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Found 13 papers, 10 papers with code
On the Cross-lingual Transferability of Contextualized Sense Embeddings
In this paper we analyze the extent to which contextualized sense embeddings, i. e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages. To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation.
Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter
This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models.
Probing Commonsense Knowledge in Pre-trained Language Models with Sense-level Precision and Expanded Vocabulary
However, this approach is restricted by the LM's vocabulary available for masked predictions, and its precision is subject to the context provided by the assertion.
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift.
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media.
TimeLMs: Diachronic Language Models from Twitter
Despite its importance, the time variable has been largely neglected in the NLP and language model literature.
LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond
Prior work has shown that these contextual representations can be used to accurately represent large sense inventories as sense embeddings, to the extent that a distance-based solution to Word Sense Disambiguation (WSD) tasks outperforms models trained specifically for the task.
Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning
The Natural Language Processing task of determining "Who did what to whom" is called Semantic Role Labeling.
Analysis and Evaluation of Language Models for Word Sense Disambiguation
We also perform an in-depth comparison of the two main language model based WSD strategies, i. e., fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data.
Don't Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation
State-of-the-art methods for Word Sense Disambiguation (WSD) combine two different features: the power of pre-trained language models and a propagation method to extend the coverage of such models.
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation
Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings.
LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)
This paper describes the LIAAD system that was ranked second place in the Word-in-Context challenge (WiC) featured in SemDeep-5.
Affordance Extraction and Inference based on Semantic Role Labeling
Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing.
