We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy.
Our ablation study shows that the ER mechanism in our LLE approach enhances the learning capabilities of the student explainer.
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.
In recent years, graph-based methods have yielded strong results, as they can closely model the social context and propagation process of online news.
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.
A community reveals the features and connections of its members that are different from those in other communities in a network.
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.
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
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.
We further compare the identified 16 security categories across different sources based on their popularity and impact.
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.
Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans.
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.
Argument component extraction is a challenging and complex high-level semantic extraction task.
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).
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.
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.
Distributed representations of text can be used as features when training a statistical classifier.
Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks.
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
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.
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.
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.
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.
This paper describes a warrant classification system for SemEval 2018 Task 12, that attempts to learn semantic representations of reasons, claims and warrants.
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.
We envision that this survey will serve as a first resource for the development of future operational text summarisation techniques for EBM.