms: Standardization processes and language norms can shape syntactic structures. Changes in language standards or norms can lead to adjustments in syntactic conventions and dependencies.

By considering these linguistic and cognitive factors alongside dependency distance minimization, researchers can gain a more comprehensive understanding of the factors influencing syntactic changes in language over time.