SynFlow: A Multidimensional Diachronic Semantic Analysis Toolkit

• URL: http://arxiv.org/abs/2608.19472v2
• Date: Sun, 23 Aug 2026 20:28:58 GMT
• Title: SynFlow: A Multidimensional Diachronic Semantic Analysis Toolkit
• Abstract summary: We present SynFlow, an open-source toolkit for multidimensional diachronic analysis of linguistic usage.<n>It supports different distance measures, together with value-level decomposition, statistical testing, and incremental clustering of lexical fillers.
• Score: 1.1340133299604382
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lexical semantic change (LSC) is commonly modelled through vector-space representations, but these approaches often provide limited insight into which aspects of usage are changing. Diachronic corpus research instead examines interpretable dimensions such as syntactic behaviour, morphology, and constructional patterns, but typically through separate analytical workflows. We present SynFlow, an open-source toolkit for multidimensional diachronic analysis of linguistic usage. SynFlow converts linguistic observations into period-specific distributions and applies a shared workflow across dependency-based co-occurrences, morphological features, constructional configurations, and externally derived representations such as Frame Semantics. It supports different distance measures, together with value-level decomposition, statistical testing, and incremental clustering of lexical fillers. We demonstrate SynFlow through a qualitative case study of the German adjective viral, showing how a single semantic development is reflected across syntactic, lexical, constructional, and morphological dimensions. We further report previously published results on SemEval-2020 Task 1 to situate the performance of these representations relative to existing lexical semantic change detection systems.

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)
• What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metric [50.699179933343686]
  We present a semantic scanpath similarity framework that integrates vision-language models into eye-tracking analysis.<n>Semantic similarity is then computed using embedding-based and lexical NLP metrics.<n>Experiments on free-viewing eye-tracking data demonstrate that semantic similarity captures partially independent variance from geometric alignment.
  arXiv  Detail & Related papers  (2026-04-09T17:36:22Z)
• Text-as-Signal: Quantitative Semantic Scoring with Embeddings, Logprobs, and Noise Reduction [0.0]
  This paper presents a practical pipeline for turning text corpora into quantitative semantic signals.<n>We show how Qwen embeddings, UMAP, semantic indicators derived directly from the model output space, and a three-stage anomaly-detection procedure combine into an operational text-as-signal workflow.
  arXiv  Detail & Related papers  (2026-03-17T19:56:29Z)
• TRACE for Tracking the Emergence of Semantic Representations in Transformers [10.777646083061395]
  We introduce TRACE, a diagnostic framework combining geometric, informational, and linguistic signals to detect phase transitions in Transformer-based LMs.<n>Experiments reveal that phase transitions align with clear intersections between curvature collapse and dimension stabilisation; these geometric shifts coincide with emerging syntactic and semantic accuracy.<n>This work advances our understanding of how linguistic abstractions emerge in LMs, offering insights into model interpretability, training efficiency, and compositional generalisation.
  arXiv  Detail & Related papers  (2025-05-23T15:03:51Z)
• Geometry of Semantics in Next-Token Prediction: How Optimization Implicitly Organizes Linguistic Representations [34.88156871518115]
  Next-token prediction (NTP) optimization leads language models to extract and organize semantic structure from text.<n>We demonstrate that concepts corresponding to larger singular values are learned earlier during training, yielding a natural semantic hierarchy.<n>This insight motivates orthant-based clustering, a method that combines concept signs to identify interpretable semantic categories.
  arXiv  Detail & Related papers  (2025-05-13T08:46:04Z)
• Towards Human Cognition: Visual Context Guides Syntactic Priming in Fusion-Encoded Models [3.63819860423174]
  Structural priming is a cognitive phenomenon where exposure to a particular syntactic structure increases the likelihood of producing the same structure in subsequent utterances.<n>We introduce PRISMATIC, the first multimodal structural priming dataset.<n>We propose the Syntactic Preservation Index (SPI), a novel reference-free evaluation metric designed specifically to assess structural priming effects in sentence level.
  arXiv  Detail & Related papers  (2025-02-24T21:33:27Z)
• SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow [94.90853153808987]
  Semantic segmentation and semantic image synthesis are representative tasks in visual perception and generation. We propose a unified framework (SemFlow) and model them as a pair of reverse problems. Experiments show that our SemFlow achieves competitive results on semantic segmentation and semantic image synthesis tasks.
  arXiv  Detail & Related papers  (2024-05-30T17:34:40Z)
• 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)
• Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge [59.22170796793179]
  Transformers Language Models (TLMs) were tested on a benchmark for the textitdynamic estimation of thematic fit Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge.
  arXiv  Detail & Related papers  (2021-07-22T20:52:26Z)
• A comprehensive comparative evaluation and analysis of Distributional Semantic Models [61.41800660636555]
  We perform a comprehensive evaluation of type distributional vectors, either produced by static DSMs or obtained by averaging the contextualized vectors generated by BERT. The results show that the alleged superiority of predict based models is more apparent than real, and surely not ubiquitous. We borrow from cognitive neuroscience the methodology of Representational Similarity Analysis (RSA) to inspect the semantic spaces generated by distributional models.
  arXiv  Detail & Related papers  (2021-05-20T15:18:06Z)
• Joint Universal Syntactic and Semantic Parsing [39.39769254704693]
  We exploit the rich syntactic and semantic annotations contained in the Universal Decompositional Semantics dataset. We analyze the behaviour of a joint model of syntax and semantics, finding patterns supported by linguistic theory. We then investigate to what degree joint modeling generalizes to a multilingual setting, where we find similar trends across 8 languages.
  arXiv  Detail & Related papers  (2021-04-12T17:56:34Z)

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.