Autoencoding Word Representations through Time for Semantic Change Detection

• URL: http://arxiv.org/abs/2004.13703v1
• Date: Tue, 28 Apr 2020 17:58:14 GMT
• Title: Autoencoding Word Representations through Time for Semantic Change Detection
• Authors: Adam Tsakalidis and Maria Liakata
• Abstract summary: We propose three variants of sequential models for detecting semantically shifted words. We take a step towards comparing different approaches in a quantitative manner, demonstrating that the temporal modelling of word representations yields a clear-cut advantage in performance.
• Score: 21.17543605603968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic change detection concerns the task of identifying words whose meaning has changed over time. The current state-of-the-art detects the level of semantic change in a word by comparing its vector representation in two distinct time periods, without considering its evolution through time. In this work, we propose three variants of sequential models for detecting semantically shifted words, effectively accounting for the changes in the word representations over time, in a temporally sensitive manner. Through extensive experimentation under various settings with both synthetic and real data we showcase the importance of sequential modelling of word vectors through time for detecting the words whose semantics have changed the most. Finally, we take a step towards comparing different approaches in a quantitative manner, demonstrating that the temporal modelling of word representations yields a clear-cut advantage in performance.

Related papers

• Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices [21.852268893122115]
  We propose a simple yet intuitive framework for how semantic shifts occur over multiple time periods. We compute a diachronic word similarity matrix using fast and lightweight word embeddings. We can categorize words that exhibit similar behavior of semantic shift in an unsupervised manner.
  arXiv  Detail & Related papers  (2025-01-16T13:42:09Z)
• Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective [50.261681681643076]
  We propose a novel metric called SemVarEffect and a benchmark named SemVarBench to evaluate the causality between semantic variations in inputs and outputs in text-to-image synthesis. Our work establishes an effective evaluation framework that advances the T2I synthesis community's exploration of human instruction understanding.
  arXiv  Detail & Related papers  (2024-10-14T08:45:35Z)
• Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning [71.14084801851381]
  Change captioning aims to succinctly describe the semantic change between a pair of similar images. Most existing methods directly capture the difference between them, which risk obtaining error-prone difference features. We propose a distractors-immune representation learning network that correlates the corresponding channels of two image representations.
  arXiv  Detail & Related papers  (2024-07-16T13:00:33Z)
• Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types [1.3436368800886478]
  We present a model that leverages information both from synchronic lexical relations and definitions of word meanings. Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank's (1997) dataset of semantic change types.
  arXiv  Detail & Related papers  (2024-06-05T16:52:21Z)
• 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)
• Grammatical Profiling for Semantic Change Detection [6.3596637237946725]
  We use grammatical profiling as an alternative method for semantic change detection. We demonstrate that it can be used for semantic change detection and even outperforms some distributional semantic methods.
  arXiv  Detail & Related papers  (2021-09-21T18:38:18Z)
• Understanding Synonymous Referring Expressions via Contrastive Features [105.36814858748285]
  We develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels. We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-20T17:56:24Z)
• 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)
• Lexical semantic change for Ancient Greek and Latin [61.69697586178796]
  Associating a word's correct meaning in its historical context is a central challenge in diachronic research. We build on a recent computational approach to semantic change based on a dynamic Bayesian mixture model. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models.
  arXiv  Detail & Related papers  (2021-01-22T12:04:08Z)
• 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)
• Analysing Lexical Semantic Change with Contextualised Word Representations [7.071298726856781]
  We propose a novel method that exploits the BERT neural language model to obtain representations of word usages. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements.
  arXiv  Detail & Related papers  (2020-04-29T12:18:14Z)

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.