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Found 12 papers, 7 papers with code
MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base
The ability to understand and generate similes is an imperative step to realize human-level AI.
Generative Entity Typing with Curriculum Learning
To overcome these drawbacks, we propose a novel generative entity typing (GET) paradigm: given a text with an entity mention, the multiple types for the role that the entity plays in the text are generated with a pre-trained language model (PLM).
Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension
In order to supply existing KGs with more fine-grained and new concepts, we propose a novel concept extraction framework, namely MRC-CE, to extract large-scale multi-granular concepts from the descriptive texts of entities.
FL-Tuning: Layer Tuning for Feed-Forward Network in Transformer
Specifically, we focus on layer tuning for feed-forward network in the Transformer, namely FL-tuning.
Language Models as Knowledge Embeddings
In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods.
Tackling Math Word Problems with Fine-to-Coarse Abstracting and Reasoning
Math Word Problems (MWP) is an important task that requires the ability of understanding and reasoning over mathematical text.
Rule Mining over Knowledge Graphs via Reinforcement Learning
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.
Semantic-based Data Augmentation for Math Word Problems
In this paper, we propose a set of novel data augmentation approaches to supplement existing datasets with such data that are augmented with different kinds of local variances, and help to improve the generalization ability of current neural models.
Revisiting the Negative Data of Distantly Supervised Relation Extraction
Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem.
Ranked #1 on
Relation Extraction
on NYT11-HRL
Collective Loss Function for Positive and Unlabeled Learning
On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions.
CN-Probase: A Data-driven Approach for Large-scale Chinese Taxonomy Construction
Taxonomies play an important role in machine intelligence.
