| Challenge: | Existing methods for creating verbal classifications are limited or non-existent in most languages . a range of automatic verb classification approaches have been proposed, but high-quality resources are needed . |
| Approach: | They propose to use top-up semantic clustering to extract syntactic and semantic information from verbs in English, Polish and Croatian. |
| Outcome: | The proposed classifications in English, Polish and Croatian are compared with other languages. |
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| Challenge: | Existing methods to learn general language representations from large volumes of unlabeled text have been used to improve multilingual NLP. |
| Approach: | They propose to use a spatial arrangement method to generate large-scale evaluation datasets that balance cross-lingual alignment with language specificity. |
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Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation (2020.coling-main)
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| Challenge: | Verb alternations allow verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related. |
| Approach: | They use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation . they use count-based and vector-based features as well as perplexity-based language model features to reflect each alternation’s felicity by simulating it. |
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Annotation and Automatic Classification of Aspectual Categories (P19-1)
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| Challenge: | Annotated resource for aspectual classification of German verb tokens in context. |
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Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction (2020.lrec-1)
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Olga Majewska, Diana McCarthy, Jasper van den Bosch, Nikolaus Kriegeskorte, Ivan Vulić, Anna Korhonen
| Challenge: | Existing methods for creating large-scale semantic similarity resources are slow and expensive . a large verb similarity dataset is available for a number of verbs, but not for English. |
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Representing Verbs with Visual Argument Vectors (2020.lrec-1)
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| Challenge: | Existing models for verb semantic similarities are based on linguistic data, but they do not register intuitive attributes. |
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Verb Knowledge Injection for Multilingual Event Processing (2021.acl-long)
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| Challenge: | Recent studies have shown that pretrainers implicitly extract a non-negligible amount of linguistic knowledge from text corpora in an unsupervised fashion. |
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A Closer Look at Clustering Bilingual Comparable Corpora (2024.lrec-main)
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| Challenge: | Existing methods for clustering comparable corpora are not suitable for bilingual corpors. |
| Approach: | They propose new clustering models fully adapted to comparable corpora based on a deep variant of Kmeans . they illustrate their behavior on bilingual collections created from Wikipedia . |
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The Interplay between Metaphors and NLP (2026.acl-tutorials)
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| Challenge: | This tutorial will provide an overview of the metaphor processing field. |
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Cross-lingual Text Classification Transfer: The Case of Ukrainian (2025.coling-main)
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| Challenge: | despite the large amount of labeled datasets, there is an imbalance in data availability across languages. |
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Graph-based Clustering for Detecting Semantic Change Across Time and Languages (2024.eacl-long)
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| Challenge: | Existing approaches to detect semantic change using contextualized embeddings are underperforming . a graph-based clustering approach captures nuanced changes in word senses across time and languages . |
| Approach: | They propose a graph-based clustering approach to capture nuanced changes in word senses across time and languages. |
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