Search Results for author:
Found 24 papers, 8 papers with code
Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports
Aggregate mining exploration results can help companies and governments to optimise and police mining permits and operations, a necessity for transition to a renewable energy future, however, these results are buried in unstructured text.
Automatic Identification of 5C Vaccine Behaviour on Social Media
Monitoring vaccine behaviour through social media can guide health policy.
LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep Learning
Despite sophisticated neural network architectures, existing models often struggle to maintain consistent performance when confronted with Out-of-Distribution (OOD) samples, including natural corruptions, adversarial perturbations, and anomalous patterns.
SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry
Workplace accidents continue to pose significant risks for human safety, particularly in industries such as construction and manufacturing, and the necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount.
Capacity Constraint Analysis Using Object Detection for Smart Manufacturing
In this study, we have initially developed a Convolutional Neural Network (CNN) based OD model to tackle this issue.
Fairness-aware Class Imbalanced Learning
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups.
Learning Causal Bayesian Networks from Text
Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems.
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP
Although the Indonesian language is spoken by almost 200 million people and the 10th most spoken language in the world, it is under-represented in NLP research.
WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets.
WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources.
Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP
Catastrophic forgetting {---} whereby a model trained on one task is fine-tuned on a second, and in doing so, suffers a {``}catastrophic{''} drop in performance over the first task {---} is a hurdle in the development of better transfer learning techniques.
Massively Multilingual Transfer for NER
In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language.
Twitter Geolocation using Knowledge-Based Methods
Automatic geolocation of microblog posts from their text content is particularly difficult because many location-indicative terms are rare terms, notably entity names such as locations, people or local organisations.
Semi-supervised User Geolocation via Graph Convolutional Networks
Social media user geolocation is vital to many applications such as event detection.
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks
We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology.
A Neural Model for User Geolocation and Lexical Dialectology
We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms.
Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text
This paper presents the shared task for English Twitter geolocation prediction in WNUT 2016.
It is not all downhill from here: Syllable Contact Law in Persian
Syllable contact pairs crosslinguistically tend to have a falling sonority slope a constraint which is called the Syllable Contact Law SCL In this study the phonotactics of syllable contacts in 4202 CVCCVC words of Persian lexicon is investigated The consonants of Persian were divided into five sonority categories and the frequency of all possible sonority slopes is computed both in lexicon type frequency and in corpus token frequency Since an unmarked phonological structure has been shown to diachronically become more frequent we expect to see the same pattern for syllable contact pairs with falling sonority slope The correlation of sonority categories of the two consonants in a syllable contact pair is measured using Pointwise Mutual Information
P-trac Procedure: The Dispersion and Neutralization of Contrasts in Lexicon
Cognitive acoustic cues have an important role in shaping the phonological structure of language as a means to optimal communication.
Twitter User Geolocation Using a Unified Text and Network Prediction Model
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements:(1) the removal of "celebrity" nodes to increase location homophily and boost tractability, and (2) he incorporation of text-based geolocation priors for test users.
Exploiting Text and Network Context for Geolocation of Social Media Users
Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very little crossover between the two, and no bench-marking of the two approaches over compara- ble datasets.
