Mary Astell's words in A Serious Proposal to the Ladies (part I), a lexicographic inquiry with NooJ

Hélène Pignot; Odile Piton

Home/Archive/arXiv

arXiv · 1412.1215

Mary Astell's words in A Serious Proposal to the Ladies (part I), a lexicographic inquiry with NooJ

Hélène Pignot·Odile Piton

View original source ↗Read source document ↗Export metadata ↓

Abstract

In the following article we elected to study with NooJ the lexis of a 17 th century text, Mary Astell's seminal essay, A Serious Proposal to the Ladies, part I, published in 1694. We first focused on the semantics to see how Astell builds her vindication of the female sex, which words she uses to sensitise women to their alienated condition and promote their education. Then we studied the morphology of the lexemes (which is different from contemporary English) used by the author, thanks to the NooJ tools we have devised for this purpose. NooJ has great functionalities for lexicographic work. Its commands and graphs prove to be most efficient in the spotting of archaic words or variants in spelling. Introduction In our previous articles, we have studied the singularities of 17 th century English within the framework of a diachronic analysis thanks to syntactical and morphological graphs and thanks to the dictionaries we have compiled from a corpus that may be expanded overtime. Our early work was based on a limited corpus of English travel literature to Greece in the 17 th century. This article deals with a late seventeenth century text written by a woman philosopher and essayist, Mary Astell (1666--1731), considered as one of the first English feminists. Astell wrote her essay at a time in English history when women were "the weaker vessel" and their main business in life was to charm and please men by their looks and submissiveness. In this essay we will see how NooJ can help us analyse Astell's rhetoric (what point of view does she adopt, does she speak in her own name, in the name of all women, what is her representation of men and women and their relationships in the text, what are the goals of education?). Then we will turn our attention to the morphology of words in the text and use NooJ commands and graphs to carry out a lexicographic inquiry into Astell's lexemes.

Explore related subjects

cs.CL

Keep this discovery

BibTeX RIS

Hélène Pignot, Odile Piton. 2014-12-03. Mary Astell's words in A Serious Proposal to the Ladies (part I), a lexicographic inquiry with NooJ. https://arxiv.org/abs/1412.1215

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System

Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale recommendation models introduces two challenges: (1) long feedback loops, where model training can take days, making serial iteration prohibitively slow and requiring parallel exploration across multiple research directions; and (2) system complexity, where large configurations, fragile infrastructure dependencies, and multi-day GPU jobs require robust and recoverable execution. We present Auto-RecSys, an autonomous research system for long-horizon experimentation on industry-scale recommendation models. Auto-RecSys addresses these challenges through three harness designs: (1) distributed asynchronous execution for running multiple experiments in parallel across servers, (2) centralized cross-server memory for persistent and recoverable execution across sessions and failures, and (3) cognitive-procedural separation, where natural-language skill files guide LLM reasoning while deterministic scripts enforce operational correctness. Auto-RecSys further employs a dual-loop self-evolving architecture: an Execution Evolution Loop in which model-specific playbooks accumulate operational knowledge by recording failed attempts and crystallizing successful pipelines, and an Idea Evolution Loop in which experimental outcomes inform subsequent ideation. Evaluated on recommendation models, Auto-RecSys significantly reduces the human time required per experiment cycle and improves execution reliability as its playbooks mature.

cs.CL↗

arXiv2026

Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation

Graph captions should help readers understand graph structure, rather than simply translate adjacency matrices into long textual edge lists. A useful graph caption abstracts connectivity into recognizable motifs, such as hubs, paths, cycles, cliques, and bridges, because these motifs provide compact structural units that are easier to read, compare, and recover. In this paper, we study motif-oriented graph captioning as a bidirectional graph-text translation task, where captions must both preserve enough topology for graph recovery and express the graph through concise motif-level descriptions. We show that direct prompting of GPT-5.1 often produces graph-recoverable captions by enumerating node-to-node connections, but these captions are verbose and can contain inconsistent motif interpretations. To address this gap, we introduce Structurally Speaking, a lightweight structured prompting protocol that guides translation between explicit connectivity and motif-level abstraction. Experiments on a synthetic motif-based dataset show that structured prompting produces shorter and more motif-consistent captions while maintaining comparable graph recovery. These results suggest that explicit topology-to-motif reasoning guidance can make LLM-generated graph captions more interpretable without model fine-tuning.

cs.CL↗

arXiv2026

Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking

Turn-taking is a fundamental mechanism that governs when interlocutors speak and listen. Although Spoken Dialogue Systems (SDS) exploit a range of linguistic, acoustic, and non-verbal cues, they produce ill-timed responses in unscripted interaction. A central challenge is anticipating Transition Relevance Places (TRPs), or opportunities, not obligations, for a listener to take the floor. Human listeners do not wait for turn endings; as an utterance unfolds, they use expectations about its developing meaning to anticipate TRPs and decide whether to take the floor. We examine whether these evolving expectations can be modeled through semantic uncertainty -- an LLM-derived measure of how strongly a turn so far constrains what may plausibly come next. To do so, we sample possible continuations of an ongoing turn and use changes in semantic dispersion to identify TRPs within turns. We evaluate this account on a dataset with TRP labels derived from real-time listener responses, rather than retrospective annotation. Our approach substantially outperforms prompt-based and fine-tuned text-only baselines, providing empirical support for the view that evolving semantic constraints inform perceived turn-taking opportunities in unscripted interaction.

cs.CL↗