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Found 35 papers, 6 papers with code
Learning Relatedness between Types with Prototypes for Relation Extraction
We propose to use prototypical examples to represent each relation type and use these examples to augment related types from a different dataset.
A Case Study on Learning a Unified Encoder of Relations
Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema.
Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier.
Entity Linking with a Paraphrase Flavor
The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions.
Combining Neural Networks and Log-linear Models to Improve Relation Extraction
The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text.
Ranked #1 on
Relation Extraction
on ACE 2005
(Cross Sentence metric)
Relation Extraction: Perspective from Convolutional Neural Networks
Ranked #1 on
Relation Extraction
on ACE 2005
(Cross Sentence metric)
Probabilistic Belief Embedding for Knowledge Base Completion
This paper contributes a novel embedding model which measures the probability of each belief $\langle h, r, t, m\rangle$ in a large-scale knowledge repository via simultaneously learning distributed representations for entities ($h$ and $t$), relations ($r$), and the words in relation mentions ($m$).
Jointly Embedding Relations and Mentions for Knowledge Population
This paper contributes a joint embedding model for predicting relations between a pair of entities in the scenario of relation inference.
Large Margin Nearest Neighbor Embedding for Knowledge Representation
Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines.
Challenges in the Knowledge Base Population Slot Filling Task
The Knowledge Based Population (KBP) evaluation track of the Text Analysis Conferences (TAC) has been held for the past 3 years.
