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Dominik Schlechtweg - One of the best experts on this subject based on the ideXlab platform.
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Challenges for Computational Lexical Semantic Change
arXiv: Computation and Language, 2021Co-Authors: Simon Hengchen, Dominik Schlechtweg, Nina Tahmasebi, Haim DubossarskyAbstract:The computational study of lexical Semantic Change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences and linguistics. Most of the research so far has focused on methods for modelling and detecting Semantic Change using large diachronic textual data, with the majority of the approaches employing neural embeddings. While methods that offer easy modelling of diachronic text are one of the main reasons for the spiking interest in LSC, neural models leave many aspects of the problem unsolved. The field has several open and complex challenges. In this chapter, we aim to describe the most important of these challenges and outline future directions.
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semeval 2020 task 1 unsupervised lexical Semantic Change detection
International Conference on Computational Linguistics, 2020Co-Authors: Dominik Schlechtweg, Simon Hengchen, Haim Dubossarsky, Barbara Mcgillivray, Nina TahmasebiAbstract:Lexical Semantic Change detection, i.e., the task of identifying words that Change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.
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ims at semeval 2020 task 1 how low can you go dimensionality in lexical Semantic Change detection
International Conference on Computational Linguistics, 2020Co-Authors: Jens Kaiser, Dominik Schlechtweg, Sean Papay, Sabine Schulte Im WaldeAbstract:We present the results of our system for SemEval-2020 Task 1 that exploits a commonly used lexical Semantic Change detection model based on Skip-Gram with Negative Sampling. Our system focuses on Vector Initialization (VI) alignment, compares VI to the currently top-ranking models for Subtask 2 and demonstrates that these can be outperformed if we optimize VI dimensionality. We demonstrate that differences in performance can largely be attributed to model-specific sources of noise, and we reveal a strong relationship between dimensionality and frequency-induced noise in VI alignment. Our results suggest that lexical Semantic Change models integrating vector space alignment should pay more attention to the role of the dimensionality parameter.
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Shared Task: Lexical Semantic Change Detection in German
arXiv: Computation and Language, 2020Co-Authors: Adnan Ahmad, Kiflom Desta, Fabian Lang, Dominik SchlechtwegAbstract:Recent NLP architectures have illustrated in various ways how Semantic Change can be captured across time and domains. However, in terms of evaluation there is a lack of benchmarks to compare the performance of these systems against each other. We present the results of the first shared task on unsupervised lexical Semantic Change detection (LSCD) in German based on the evaluation framework proposed by Schlechtweg et al. (2019).
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Simulating Lexical Semantic Change from Sense-Annotated Data.
arXiv: Computation and Language, 2020Co-Authors: Dominik Schlechtweg, Sabine Schulte Im WaldeAbstract:We present a novel procedure to simulate lexical Semantic Change from synchronic sense-annotated data, and demonstrate its usefulness for assessing lexical Semantic Change detection models. The induced dataset represents a stronger correspondence to empirically observed lexical Semantic Change than previous synthetic datasets, because it exploits the intimate relationship between synchronic polysemy and diachronic Change. We publish the data and provide the first large-scale evaluation gold standard for LSC detection models.
Barbara Mcgillivray - One of the best experts on this subject based on the ideXlab platform.
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Lexical Semantic Change for Ancient Greek and Latin.
arXiv: Computation and Language, 2021Co-Authors: Valerio Perrone, Simon Hengchen, Marco Palma, Alessandro Vatri, Jim Q. Smith, Barbara McgillivrayAbstract:Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associating a word's correct meaning in its historical context is a central challenge in diachronic research. Historical corpora of classical languages, such as Ancient Greek and Latin, typically come with rich metadata, and existing models are limited by their inability to exploit contextual information beyond the document timestamp. While embedding-based methods feature among the current state of the art systems, they are lacking in the interpretative power. In contrast, Bayesian models provide explicit and interpretable representations of Semantic Change phenomena. In this chapter we build on GASC, a recent computational approach to Semantic Change based on a dynamic Bayesian mixture model. In this model, the evolution of word senses over time is based not only on distributional information of lexical nature, but also on text genres. We provide a systematic comparison of dynamic Bayesian mixture models for Semantic Change with state-of-the-art embedding-based models. On top of providing a full description of meaning Change over time, we show that Bayesian mixture models are highly competitive approaches to detect binary Semantic Change in both Ancient Greek and Latin.
