Methods, Data, and Conceptual Change: Reflections from Two Quantitative Diachronic Case Studies
- URL: http://arxiv.org/abs/2605.02052v1
- Date: Sun, 03 May 2026 20:53:47 GMT
- Title: Methods, Data, and Conceptual Change: Reflections from Two Quantitative Diachronic Case Studies
- Abstract summary: We examine English data using quad-based concept modelling of Early Modern English discourse in EEBO- TCP (c. 1470s-1690s; 765M words) and SynFlow analysis of scientific writing in Royal Society Corpus 6.0.4 (1750-1799)<n>We argue that comparative methodological reflection clarifies the limits of purely lexical, frequency-based approaches.
- Score: 0.7196517805648485
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This discussion paper reflects on how quantitative approaches to historical linguistics interact with dataset properties. Drawing on two worked examples, we examine English data using quad-based concept modelling of Early Modern English discourse in EEBO-TCP (c. 1470s-1690s; 765M words) alongside SynFlow analysis of scientific writing in Royal Society Corpus 6.0.4 (1750-1799; drawn from a 78.6M-token open corpus). Through parallel comparison, the paper explores how each approach operationalises concepts, the data assumptions they entail, and the diachronic interpretations they support. We argue that comparative methodological reflection clarifies the limits of purely lexical, frequency-based approaches and highlights how dataset structure shapes the kinds of semantic change that quantitative methods can reliably detect.
Related papers
- Does Syntax Matter? A Graph-Augmented Variational Topic Model for Computational Social Sciences [51.56484100374058]
This paper introduces the Structural Contextual Probabilistic Topic Model ( SCPTM), an architecture that incorporates syntactic dependency relations into topic inference.<n>We evaluate seven topic modeling techniques across four corpora differing in register and discourse structure.<n>Results show that SCPTM's neural architecture yields substantial gains in document-topic alignment over generative baselines.
arXiv Detail & Related papers (2026-09-07T17:39:06Z) - Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms [0.0]
This article presents the first systematic review of unsupervised and semi-supervised computational text-based ideal point estimation (CT-IPE) algorithms.<n>CT-IPE algorithms are widely used in political science, communication, computational social science, and computer science to estimate ideological preferences from parliamentary speeches, party manifestos, and social media.
arXiv Detail & Related papers (2025-11-17T11:01:09Z) - Unpacking Robustness in Inflectional Languages: Adversarial Evaluation and Mechanistic Insights [2.3224139967919974]
We evaluate and explain how adversarial attacks perform in inflectional languages.<n>We use a novel protocol inspired by mechanistic interpretability, based on Edge Attribution Patching (EAP) method.<n>We create a new benchmark based on task-oriented dataset MultiEmo.
arXiv Detail & Related papers (2025-05-08T08:00:03Z) - How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored.
Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges.
We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
arXiv Detail & Related papers (2023-11-14T08:51:00Z) - Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language
Modelling [70.23876429382969]
We propose a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks.
Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena.
For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge.
arXiv Detail & Related papers (2023-07-16T15:18:25Z) - 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) - Text analysis and deep learning: A network approach [0.0]
We propose a novel method that combines transformer models with network analysis to form a self-referential representation of language use within a corpus of interest.
Our approach produces linguistic relations strongly consistent with the underlying model as well as mathematically well-defined operations on them.
It represents, to the best of our knowledge, the first unsupervised method to extract semantic networks directly from deep language models.
arXiv Detail & Related papers (2021-10-08T14:18:36Z) - Weakly-Supervised Aspect-Based Sentiment Analysis via Joint
Aspect-Sentiment Topic Embedding [71.2260967797055]
We propose a weakly-supervised approach for aspect-based sentiment analysis.
We learn sentiment, aspect> joint topic embeddings in the word embedding space.
We then use neural models to generalize the word-level discriminative information.
arXiv Detail & Related papers (2020-10-13T21:33:24Z) - A computational model implementing subjectivity with the 'Room Theory'.
The case of detecting Emotion from Text [68.8204255655161]
This work introduces a new method to consider subjectivity and general context dependency in text analysis.
By using similarity measure between words, we are able to extract the relative relevance of the elements in the benchmark.
This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text.
arXiv Detail & Related papers (2020-05-12T21:26:04Z) - Analysing Lexical Semantic Change with Contextualised Word
Representations [7.071298726856781]
We propose a novel method that exploits the BERT neural language model to obtain representations of word usages.
We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements.
arXiv Detail & Related papers (2020-04-29T12:18:14Z) - How Far are We from Effective Context Modeling? An Exploratory Study on
Semantic Parsing in Context [59.13515950353125]
We present a grammar-based decoding semantic parsing and adapt typical context modeling methods on top of it.
We evaluate 13 context modeling methods on two large cross-domain datasets, and our best model achieves state-of-the-art performances.
arXiv Detail & Related papers (2020-02-03T11:28:10Z)
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.