An overview on machine translation evaluation
L Han - arXiv preprint arXiv:2202.11027, 2022 - arxiv.org
Since the 1950s, machine translation (MT) has become one of the important tasks of AI and
development, and has experienced several different periods and stages of development …
development, and has experienced several different periods and stages of development …
Medtem2. 0: Prompt-based temporal classification of treatment events from discharge summaries
Discharge summaries are comprehensive medical records that encompass vital information
about a patient's hospital stay. A crucial aspect of discharge summaries is the temporal …
about a patient's hospital stay. A crucial aspect of discharge summaries is the temporal …
Detection of verbal multi-word expressions via conditional random fields with syntactic dependency features and semantic re-ranking
A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running
text is presented. The system mainly exploits universal syntactic dependency features …
text is presented. The system mainly exploits universal syntactic dependency features …
CWPC_BiAtt: Character–word–position combined BiLSTM-attention for Chinese named entity recognition
S Johnson, S Shen, Y Liu - Information, 2020 - mdpi.com
Usually taken as linguistic features by Part-Of-Speech (POS) tagging, Named Entity
Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we …
Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we …
A graph-based semi-supervised multi-label learning method based on label correlation consistency
Multi-label learning deals with the problem which each data example can be represented by
an instance and associated with a set of labels, ie, every example can be classified into …
an instance and associated with a set of labels, ie, every example can be classified into …
Extraction of Medication and Temporal Relation from Clinical Text using Neural Language Models
Clinical texts, represented in electronic medical records (EMRs), contain rich medical
information and are essential for disease prediction, personalised information …
information and are essential for disease prediction, personalised information …
Random multi-graphs: a semi-supervised learning framework for classification of high dimensional data
Currently, high dimensional data processing confronts two main difficulties: inefficient
similarity measure and high computational complexity in both time and memory space …
similarity measure and high computational complexity in both time and memory space …
[PDF][PDF] Semantic reranking of CRF label sequences for verbal multiword expression identification
Moreau, Alsulaimani, Maldonado, Han, Vogel & Dutta Chowdhury that of its constituent
words. This is why it uses semantic features based on comparing the context vector of a …
words. This is why it uses semantic features based on comparing the context vector of a …
Enhancing traditional Chinese medical named entity recognition with Dyn-Att Net: a dynamic attention approach
Our study focuses on Traditional Chinese Medical (TCM) named entity recognition (NER),
which involves identifying and extracting specific entity names from TCM record. This task …
which involves identifying and extracting specific entity names from TCM record. This task …
Extraction of Medication and Temporal Relation from Clinical Text by Harnessing Different Deep Learning Models
Clinical texts, represented in electronic medical records (EMRs), contain rich medical
information and are essential for disease prediction, personalised information …
information and are essential for disease prediction, personalised information …