Graph Convolutional Network

AbstractWe propose a neural multi-document summarization system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural …...

AbstractEntity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts …...

AbstractMultilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a source language with their …...

AbstractWe propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works …...

AbstractWe introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge …...

AbstractWe develop a new paradigm for the task of joint entity relation extraction. It first identifies entity spans, then performs a joint inference on entity types and relation types. To …...

AbstractIdentifying the relationship between two articles, e.g., whether two articles published from different sources describe the same breaking news, is critical to many document understanding tasks. Existing approaches for modeling …...

AbstractAutomatically verifying rumorous information has become an important and challenging task in natural language processing and social media analytics. Previous studies reveal that people’s stances towards rumorous messages can provide …...

AbstractIn this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user’s occupational class. We show that the content information …...

AbstractSyntactic information is essential for both sentiment analysis(SA) and aspect-based sentiment analysis(ABSA). Previous work has already achieved great progress utilizing Graph Convolutional Network(GCN) over dependency tree of a sentence. However, …...

AbstractIn this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and …...

AbstractPre-trained language models such as BERT have achieved the state-of-the-art performance on natural language inference (NLI). However, it has been shown that such models can be tricked by variations of …...

AbstractAspect-level sentiment analysis(ASC) predicts each specific aspect term’s sentiment polarity in a given text or review. Recent studies used attention-based methods that can effectively improve the performance of aspect-level sentiment …...

AbstractIn the recent past, dialogue systems have gained immense popularity and have become ubiquitous. During conversations, humans not only rely on languages but seek contextual information through visual contents as …...

AbstractFact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about …...

AbstractSlot filling and intent detection are two major tasks for spoken language understanding. In most existing work, these two tasks are built as joint models with multi-task learning with no …...

AbstractIn this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and CDP are …...

In this paper, we propose a novel edge-editing approach to extract relation information from a document. We treat the relations in a doc- ument as a relation graph among entities …...

AbstractTarget-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text. Current …...

AbstractA dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, …...

Interactive argument pair identification is es- sential in the context of dialogical argumenta- tion mining. Existing research treats it as a problem of sentence matching and largely re- lies on …...

AbstractGraph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN …...

AbstractDistantly supervised relation extraction is challenging due to the noise within data. Recent methods focus on exploiting bag representations based on deep neural networks with complex de-noising scheme to achieve …...

AbstractEvent schema depicts the typical structure of complex events, serving as a scaffolding to effectively analyze, predict, and possibly intervene in the ongoing events. To induce event schemas from historical …...