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Found 37 papers, 10 papers with code
Lifelong Explainer for Lifelong Learners
Our ablation study shows that the ER mechanism in our LLE approach enhances the learning capabilities of the student explainer.
Harnessing Privileged Information for Hyperbole Detection
The detection of hyperbole is an important stepping stone to understanding the intentions of a hyperbolic utterance.
Benchmarking of Transformer-Based Pre-Trained Models on Social Media Text Classification Datasets
We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy.
Demonstrating the Reliability of Self-Annotated Emotion Data
Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them.
Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences
Information extraction from the scientific literature is one of the main techniques to transform unstructured knowledge hidden in the text into structured data which can then be used for decision-making in down-stream tasks.
Less is More: Unseen Domain Fake News Detection via Causal Propagation Substructures
The spread of fake news on social media poses significant threats to individuals and society.
Do Text-to-Vis Benchmarks Test Real Use of Visualisations?
Large language models are able to generate code for visualisations in response to simple user requests.
MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction
Domain generalisation - the ability of a machine learning model to perform well on new, unseen domains (text types) - is under-explored.
A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams
By harnessing the strengths of both humans and AI, it significantly improves the efficiency and effectiveness of complex decision-making in dynamic and evolving environments.
Fake News Detection Through Graph-based Neural Networks: A Survey
In recent years, graph-based methods have yielded strong results, as they can closely model the social context and propagation process of online news.
Fake News Detection Through Temporally Evolving User Interactions
We join this model with a neural Hawkes process model to exploit the distinctive self-exciting patterns of true news and fake news on social media.
Learning to Explain: Generating Stable Explanations Fast
The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged.
A Comprehensive Survey on Community Detection with Deep Learning
A community reveals the features and connections of its members that are different from those in other communities in a network.
Assessing Social License to Operate from the Public Discourse on Social Media
Our experimental results show that SIRTA is highly effective in distilling stances from social posts for SLO level assessment, and that the continuous monitoring of SLO levels afforded by SIRTA enables the early detection of critical SLO changes.
Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media
Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data.
Ranked #3 on
Clinical Concept Extraction
on 2010 i2b2/VA
Less is More: Rejecting Unreliable Reviews for Product Question Answering
In the literature, PQA is formulated as a retrieval problem with the goal to search for the most relevant reviews to answer a given product question.
Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity
We further compare the identified 16 security categories across different sources based on their popularity and impact.
Deep Learning for Community Detection: Progress, Challenges and Opportunities
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.
An Effective Transition-based Model for Discontinuous NER
Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans.
DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains
We address the issue of having a limited number of annotations for stance classification in a new domain, by adapting out-of-domain classifiers with domain adaptation.
Reevaluating Argument Component Extraction in Low Resource Settings
Argument component extraction is a challenging and complex high-level semantic extraction task.
Image Captioning using Facial Expression and Attention
An analysis of the generated captions finds that, perhaps unexpectedly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.
Towards Generating Stylized Image Captions via Adversarial Training
While most image captioning aims to generate objective descriptions of images, the last few years have seen work on generating visually grounded image captions which have a specific style (e. g., incorporating positive or negative sentiment).
Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
The introduction of figurative usage detection results in an average improvement of 2. 21% F-score of personal health mention detection, in the case of the feature augmentation-based approach.
A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics
Distributed representations of text can be used as features when training a statistical classifier.
NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks.
Recognising Agreement and Disagreement between Stances with Reason Comparing Networks
We identify agreement and disagreement between utterances that express stances towards a topic of discussion.
Does Multi-Task Learning Always Help?: An Evaluation on Health Informatics
Multi-Task Learning (MTL) has been an attractive approach to deal with limited labeled datasets or leverage related tasks, for a variety of NLP problems.
Using Similarity Measures to Select Pretraining Data for NER
Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks.
Ranked #1 on
Named Entity Recognition (NER)
on WetLab
Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective
Epidemic intelligence deals with the detection of disease outbreaks using formal (such as hospital records) and informal sources (such as user-generated text on the web) of information.
Senti-Attend: Image Captioning using Sentiment and Attention
However, such models typically have difficulty in balancing the semantic aspects of the image and the non-factual dimensions of the caption; in addition, it can be observed that humans may focus on different aspects of an image depending on the chosen sentiment or style of the caption.
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data.
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing
This paper describes a warrant classification system for SemEval 2018 Task 12, that attempts to learn semantic representations of reasons, claims and warrants.
Cross-Target Stance Classification with Self-Attention Networks
In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target.
Automated text summarisation and evidence-based medicine: A survey of two domains
We envision that this survey will serve as a first resource for the development of future operational text summarisation techniques for EBM.
