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Found 7 papers, 3 papers with code
Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task
As a recent development in few-shot learning, prompt-based techniques have demonstrated promising potential in a variety of natural language processing tasks.
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.
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks.
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.
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.
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.
WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context
More specifically, we introduce a framework for Target Sense Verification of Words in Context which grounds its uniqueness in the formulation as a binary classification task thus being independent of external sense inventories, and the coverage of various domains.
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on WiC-TSV
(Task 3 Accuracy: all metric)
