SemEval 2010 Тапсырма 8

The automatic interpretation of semantic relations between nominals is an impor- tant subproblem within natural language understanding applications and is an area of increasing interest. In this paper, we present …...

This report describes the UNITN system, a Part-Of-Speech Context Counter, that par- ticipated at Semeval 2010 Task 8: Multi- Way Classification of Semantic Relations Between Pairs of Nominals. Given a …...

This paper describes our system for SemEval-2010 Task 8 on multi-way clas- sification of semantic relations between nominals. First, the type of semantic re- lation is classified. Then a relation …...

Syntactic features play an essential role in identifying relationship in a sentence. Pre- vious neural network models directly work on raw word sequences or constituent parse trees, thus often suffer …...

AbstractNowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model …...

AbstractNowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with …...

AbstractGeneral purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their …...

AbstractRecently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based …...

AbstractWe propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked …...

AbstractDespite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, conditioned on the entity mentions, before …...

AbstractWe present a simple yet effective self-training approach, named as STAD, for low-resource relation extraction. The approach first classifies the auto-annotated instances into two groups: confident instances and uncertain instances, …...