An Exploratory Analysis on the Explanatory Potential of Embedding-Based Measures of Semantic Transparency for Malay Word Recognition

• URL: http://arxiv.org/abs/2505.05973v1
• Date: Fri, 09 May 2025 11:57:10 GMT
• Title: An Exploratory Analysis on the Explanatory Potential of Embedding-Based Measures of Semantic Transparency for Malay Word Recognition
• Authors: M. Maziyah Mohamed, R. H. Baayen,
• Abstract summary: We explore embedding-based measures of semantic transparency.<n>We investigate whether these measures are significant predictors of lexical decision latencies.<n>All measures predicted decision latencies after accounting for word frequency, word length, and morphological family size.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Studies of morphological processing have shown that semantic transparency is crucial for word recognition. Its computational operationalization is still under discussion. Our primary objectives are to explore embedding-based measures of semantic transparency, and assess their impact on reading. First, we explored the geometry of complex words in semantic space. To do so, we conducted a t-distributed Stochastic Neighbor Embedding clustering analysis on 4,226 Malay prefixed words. Several clusters were observed for complex words varied by their prefix class. Then, we derived five simple measures, and investigated whether they were significant predictors of lexical decision latencies. Two sets of Linear Discriminant Analyses were run in which the prefix of a word is predicted from either word embeddings or shift vectors (i.e., a vector subtraction of the base word from the derived word). The accuracy with which the model predicts the prefix of a word indicates the degree of transparency of the prefix. Three further measures were obtained by comparing embeddings between each word and all other words containing the same prefix (i.e., centroid), between each word and the shift from their base word, and between each word and the predicted word of the Functional Representations of Affixes in Compositional Semantic Space model. In a series of Generalized Additive Mixed Models, all measures predicted decision latencies after accounting for word frequency, word length, and morphological family size. The model that included the correlation between each word and their centroid as a predictor provided the best fit to the data.

Related papers

• Static Word Embeddings for Sentence Semantic Representation [9.879896956915598]
  We propose new static word embeddings optimised for sentence semantic representation.<n>We first extract word embeddings from a pre-trained Sentence Transformer, and improve them with sentence-level principal component analysis.<n>During inference, we represent sentences by simply averaging word embeddings, which requires little computational cost.
  arXiv  Detail & Related papers  (2025-06-05T04:33:10Z)
• Word-specific tonal realizations in Mandarin [0.9249657468385781]
  This study shows that tonal realization is also partially determined by words' meanings.<n>We first show, on the basis of a corpus of Taiwan Mandarin spontaneous conversations, that word type is a stronger predictor of tonal realization than all the previously established word-form related predictors combined.<n>We then proceed to show, using computational modeling with context-specific word embeddings, that token-specific pitch contours predict word type with 50% accuracy on held-out data.
  arXiv  Detail & Related papers  (2024-05-11T13:00:35Z)
• Identifying and interpreting non-aligned human conceptual representations using language modeling [0.0]
  We show that congenital blindness induces conceptual reorganization in both a-modal and sensory-related verbal domains. We find that blind individuals more strongly associate social and cognitive meanings to verbs related to motion. For some verbs, representations of blind and sighted are highly similar.
  arXiv  Detail & Related papers  (2024-03-10T13:02:27Z)
• Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings [17.803726860514193]
  Detection of semantic variation of words is an important task for various NLP applications. We argue that mean representations alone cannot accurately capture such semantic variations. We propose a method that uses the entire cohort of the contextualised embeddings of the target word.
  arXiv  Detail & Related papers  (2023-05-15T13:58:21Z)
• Neighboring Words Affect Human Interpretation of Saliency Explanations [65.29015910991261]
  Word-level saliency explanations are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such as word length can distort human interpretation of the communicated saliency scores. We investigate how the marking of a word's neighboring words affect the explainee's perception of the word's importance in the context of a saliency explanation.
  arXiv  Detail & Related papers  (2023-05-04T09:50:25Z)
• Contextualized Semantic Distance between Highly Overlapped Texts [85.1541170468617]
  Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. This paper aims to address the issue with a mask-and-predict strategy. We take the words in the longest common sequence as neighboring words and use masked language modeling (MLM) to predict the distributions on their positions. Experiments on Semantic Textual Similarity show NDD to be more sensitive to various semantic differences, especially on highly overlapped paired texts.
  arXiv  Detail & Related papers  (2021-10-04T03:59:15Z)
• Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks [58.87961226278285]
  We propose a self-supervised approach to model lexical semantic change. We show that our method can be used for the detection of semantic change with any alignment method. We illustrate the utility of our techniques using experimental results on three different datasets.
  arXiv  Detail & Related papers  (2021-01-30T18:59:43Z)
• SemGloVe: Semantic Co-occurrences for GloVe from BERT [55.420035541274444]
  GloVe learns word embeddings by leveraging statistical information from word co-occurrence matrices. We propose SemGloVe, which distills semantic co-occurrences from BERT into static GloVe word embeddings.
  arXiv  Detail & Related papers  (2020-12-30T15:38:26Z)
• SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change [58.87961226278285]
  This paper describes SChME, a method used in SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME usesa model ensemble combining signals of distributional models (word embeddings) and wordfrequency models where each model casts a vote indicating the probability that a word sufferedsemantic change according to that feature.
  arXiv  Detail & Related papers  (2020-12-02T23:56:34Z)
• SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces [63.17308641484404]
  We propose to identify clusters among different occurrences of each target word, considering these as representatives of different word meanings. Disagreements in obtained clusters naturally allow to quantify the level of semantic shift per each target word in four target languages. Our approach performs well both measured separately (per language) and overall, where we surpass all provided SemEval baselines.
  arXiv  Detail & Related papers  (2020-10-02T08:38:40Z)
• Comparative Analysis of Word Embeddings for Capturing Word Similarities [0.0]
  Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.
  arXiv  Detail & Related papers  (2020-05-08T01:16:03Z)

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.