Search Results for author:
Found 27 papers, 4 papers with code
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge
Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pre-trained language models (PLMs) into neural topic modeling.
A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge
Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes.
Implicit Sentiment Analysis with Event-centered Text Representation
Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years.
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs
The other is label heterogeneous graph, which is constructed based on both the labels’ hierarchy and their statistical dependencies.
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering
However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance.
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation
Therefore, in this paper, multi-hop relation detection is considered as a multi-label learning problem.
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection
Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states.
Ranked #2 on
Emotion Recognition in Conversation
on EmoryNLP
Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification
To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim.
Variational Gaussian Topic Model with Invertible Neural Projections
Based on the variational auto-encoder, the proposed VaGTM models each topic with a multivariate Gaussian in decoder to incorporate word relatedness.
Emotion Classification by Jointly Learning to Lexiconize and Classify
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018).
Neural Topic Modeling with Cycle-Consistent Adversarial Training
ToMCAT employs a generator network to interpret topics and an encoder network to infer document topics.
Neural Topic Modeling by Incorporating Document Relationship Graph
Graph Neural Networks (GNNs) that capture the relationships between graph nodes via message passing have been a hot research direction in the natural language processing community.
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings
We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings.
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context.
Neural Topic Modeling with Bidirectional Adversarial Training
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 for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA).
Ranked #1 on
Text Clustering
on 20 Newsgroups
Interpretable Relevant Emotion Ranking with Event-Driven Attention
Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results.
Variational Conditional GAN for Fine-grained Controllable Image Generation
In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity.
Open Event Extraction from Online Text using a Generative Adversarial Network
Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15\% is observed in F-measure.
ATM:Adversarial-neural Topic Model
The proposed ATM models topics with Dirichlet prior and employs a generator network to capture the semantic patterns among latent topics.
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking
As such, it is crucial to predict and rank multiple relevant emotions by their intensities.
Relevant Emotion Ranking from Text Constrained with Emotion Relationships
As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem.
Neural Storyline Extraction Model for Storyline Generation from News Articles
To tackle this problem, approaches based on probabilistic graphic models jointly model the generations of events and storylines without the use of annotated data.
Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings
To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event.
