Modeling semantic association in self-paced reading with language model embeddings
- URL: http://arxiv.org/abs/2606.07066v1
- Date: Fri, 05 Jun 2026 09:06:22 GMT
- Title: Modeling semantic association in self-paced reading with language model embeddings
- Abstract summary: We use embeddings from LMs to estimate semantic association on a corpus of electroencephalography (EEG) and self-paced reading of natural, Dutch texts.<n>The choice of embedding model can alter the estimated effect of semantic association on both the N400 and self-paced reading times.
- Score: 1.647226581907515
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Semantic association between a word and its context has been identified as an important component of reading comprehension, even when word predictability is accounted for. Recent research has highlighted the potential of language model ( LM) embeddings to quantify semantic association. Yet, embedding-based semantic association have been operationalized in a myriad of ways. In this study, we use embeddings from LMs to estimate semantic association on a corpus of joint electroencephalography (EEG) and self-paced reading of natural, Dutch texts. Semantic association is calculated in ten different implementations that vary the embedding model and context lengths. The effects of semantic association across the different implementations on the N400 and self-paced reading times are examined using Bayesian hierarchical models and Bayes factor. The results show that the choice of embedding model can alter the estimated effect of semantic association on both the N400 and self-paced reading times. Furthermore, the results demonstrate a promising potential of sentence embeddings for capturing semantic association, as only implementations relying on sentence embeddings indicate reliable results of semantic association beyond word predictability on both neural and behavioral measures. Together, these findings highlight the importance of methodological choices in quantifying semantic association.
Related papers
- Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive [77.71680953280436]
The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics.
The semantic properties considered are telicity (also combined with definiteness) and agentivity.
arXiv Detail & Related papers (2023-07-06T10:52:22Z) - 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) - Constructing Word-Context-Coupled Space Aligned with Associative
Knowledge Relations for Interpretable Language Modeling [0.0]
The black-box structure of the deep neural network in pre-trained language models seriously limits the interpretability of the language modeling process.
A Word-Context-Coupled Space (W2CSpace) is proposed by introducing the alignment processing between uninterpretable neural representation and interpretable statistical logic.
Our language model can achieve better performance and highly credible interpretable ability compared to related state-of-the-art methods.
arXiv Detail & Related papers (2023-05-19T09:26:02Z) - Relational Sentence Embedding for Flexible Semantic Matching [86.21393054423355]
We present Sentence Embedding (RSE), a new paradigm to discover further the potential of sentence embeddings.
RSE is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art embedding methods.
arXiv Detail & Related papers (2022-12-17T05:25:17Z) - Lexical semantics enhanced neural word embeddings [4.040491121427623]
hierarchy-fitting is a novel approach to modelling semantic similarity nuances inherently stored in the IS-A hierarchies.
Results demonstrate the efficacy of hierarchy-fitting in specialising neural embeddings with semantic relations in late fusion.
arXiv Detail & Related papers (2022-10-03T08:10:23Z) - Synonym Detection Using Syntactic Dependency And Neural Embeddings [3.0770051635103974]
We study the role of syntactic dependencies in deriving distributional semantics using the Vector Space Model.
We study the effectiveness of injecting human-compiled semantic knowledge into neural embeddings on computing distributional similarity.
Our results show that the syntactically conditioned contexts can interpret lexical semantics better than the unconditioned ones.
arXiv Detail & Related papers (2022-09-30T03:16:41Z) - Testing Pre-trained Language Models' Understanding of Distributivity via
Causal Mediation Analysis [13.07356367140208]
We introduce DistNLI, a new diagnostic dataset for natural language inference.
We find that the extent of models' understanding is associated with model size and vocabulary size.
arXiv Detail & Related papers (2022-09-11T00:33:28Z) - 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) - Model Choices Influence Attributive Word Associations: A Semi-supervised
Analysis of Static Word Embeddings [0.0]
This work aims to assess attributive word associations across five different static word embedding architectures.
Our results reveal that the choice of the context learning flavor during embedding training (CBOW vs skip-gram) impacts the word association distinguishability and word embeddings' sensitivity to deviations in the training corpora.
arXiv Detail & Related papers (2020-12-14T22:27:18Z) - Introducing Syntactic Structures into Target Opinion Word Extraction
with Deep Learning [89.64620296557177]
We propose to incorporate the syntactic structures of the sentences into the deep learning models for targeted opinion word extraction.
We also introduce a novel regularization technique to improve the performance of the deep learning models.
The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.
arXiv Detail & Related papers (2020-10-26T07:13:17Z) - Multidirectional Associative Optimization of Function-Specific Word
Representations [86.87082468226387]
We present a neural framework for learning associations between interrelated groups of words.
Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together.
The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure.
arXiv Detail & Related papers (2020-05-11T17:07:20Z)
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.