Machine-Learning-Augmented Analysis of Textual Data: Application in Transit Disruption Management
Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are often manually processed and subsequently stored in a standardized format for later use. The information con...
Main Authors: | Peyman Noursalehi, Haris N. Koutsopoulos, Jinhua Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Open Journal of Intelligent Transportation Systems |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9261594/ |
Similar Items
-
Machine-Learning-Augmented Analysis of Textual Data: Application in Transit Disruption Management
by: Noursalehi, Peyman, et al.
Published: (2022) -
The language of proteins: NLP, machine learning & protein sequences
by: Dan Ofer, et al.
Published: (2021-01-01) -
The Automatic Detection of Dataset Names in Scientific Articles
by: Jenny Heddes, et al.
Published: (2021-08-01) -
Benchmarking Natural Language Inference and Semantic Textual Similarity for Portuguese
by: Pedro Fialho, et al.
Published: (2020-10-01) -
French AXA Insurance Word Embeddings : Effects of Fine-tuning BERT and Camembert on AXA France’s data
by: Zouari, Hend
Published: (2020)