54 citations · 87 across the 14 of their papers we have counts for
14 papers
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources
Jiasheng Si, Yingjie Zhu, Xingyu Shi +2
Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring b…
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
Gabriele Pergola, Elena Kochkina, Lin Gui +2
Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature. Although an i…
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews
Runcong Zhao, Lin Gui, Gabriele Pergola +1
In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews. Different from existing models for sentime…
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering
Junru Lu, Gabriele Pergola, Lin Gui +2
We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting…
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings
Lixing Zhu, Yulan He, Deyu Zhou
We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global late…
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
Lixing Zhu, Yulan He, Deyu Zhou
Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users' tweet posting behaviour as a tem…
Neural Topic Modeling with Bidirectional Adversarial Training
Rui Wang, Xuemeng Hu, Deyu Zhou +4
Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations fo…
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context
Gabriele Pergola, Yulan He, David Lowe
Making sense of words often requires to simultaneously examine the surrounding context of a term as well as the global themes characterizing the overall corpus. Several topic model…
Variational Conditional GAN for Fine-grained Controllable Image Generation
Mingqi Hu, Deyu Zhou, Yulan He
In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity. Traditional gen…
Open Event Extraction from Online Text using a Generative Adversarial Network
Rui Wang, Deyu Zhou, Yulan He
To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a d…
TDAM: a Topic-Dependent Attention Model for Sentiment Analysis
Gabriele Pergola, Lin Gui, Yulan He
We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and emplo…
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
Jichuan Zeng, Jing Li, Yulan He +3
This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discove…
ATM:Adversarial-neural Topic Model
Rui Wang, Deyu Zhou, Yulan He
Topic models are widely used for thematic structure discovery in text. But traditional topic models often require dedicated inference procedures for specific tasks at hand. Also, t…
A Question Answering Approach to Emotion Cause Extraction
Lin Gui, Jiannan Hu, Yulan He +3
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by r…