AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
July 04, 2015 Β· Declared Dead Β· π Annual Meeting of the Association for Computational Linguistics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sascha Rothe, Hinrich SchΓΌtze
arXiv ID
1507.01127
Category
cs.CL: Computation & Language
Citations
290
Venue
Annual Meeting of the Association for Computational Linguistics
Last Checked
3 months ago
Abstract
We present \textit{AutoExtend}, a system to learn embeddings for synsets and lexemes. It is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The synset/lexeme embeddings obtained live in the same vector space as the word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet as a lexical resource, but AutoExtend can be easily applied to other resources like Freebase. AutoExtend achieves state-of-the-art performance on word similarity and word sense disambiguation tasks.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Computation & Language
π
π
Old Age
π
π
Old Age
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
RoBERTa: A Robustly Optimized BERT Pretraining Approach
R.I.P.
π»
Ghosted
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
R.I.P.
π»
Ghosted
Deep contextualized word representations
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
R.I.P.
π»
Ghosted