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semeval 2020 task 1 unsupervised lexical Semantic Change detection
International Conference on Computational Linguistics, 2020Co-Authors: Dominik Schlechtweg, Simon Hengchen, Haim Dubossarsky, Barbara Mcgillivray, Nina TahmasebiAbstract:Lexical Semantic Change detection, i.e., the task of identifying words that Change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.
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room to glo a systematic comparison of Semantic Change detection approaches with word embeddings
Empirical Methods in Natural Language Processing, 2019Co-Authors: Philippa Shoemark, Farhana Ferdousi Liza, Dong Nguyen, Scott A Hale, Barbara McgillivrayAbstract:Word embeddings are increasingly used for the automatic detection of Semantic Change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking. We propose a new evaluation framework for Semantic Change detection and find that (i) using the whole time series is preferable over only comparing between the first and last time points; (ii) independently trained and aligned embeddings perform better than continuously trained embeddings for long time periods; and (iii) that the reference point for comparison matters. We also present an analysis of the Changes detected on a large Twitter dataset spanning 5.5 years.
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GASC: Genre-Aware Semantic Change for Ancient Greek
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, 2019Co-Authors: Valerio Perrone, Simon Hengchen, Marco Palma, Alessandro Vatri, Jim Q. Smith, Barbara McgillivrayAbstract:Word meaning Changes over time, depending on linguistic and extra-linguistic factors. Associating a word’s correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and Semantic search in historical texts. Bayesian models for Semantic Change have emerged as a powerful tool to address this challenge, providing explicit and interpretable representations of Semantic Change phenomena. However, while corpora typically come with rich metadata, existing models are limited by their inability to exploit contextual information (such as text genre) beyond the document time-stamp. This is particularly critical in the case of ancient languages, where lack of data and long diachronic span make it harder to draw a clear distinction between polysemy (the fact that a word has several senses) and Semantic Change (the process of acquiring, losing, or changing senses), and current systems perform poorly on these languages. We develop GASC, a dynamic Semantic Change model that leverages categorical metadata about the texts’ genre to boost inference and uncover the evolution of meanings in Ancient Greek corpora. In a new evaluation framework, our model achieves improved predictive performance compared to the state of the art.
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mining the uk web archive for Semantic Change detection
Recent Advances in Natural Language Processing, 2019Co-Authors: Adam Tsakalidis, Pierpaolo Basile, Marya Bazzi, Mihai Cucuringu, Barbara McgillivrayAbstract:Semantic Change detection (i.e., identifying words whose meaning has Changed over time) started emerging as a growing area of research over the past decade, with important downstream applications in natural language processing, historical linguistics and computational social science. However, several obstacles make progress in the domain slow and difficult. These pertain primarily to the lack of well-established gold standard datasets, resources to study the problem at a fine-grained temporal resolution, and quantitative evaluation approaches. In this work, we aim to mitigate these issues by (a) releasing a new labelled dataset of more than 47K word vectors trained on the UK Web Archive over a short time-frame (2000-2013); (b) proposing a variant of Procrustes alignment to detect words that have undergone Semantic shift; and (c) introducing a rank-based approach for evaluation purposes. Through extensive numerical experiments and validation, we illustrate the effectiveness of our approach against competitive baselines. Finally, we also make our resources publicly available to further enable research in the domain.
Haim Dubossarsky - One of the best experts on this subject based on the ideXlab platform.
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Challenges for Computational Lexical Semantic Change
arXiv: Computation and Language, 2021Co-Authors: Simon Hengchen, Dominik Schlechtweg, Nina Tahmasebi, Haim DubossarskyAbstract:The computational study of lexical Semantic Change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences and linguistics. Most of the research so far has focused on methods for modelling and detecting Semantic Change using large diachronic textual data, with the majority of the approaches employing neural embeddings. While methods that offer easy modelling of diachronic text are one of the main reasons for the spiking interest in LSC, neural models leave many aspects of the problem unsolved. The field has several open and complex challenges. In this chapter, we aim to describe the most important of these challenges and outline future directions.
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semeval 2020 task 1 unsupervised lexical Semantic Change detection
International Conference on Computational Linguistics, 2020Co-Authors: Dominik Schlechtweg, Simon Hengchen, Haim Dubossarsky, Barbara Mcgillivray, Nina TahmasebiAbstract:Lexical Semantic Change detection, i.e., the task of identifying words that Change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.
