Papers Relation Classification

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose …

On the Need of Cross Validation for Discourse Relation Classification

The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic. Existing machine learning models for the task train on sections 2-21…

Neural Temporal Relation Extraction

We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-e…

Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations

This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem.…

A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge

For argumentation mining, there are several sub-tasks such as argumentation component type classification, relation classification. Existing research tends to solve such sub-tasks separately, but ignore the close relatio…

CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks

In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared tas…

CATENA: CAusal and TEmporal relation extraction from NAtural language texts

We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the perf…

Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention

Semantic relation classification remains a challenge in natural language processing. In this paper, we introduce a hierarchical recurrent neural network that is capable of extracting information from raw sentences for re…

A Unified Architecture for Semantic Role Labeling and Relation Classification

This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SR…

Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks

Distant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from…

Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network

Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especial…

Attention-Based Convolutional Neural Network for Semantic Relation Extraction

Nowadays, 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 makes full use…

Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction

This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model t…

On the contribution of word embeddings to temporal relation classification

Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose en…

Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion

Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities an…

A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification

A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification

CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations

We present a submission to the CogALex 2016 shared task on the corpus-based identification of semantic relations, using LexNET (Shwartz and Dagan, 2016), an integrated path-based and distributional method for semantic re…

Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison

Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover's distance (EMD) is the most effective histogram distance metric for the application of multi…

A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features

Relation classification is associated with many potential applications in the artificial intelligence area. Recent approaches usually leverage neural networks based on structure features such as syntactic or dependency f…