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Found 7 papers, 2 papers with code
From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification
“Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification.
Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples
However, the existing method depends on the relevance between tasks and is prone to inter-type confusion. In this paper, we propose a novel two-stage framework Learn-and-Review (L&R) for continual NER under the type-incremental setting to alleviate the above issues. Specifically, for the learning stage, we distill the old knowledge from teacher to a student on the current dataset.
Continual Named Entity Recognition
named-entity-recognition
+2
Learning In-context Learning for Named Entity Recognition
M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .
Universal Information Extraction as Unified Semantic Matching
Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing.
Unified Structure Generation for Universal Information Extraction
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on ASTE
(using extra training data)
Joint extraction of entities and overlapping relations using position-attentive sequence labeling
Joint entity and relation extraction is to detect entity and relation using a single model.
Ranked #2 on
Relation Extraction
on NYT-single
ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification
In this paper, we propose ARNOR, a novel Attention Regularization based NOise Reduction framework for distant supervision relation classification.