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time out temporal referencing for robust modeling of lexical Semantic Change
arXiv: Computation and Language, 2019Co-Authors: Haim Dubossarsky, Nina Tahmasebi, Simon Hengchen, Dominik SchlechtwegAbstract:State-of-the-art models of lexical Semantic Change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical Semantic Change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical Semantic Change and systematically control for possible biases.
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ACL (1) - Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019Co-Authors: Haim Dubossarsky, Nina Tahmasebi, Simon Hengchen, Dominik SchlechtwegAbstract:State-of-the-art models of lexical Semantic Change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical Semantic Change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical Semantic Change and systematically control for possible biases.
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outta control laws of Semantic Change and inherent biases in word representation models
Empirical Methods in Natural Language Processing, 2017Co-Authors: Haim Dubossarsky, Daphna Weinshall, Eitan GrossmanAbstract:This article evaluates three proposed laws of Semantic Change. Our claim is that in order to validate a putative law of Semantic Change, the effect should be observed in the genuine condition but absent or reduced in a suitably matched control condition, in which no Change can possibly have taken place. Our analysis shows that the effects reported in recent literature must be substantially revised: (i) the proposed negative correlation between meaning Change and word frequency is shown to be largely an artefact of the models of word representation used; (ii) the proposed negative correlation between meaning Change and prototypicality is shown to be much weaker than what has been claimed in prior art; and (iii) the proposed positive correlation between meaning Change and polysemy is largely an artefact of word frequency. These empirical observations are corroborated by analytical proofs that show that count representations introduce an inherent dependence on word frequency, and thus word frequency cannot be evaluated as an independent factor with these representations.
Jakub Sido - One of the best experts on this subject based on the ideXlab platform.
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uwb at semeval 2020 task 1 lexical Semantic Change detection
International Conference on Computational Linguistics, 2020Co-Authors: Ondrej Prazak, Pavel Priban, Stephen Taylor, Jakub SidoAbstract:In this paper, we describe our method for detection of lexical Semantic Change, i.e., word sense Changes over time. We examine Semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary Change detection, and 4th in Sub-task 2: ranked Change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a Semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.
Andrey Kutuzov - One of the best experts on this subject based on the ideXlab platform.
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rusemshift a dataset of historical lexical Semantic Change in russian
International Conference on Computational Linguistics, 2020Co-Authors: Julia Rodina, Andrey KutuzovAbstract:We present RuSemShift, a large-scale manually annotated test set for the task of Semantic Change modeling in Russian for two long-term time period pairs: from the pre-Soviet through the Soviet times and from the Soviet through the post-Soviet times. Target words were annotated by multiple crowd-source workers. The annotation process was organized following the DURel framework and was based on sentence contexts extracted from the Russian National Corpus. Additionally, we report the performance of several distributional approaches on RuSemShift, achieving promising results, which at the same time leave room for other researchers to improve.
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ELMo and BERT in Semantic Change detection for Russian.
arXiv: Computation and Language, 2020Co-Authors: Julia Rodina, Yuliya Trofimova, Andrey Kutuzov, Ekaterina ArtemovaAbstract:We study the effectiveness of contextualized embeddings for the task of diachronic Semantic Change detection for Russian language data. Evaluation test sets consist of Russian nouns and adjectives annotated based on their occurrences in texts created in pre-Soviet, Soviet and post-Soviet time periods. ELMo and BERT architectures are compared on the task of ranking Russian words according to the degree of their Semantic Change over time. We use several methods for aggregation of contextualized embeddings from these architectures and evaluate their performance. Finally, we compare unsupervised and supervised techniques in this task.
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uio uva at semeval 2020 task 1 contextualised embeddings for lexical Semantic Change detection
arXiv: Computation and Language, 2020Co-Authors: Andrey Kutuzov, Mario GiulianelliAbstract:We apply contextualised word embeddings to lexical Semantic Change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their Semantic drift over time. We analyse the performance of two contextualising architectures (BERT and ELMo) and three Change detection algorithms. We find that the most effective algorithms rely on the cosine similarity between averaged token embeddings and the pairwise distances between token embeddings. They outperform strong baselines by a large margin (in the post-evaluation phase, we have the best Subtask 2 submission for SemEval-2020 Task 1), but interestingly, the choice of a particular algorithm depends on the distribution of gold scores in the test set.