We present SimLex-999, a gold standard resource for evaluating distributional semantic
models that improves on existing resources in several important ways. First, in contrast to …

Recent developments in language modeling have enabled large text encoders to derive a
wealth of linguistic information from raw text corpora without supervision. Their success …

In previous research in automatic verb classification, syntactic features have proved the most
useful features, although manual classifications rely heavily on semantic features. We show …

In this work, we apply Dirichlet Process Mixture Models (DPMMs) to a learning task in
natural language processing (NLP): lexical-semantic verb clustering. We thoroughly …

Existing approaches to automatic VerbNet-style verb classification are heavily dependent on
feature engineering and therefore limited to languages with mature NLP pipelines. In this …

This study first examines the implicit and explicit premises of four systems for identifying
metaphoric utterances from unannotated input text. All four systems are then evaluated on a …

This paper evaluates four metaphor identification systems on the 200,000 word VU
Amsterdam Metaphor Corpus, comparing results by genre and by sub-class of metaphor …

Investigating the cross-linguistic potential of VerbNet-style classification Page 1 HAL Id: hal-00539036
https://hal.science/hal-00539036 Submitted on 8 Dec 2010 HAL is a multi-disciplinary open …

Distributional measures of lexical similarity and kernel methods for classification are well-
known tools in Natural Language Processing. We bring these two methods together by …

In this work we apply Dirichlet Process Mixture Models to a learning task in natural language
processing (NLP): lexical-semantic verb clustering. We assess the performance on a dataset …