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Found 11 papers, 8 papers with code
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)
Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications.
ID10M: Idiom Identification in 10 Languages
Idioms are phrases which present a figurative meaning that cannot be (completely) derived by looking at the meaning of their individual components. Identifying and understanding idioms in context is a crucial goal and a key challenge in a wide range of Natural Language Understanding tasks.
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER
Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP.
Named Entity Recognition for Entity Linking: What Works and What’s Next
Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models.
Ranked #4 on
Entity Disambiguation
on ACE2004
NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection
Idioms are lexically-complex phrases whose meaning cannot be derived by compositionally interpreting their components.
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming
When building Large Language Models (LLMs), it is paramount to bear safety in mind and protect them with guardrails.
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset
Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge.
What's the Meaning of Superhuman Performance in Today's NLU?
In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension.
EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation
We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection.
Focusing on Context is NICE: Improving Overshadowed Entity Disambiguation
Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base.
