paper-with-me

Papers

How to Do Things without Words: Modeling Semantic Drift of Emoji

2021-10-08 · Eyal Arviv, Oren Tsur

Emoji have become a significant part of our informal textual communication. Previous work addressing the societal and linguistic functions of emoji overlook the evolving meaning of the symbol. This evolution could be addressed through the framework of semantic drifts. In this paper we model and analyze the semantic drift of emoji and discuss the features that may be contributing to the drift, some are unique to emoji and some are more general.

📄 PDF Abstract BibTeX arXiv:2110.04093

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

“Vaderland”, “Volk” and “Natie”: Semantic Change Related to Nationalism in Dutch Literature Between 1700 and 1880 Captured with Dynamic Bernoulli Word Embeddings

2022-05-01 · LChange (ACL) 2022 5 · Marije Timmermans, Eva Vanmassenhove, Dimitar Shterionov

Languages can respond to external events in various ways - the creation of new words or named entities, additional senses might develop for already existing words or the valence of words can change. In this work, we expl…

Word Embeddings

Bayesian Hierarchical Words Representation Learning

2020-04-12 · ACL 2020 6 · Oren Barkan, Idan Rejwan, Avi Caciularu, Noam Koenigstein

This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical prio…

Representation Learning

Unsupervised detection of diachronic word sense evolution

2018-05-29 · Jean-François Delpech

Most words have several senses and connotations which evolve in time due to semantic shift, so that closely related words may gain different or even opposite meanings over the years. This evolution is very relevant to th…

Time SeriesTime Series AnalysisWord Embeddings

Idea-Gated Transformers: Enforcing Semantic Coherence via Differentiable Vocabulary Pruning

2025-12-03 · Darshan Fofadiya arxiv

Autoregressive Language Models (LLMs) trained on Next-Token Prediction (NTP) often suffer from Topic Drift where the generation wanders away from the initial prompt due to a reliance on local associations rather than glo…

A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

2021-04-21 · Li Yang, Abdallah Shami

In recent years, with the increasing popularity of "Smart Technology", the number of Internet of Things (IoT) devices and systems have surged significantly. Various IoT services and functionalities are based on the analy…

Anomaly DetectionDrift Detection