ASC analyzer: A Python package for measuring argument structure construction usage in English texts

• URL: http://arxiv.org/abs/2510.10384v1
• Date: Sun, 12 Oct 2025 00:45:18 GMT
• Title: ASC analyzer: A Python package for measuring argument structure construction usage in English texts
• Authors: Hakyung Sung, Kristopher Kyle,
• Abstract summary: This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap.<n>The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength.
• Score: 1.0312968200748116
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength. To demonstrate its utility, we conduct both bivariate and multivariate analyses that examine the relationship between ASC-based indices and L2 writing scores.

Related papers

• Deep networks learn to parse uniform-depth context-free languages from local statistics [12.183764229746926]
  Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning.<n>We introduce a class of context-free grammars (PCFGs) in which both the degree of ambiguity and the correlation structure across scales can be controlled.<n>We propose a unifying framework where correlations at different scales lift local ambiguities, enabling the emergence of hierarchical representations of the data.
  arXiv  Detail & Related papers  (2026-01-31T17:35:06Z)
• Unveiling Decision-Making in LLMs for Text Classification : Extraction of influential and interpretable concepts with Sparse Autoencoders [0.0]
  We present a novel SAE-based architecture tailored for text classification.<n>We benchmark this architecture against established methods such as ConceptShap, Independent Component Analysis, and other SAE-based concept extraction techniques.<n>Our empirical results show that our architecture improves both the causality and interpretability of the extracted features.
  arXiv  Detail & Related papers  (2025-06-30T15:18:50Z)
• Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey [64.08485471150486]
  This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings.<n>We systematically reviewed nearly 250 scholarly sources, capturing the state of the art from various venues of publication.
  arXiv  Detail & Related papers  (2025-03-28T14:08:40Z)
• Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework [61.38174427966444]
  Large Language Models (LLMs) are being used more and more extensively for automated evaluation in various scenarios.<n>Previous studies have attempted to fine-tune open-source LLMs to replicate the evaluation explanations and judgments of powerful proprietary models.<n>We propose a novel evaluation framework, ARJudge, that adaptively formulates evaluation criteria and synthesizes both text-based and code-driven analyses.
  arXiv  Detail & Related papers  (2025-02-26T06:31:45Z)
• Computational Semantics and Evaluation Benchmark for Interrogative Sentences via Combinatory Categorial Grammar [8.666172545138275]
  We present a compositional semantics for various types of polar questions and wh-questions within the framework of Combinatory Categorial Grammar (CCG) We introduce a question-answering dataset QSEM specifically designed to evaluate the semantics of interrogative sentences.
  arXiv  Detail & Related papers  (2023-12-22T14:46:02Z)
• Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) Framework [0.0]
  This paper proposes a general framework to enhance latent space representations for improving interpretability of linear latent spaces. Although the concepts in this paper are language agnostic, the framework is written in Python. Several innovative enhancements are incorporated including latent ranking (LR), latent scaling (LS), latent clustering (LC), and latent condensing (LCON)
  arXiv  Detail & Related papers  (2023-07-11T03:56:04Z)
• Bayesian Networks for Named Entity Prediction in Programming Community Question Answering [0.0]
  We propose a new approach for natural language processing using Bayesian networks to predict and analyze the context. We compare the Bayesian networks with different score metrics, such as the BIC, BDeu, K2 and Chow-Liu trees. In addition, we examine the visualization of directed acyclic graphs to analyze semantic relationships.
  arXiv  Detail & Related papers  (2023-02-26T07:26:36Z)
• A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis [31.05169054736711]
  Cross-lingual structured sentiment analysis task aims to transfer the knowledge from source language to target one. We propose a Knowledge-Enhanced Adversarial Model (textttKEAM) with both implicit distributed and explicit structural knowledge. We conduct experiments on five datasets and compare textttKEAM with both the supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2022-05-31T03:07:51Z)
• Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification [78.83284164605473]
  emphFunnelling (Fun) is a recently proposed method for cross-lingual text classification. We describe emphGeneralized Funnelling (gFun) as a generalization of Fun. We show that gFun substantially improves over Fun and over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-09-17T23:33:04Z)
• A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space [61.18554842370824]
  In cross-lingual language models, representations for many different languages live in the same space. We compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance. We examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics.
  arXiv  Detail & Related papers  (2021-09-13T21:05:37Z)
• X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing [51.81533991497547]
  Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries. We present X2 compared a transferable Cross-lingual and Cross-domain for TCSP. We propose to predict flattened intents and slots representations separately and cast both prediction tasks into sequence labeling problems.
  arXiv  Detail & Related papers  (2021-06-07T16:40:05Z)
• Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval [51.60862829942932]
  We present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks. For sentence-level CLIR, we demonstrate that state-of-the-art performance can be achieved. However, the peak performance is not met using the general-purpose multilingual text encoders off-the-shelf', but rather relying on their variants that have been further specialized for sentence understanding tasks.
  arXiv  Detail & Related papers  (2021-01-21T00:15:38Z)

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.