13,436 research outputs found
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data
Few-shot relation classification seeks to classify incoming query instances
after meeting only few support instances. This ability is gained by training
with large amount of in-domain annotated data. In this paper, we tackle an even
harder problem by further limiting the amount of data available at training
time. We propose a few-shot learning framework for relation classification,
which is particularly powerful when the training data is very small. In this
framework, models not only strive to classify query instances, but also seek
underlying knowledge about the support instances to obtain better instance
representations. The framework also includes a method for aggregating
cross-domain knowledge into models by open-source task enrichment.
Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a
few-shot relation classification dataset in health domain with purposely small
training data and challenging relation classes. Experimental results
demonstrate that our framework brings performance gains for most underlying
classification models, outperforms the state-of-the-art results given small
training data, and achieves competitive results with sufficiently large
training data
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction
Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by
extracting entity relations from texts.However, it usually suffers from the
long-tail issue. The training data mainly concentrates on a few types of
relations, leading to the lackof sufficient annotations for the remaining types
of relations. In this paper, we propose a general approach to learn relation
prototypesfrom unlabeled texts, to facilitate the long-tail relation extraction
by transferring knowledge from the relation types with sufficient trainingdata.
We learn relation prototypes as an implicit factor between entities, which
reflects the meanings of relations as well as theirproximities for transfer
learning. Specifically, we construct a co-occurrence graph from texts, and
capture both first-order andsecond-order entity proximities for embedding
learning. Based on this, we further optimize the distance from entity pairs
tocorresponding prototypes, which can be easily adapted to almost arbitrary RE
frameworks. Thus, the learning of infrequent or evenunseen relation types will
benefit from semantically proximate relations through pairs of entities and
large-scale textual information.We have conducted extensive experiments on two
publicly available datasets: New York Times and Google Distant
Supervision.Compared with eight state-of-the-art baselines, our proposed model
achieves significant improvements (4.1% F1 on average). Furtherresults on
long-tail relations demonstrate the effectiveness of the learned relation
prototypes. We further conduct an ablation study toinvestigate the impacts of
varying components, and apply it to four basic relation extraction models to
verify the generalization ability.Finally, we analyze several example cases to
give intuitive impressions as qualitative analysis. Our codes will be released
later
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