Papers Relation Classification

Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification

Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer …

Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks

Typically, relation extraction models are trained to extract instances of a relation ontology using only training data from a single language. However, the concepts represented by the relation ontology (e.g. ResidesIn, E…

Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words

Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings l…

Inter-Weighted Alignment Network for Sentence Pair Modeling

Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize …

Exploring Vector Spaces for Semantic Relations

Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for …

Argument Relation Classification Using a Joint Inference Model

In this paper, we address the problem of argument relation classification where argument units are from different texts. We design a joint inference method for the task by modeling argument relation classification and st…

Recognizing Reputation Defence Strategies in Critical Political Exchanges

We propose a new task of automatically detecting reputation defence strategies in the field of computational argumentation. We cast the problem as relation classification, where given a pair of reputation threat and repu…

What matters in a transferable neural network model for relation classification in the biomedical domain?

Lack of sufficient labeled data often limits the applicability of advanced machine learning algorithms to real life problems. However efficient use of Transfer Learning (TL) has been shown to be very useful across domain…

Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention

Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features. We explore this task in a deep-learning framework, …

LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network

In this paper, we describe our participation at the subtask of extraction of relationships between two identified keyphrases. This task can be very helpful in improving search engines for scientific articles. Our approac…

TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers

This paper describes our TTI-COIN system that participated in SemEval-2017 Task 10. We investigated appropriate embeddings to adapt a neural end-to-end entity and relation extraction system LSTM-ER to this task. We parti…

Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text

A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task

For this year{'}s multilingual dependency parsing shared task, we developed a pipeline system, which uses a variety of features for each of its components. Unlike the recent popular deep learning approaches that learn lo…

Extracting Drug-Drug Interactions with Attention CNNs

We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attenti…

Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text

Word representation models have achieved great success in natural language processing tasks, such as relation classification. However, it does not always work on informal text, and the morphemes of some misspelling words…

Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification

We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for …

A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events

We present a sequential model for temporal relation classification between intra-sentence events. The key observation is that the overall syntactic structure and compositional meanings of the multi-word context between e…

Composing Distributed Representations of Relational Patterns

Learning distributed representations for relation instances is a central technique in downstream NLP applications. In order to address semantic modeling of relational patterns, this paper constructs a new dataset that pr…

Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification

Most of neural approaches to relation classification have focused on finding short patterns that represent the semantic relation using Convolutional Neural Networks (CNNs) and those approaches have generally achieved bet…

Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths

Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a signif…