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Found 6 papers, 4 papers with code
A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection
Detecting temporal semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions.
$\textit{Swap and Predict}$ -- Predicting the Semantic Changes in Words across Corpora by Context Swapping
Intuitively, if the meaning of $w$ does not change between $\mathcal{C}_1$ and $\mathcal{C}_2$, we would expect the distributions of contextualised word embeddings of $w$ to remain the same before and after this random swapping process.
Can Word Sense Distribution Detect Semantic Changes of Words?
Given this relationship between WSD and SCD, we explore the possibility of predicting whether a target word has its meaning changed between two corpora collected at different time steps, by comparing the distributions of senses of that word in each corpora.
Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings
However, some of the previously associated meanings of a target word can become obsolete over time (e. g. meaning of gay as happy), while novel usages of existing words are observed (e. g. meaning of cell as a mobile phone).
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction
Therefore, in this study, we created a quality estimation dataset with manual evaluation to build an automatic evaluation model for Japanese GEC.
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings
In this study, we propose a model that extends the continuous space topic model (CSTM), which flexibly controls word probability in a document, using pre-trained word embeddings.
