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05/19/2023
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis
We propose using automatically generated natural language definitions of...
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05/06/2023
NorBench – A Benchmark for Norwegian Language Models
We present NorBench: a streamlined suite of NLP tasks and probes for eva...
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03/17/2023
Trained on 100 million words and still in shape: BERT meets British National Corpus
While modern masked language models (LMs) are trained on ever larger cor...
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09/28/2022
RuDSI: graph-based word sense induction dataset for Russian
We present RuDSI, a new benchmark for word sense induction (WSI) in Russ...
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08/31/2022
Contextualized language models for semantic change detection: lessons learned
We present a qualitative analysis of the (potentially erroneous) outputs...
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04/12/2022
Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change
Morphological and syntactic changes in word usage (as captured, e.g., by...
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01/13/2022
NorDiaChange: Diachronic Semantic Change Dataset for Norwegian
We describe NorDiaChange: the first diachronic semantic change dataset f...
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09/21/2021
Grammatical Profiling for Semantic Change Detection
Semantics, morphology and syntax are strongly interdependent. However, t...
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06/15/2021
Three-part diachronic semantic change dataset for Russian
We present a manually annotated lexical semantic change dataset for Russ...
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05/03/2021
Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks
Leader-boards like SuperGLUE are seen as important incentives for active...
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04/13/2021
Large-Scale Contextualised Language Modelling for Norwegian
We present the ongoing NorLM initiative to support the creation and use ...
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03/30/2021
Representing ELMo embeddings as two-dimensional text online
We describe a new addition to the WebVectors toolkit which is used to se...
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10/13/2020
RuSemShift: a dataset of historical lexical semantic change in Russian
We present RuSemShift, a large-scale manually annotated test set for the...
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10/07/2020
ELMo and BERT in semantic change detection for Russian
We study the effectiveness of contextualized embeddings for the task of ...
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04/30/2020
UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection
We apply contextualised word embeddings to lexical semantic change detec...
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03/14/2020
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Disambiguation of word senses in context is easy for humans, but is a ma...
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09/06/2019
To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation
We critically evaluate the widespread assumption that deep learning NLP ...
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07/29/2019
One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts
We extend the well-known word analogy task to a one-to-X formulation, in...
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06/17/2019
Making Fast Graph-based Algorithms with Graph Metric Embeddings
The computation of distance measures between nodes in graphs is ineffici...
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05/16/2019
Tracing cultural diachronic semantic shifts in Russian using word embeddings: test sets and baselines
The paper introduces manually annotated test sets for the task of tracin...
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08/16/2018
Learning Graph Embeddings from WordNet-based Similarity Measures
We present a new approach for learning graph embeddings, that relies on ...
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06/09/2018
Diachronic word embeddings and semantic shifts: a survey
Recent years have witnessed a surge of publications aimed at tracing tem...
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05/06/2018
Russian word sense induction by clustering averaged word embeddings
The paper reports our participation in the shared task on word sense ind...
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01/19/2018
Size vs. Structure in Training Corpora for Word Embedding Models: Araneum Russicum Maximum and Russian National Corpus
In this paper, we present a distributional word embedding model trained ...
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07/26/2017
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants
This paper deals with using word embedding models to trace the temporal ...
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04/19/2017
Redefining Context Windows for Word Embedding Models: An Experimental Study
Distributional semantic models learn vector representations of words thr...
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08/12/2016
Redefining part-of-speech classes with distributional semantic models
This paper studies how word embeddings trained on the British National C...
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04/18/2016
Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints
We present our experience in applying distributional semantics (neural w...
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04/30/2015