Deep learning in hospitality and tourism: a research framework agenda for future research
A Essien, G Chukwukelu - International Journal of Contemporary …, 2022 - emerald.com
Purpose This study aims to provide a systematic review of the existing literature on the
applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for …
applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for …
Adverse drug event detection and extraction from open data: A deep learning approach
B Fan, W Fan, C Smith - Information Processing & Management, 2020 - Elsevier
Drug prescription is a task that doctors face daily with each patient. However, when
prescribing drugs, doctors must be conscious of all potential drug side effects. In fact …
prescribing drugs, doctors must be conscious of all potential drug side effects. In fact …
A survey on narrative extraction from textual data
Narratives are present in many forms of human expression and can be understood as a
fundamental way of communication between people. Computational understanding of the …
fundamental way of communication between people. Computational understanding of the …
LearningToAdapt with word embeddings: Domain adaptation of Named Entity Recognition systems
Abstract The task of Named Entity Recognition (NER) is aimed at identifying named entities
in a given text and classifying them into pre-defined domain entity types such as persons …
in a given text and classifying them into pre-defined domain entity types such as persons …
A wrapper based binary bat algorithm with greedy crossover for attribute selection
S Akila, SA Christe - Expert Systems with Applications, 2022 - Elsevier
Attribute selection plays a vital role in optimization and machine learning that involves huge
datasets. Classification accuracy of any learning model depends on the dimensionality of …
datasets. Classification accuracy of any learning model depends on the dimensionality of …
Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation
Financial news items are unstructured sources of information that can be mined to extract
knowledge for market screening applications. They are typically written by market experts …
knowledge for market screening applications. They are typically written by market experts …
A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data
Challenges in the big data phenomenon arise due to the existence of unstructured text data,
which is very large, comes from various sources, has various formats, and contains much …
which is very large, comes from various sources, has various formats, and contains much …
Extracting temporal and causal relations based on event networks
Event relations specify how different event flows expressed within the context of a textual
passage relate to each other in terms of temporal and causal sequences. There have …
passage relate to each other in terms of temporal and causal sequences. There have …
Sequence tagging with a rethinking structure for joint entity and relation extraction
Joint entity and relation extraction have become increasingly popular due to their knowledge
graph construction advantages. Despite the promising results on this task, disregarding the …
graph construction advantages. Despite the promising results on this task, disregarding the …
Persona analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization
Personified big data and rapidly developing data science techniques enable previously
unforeseen methodological developments for longitudinal analysis of online audiences …
unforeseen methodological developments for longitudinal analysis of online audiences …