Statistically Significant Detection of Linguistic Change (1411.3315v1)

Abstract: We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural LLMs, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track it's linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book-ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.

• The paper proposes a framework that detects linguistic change by analyzing time series of word frequency, syntax, and distributional semantics.
• It validates the approach across datasets like Google Books, Twitter, and Amazon Reviews, highlighting both historical and modern semantic shifts.
• The study demonstrates that distributional methods offer robust sensitivity and specificity, paving the way for enhanced natural language understanding.

Statistical Detection of Linguistic Change: An Expert Overview

The paper "Statistically Significant Detection of Linguistic Change," authored by Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, and Steven Skiena, introduces a computational framework aiming to detect and track linguistic shifts across different temporal corpora. This research emphasizes the dynamic nature of language, particularly in the rapidly evolving digital landscape, by constructing and analyzing time series to capture semantic, syntactic, and usage shifts of words over time.

The authors propose three core methodologies for constructing linguistic property time series: Frequency, Syntactic, and Distributional. Each method che differencing techniques to track and measure changes in word usage. Frequency methods draw insights from the number of occurrences. Syntactic approaches observe changes in parts-of-speech distributions, while Distributional methods employ deep neural models to explore word-behavior similarities in context, utilizing advances in word embeddings.

The paper details the application of these techniques across various datasets, including Google Books Ngram Corpus, Twitter, and Amazon Movie Reviews. This enables the framework to assess and detect linguistic changes across different domains and timescales effectively.

Methodological Insights

1. Frequency-based Methods: These methods compute the change in frequency of word usage over time, capturing shifts attributable to changes in context and meaning. While straightforward, this approach risks false positives due to temporal popularity spikes, indicative but not definitive of semantic shift.
2. Syntactic Methods: By building time series based on part-of-speech tag shifts, these methods can capture changes in syntactic role—offering insight particularly useful when word functions shift significantly.
3. Distributional Methods: The use of deep neural LLMs allows for capturing semantic shifts by analyzing word usage in context. Aligning temporal vector spaces addresses the challenge of stochastic model training variance, providing a unified coordinate system for observing shifts in a semantic space.

Strong Results and Applications

One notable finding is their success in detecting historical linguistic shifts, such as those experienced by the word "gay"—capturing its evolving meaning over the past century using the Google Books Ngram Corpus. The authors demonstrate the framework's scalability and applicability by also identifying shifts related to modern cultural phenomena (e.g., the emergence of new meanings for terms like "Twilight" and "Candy" within shorter timescales via social media datasets).

The quantitative evaluation, utilizing synthetic datasets, corroborates the proposed methods' efficacy in detecting genuine linguistic shifts, espite temporal spikes not attributable to true semantic change. Notably, the Distributional method emerged as particularly robust in balancing detection sensitivity and specificity across evaluated conditions.

Implications for Future Developments

The implications of this research extend into practical applications including semantic search optimization and advanced Internet linguistics insights. By effectively automating the detection of significant linguistic shifts, the research holds potential to enhance natural language understanding systems, facilitate more contextually aware semantic-based applications, and improve sense disambiguation particularly in a digitally-driven society where language evolves rapidly.

The methodologies presented offer a quantifiable approach to linguistic change detection, paving the way for more responsive computational linguistics frameworks. Speculative future work may involve integrating auxiliary datasets like geographic data to explore sociolinguistic factors further influencing language change. As natural languages continue evolving in the increasingly interconnected online environment, frameworks like this provide critical tools for concurrent linguistic analysis and understanding in real-time.