DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5 6 7 8...72
Hits 61 – 80 of 1.423

61
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction ...
BASE
Show details
62
The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English ...
BASE
Show details
63
Situation-Based Multiparticipant Chat Summarization: a Concept, an Exploration-Annotation Tool and an Example Collection ...
BASE
Show details
64
Don't Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models ...
BASE
Show details
65
Boundary Detection with BERT for Span-level Emotion Cause Analysis ...
BASE
Show details
66
TexSmart: A System for Enhanced Natural Language Understanding ...
BASE
Show details
67
AND does not mean OR: Using Formal Languages to Study Language Models’ Representations ...
BASE
Show details
68
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images ...
BASE
Show details
69
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
BASE
Show details
70
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
BASE
Show details
71
DALC: the Dutch Abusive Language Corpus ...
BASE
Show details
72
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition ...
BASE
Show details
73
Document-level Event Extraction via Parallel Prediction Networks ...
BASE
Show details
74
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes ...
BASE
Show details
75
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.487 Abstract: Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to iden- tify and classify named entity mentions. Pro- totypical network shows superior performance on few-shot NER. However, existing prototyp- ical methods fail to differentiate rich seman- tics in other-class words, which will aggravate overfitting under few shot scenario. To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different unde- fined classes from the other class to improve few-shot NER. With these extra-labeled unde- fined classes, our method will improve the dis- criminative ability of NER classifier and en- hance the understanding of predefined classes with stand-by semantic knowledge. Experi- mental results demonstrate that our model out- performs five state-of-the-art models in both 1- shot and 5-shots settings on four NER bench- marks. We will release the code upon ...
URL: https://dx.doi.org/10.48448/xvx2-6258
https://underline.io/lecture/25935-learning-from-miscellaneous-other-class-words-for-few-shot-named-entity-recognition
BASE
Hide details
76
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental ...
BASE
Show details
77
Attention-based Contextual Language Model Adaptation for Speech Recognition ...
BASE
Show details
78
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech ...
BASE
Show details
79
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning ...
BASE
Show details
80
LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification ...
BASE
Show details

Page: 1 2 3 4 5 6 7 8...72

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
1.423
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern