DE eng

Search in the Catalogues and Directories

Hits 1 – 16 of 16

1
What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification ...
Bassignana, Elisa; Plank, Barbara. - : arXiv, 2022
BASE
Show details
2
How Universal is Genre in Universal Dependencies? ...
BASE
Show details
3
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions ...
BASE
Show details
4
Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
BASE
Show details
5
DaN+: Danish Nested Named Entities and Lexical Normalization ...
BASE
Show details
6
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ...
BASE
Show details
7
Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering ...
BASE
Show details
8
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat ...
BASE
Show details
9
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging ...
Plank, Barbara; Agić, Željko. - : arXiv, 2018
BASE
Show details
10
The Best of Both Worlds: Lexical Resources To Improve Low-Resource Part-of-Speech Tagging ...
BASE
Show details
11
Bleaching Text: Abstract Features for Cross-lingual Gender Prediction ...
BASE
Show details
12
ALL-IN-1: Short Text Classification with One Model for All Languages ...
Plank, Barbara. - : arXiv, 2017
BASE
Show details
13
Parsing Universal Dependencies without training ...
BASE
Show details
14
Semantic Tagging with Deep Residual Networks ...
BASE
Show details
15
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss ...
Abstract: Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful for various NLP sequence modeling tasks, but little is known about their reliance to input representations, target languages, data set size, and label noise. We address these issues and evaluate bi-LSTMs with word, character, and unicode byte embeddings for POS tagging. We compare bi-LSTMs to traditional POS taggers across languages and data sizes. We also present a novel bi-LSTM model, which combines the POS tagging loss function with an auxiliary loss function that accounts for rare words. The model obtains state-of-the-art performance across 22 languages, and works especially well for morphologically complex languages. Our analysis suggests that bi-LSTMs are less sensitive to training data size and label corruptions (at small noise levels) than previously assumed. ... : In ACL 2016 (short) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1604.05529
https://arxiv.org/abs/1604.05529
BASE
Hide details
16
Keystroke dynamics as signal for shallow syntactic parsing ...
Plank, Barbara. - : arXiv, 2016
BASE
Show details

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
16
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern