Extreme Multi-Label Classification
29 papers with code • 0 benchmarks • 2 datasets
Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.
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LSHTC
EXTREME CLASSIFICATION
Latest papers
In-Context Learning for Extreme Multi-Label Classification
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to demonstrate every class in a prompt.
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space.
Generalized test utilities for long-tail performance in extreme multi-label classification
mwydmuch/macro-measures-in-xmlc
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NeurIPS 2023
As such, it is characterized by long-tail labels, i. e., most labels have very few positive instances.
Dense Retrieval as Indirect Supervision for Large-space Decision Making
Many discriminative natural language understanding (NLU) tasks have large label spaces.
Dual-Encoders for Extreme Multi-Label Classification
nilesh2797/dexml
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We propose decoupled softmax loss - a simple modification to the InfoNCE loss - that overcomes the limitations of existing contrastive losses.
MDACE: MIMIC Documents Annotated with Code Evidence
In this paper, we introduce MDACE, the first publicly available code evidence dataset, which is built on a subset of the MIMIC-III clinical records.
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
amzn/pecos
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Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances.
Cluster-Guided Label Generation in Extreme Multi-Label Classification
For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent and separate labels.
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
nilesh2797/elias
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A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search.
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
amzn/pecos
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ICLR 2022
We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
