Semanlink

Parents :

OOV

Amis :

Domain adaptation: vocabulary

Descendants

5 Documents (Liste longue)

Entity Embedding Completion for Wide-Coverage Entity Disambiguation

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2022-12-11 A propos

Adding New Words into a Language Model using Parameters of Known Words with Similar Behavior (2018)

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2022-03-21 A propos

Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge (ACL Anthology 2020)

Tags:

Aim to leverage both contextual representation of input text (deep LMs)  and  knowledge derived
from curated KBs ([Wordnet](tag:wordnet)) to improve [slot tagging](tag:slot_tagging) in the presence of [out-of-vocab](tag:oov) words ([few-shot scenario](tag:few_shot_learning))

Method:

1. retrieve potentially relevant KB entities and
encode them into distributed representations that
describe global graph-structured information
2. BERT encoder
layer to capture context-aware representations of
the sequence and attend to the KB embeddings
using multi-level graph attention
3. integrate
BERT embeddings and the KB embeddings
to predict the slot type

Contributions:

1. feasibility of applying lexical ontology
to facilitate recognizing OOV words. First to consider the large-scale background
knowledge for enhancing context-aware
slot tagging models.
2. a knowledge integration mechanism that uses multi-level graph
attention to model explicit lexical relations.
3.experiments on two benchmark datasets

> our method makes a notable difference in a
scenario where samples are linguistically diverse,
and large vocab exists.

(Better improvements when using RNN than BERT, because BERT already contains a lot of background knowledge)

2020-07-04 A propos

Using FastText models (not vectors) for robust embeddings | Kaggle

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2019-01-29 A propos

[1703.00993] A Comparative Study of Word Embeddings for Reading Comprehension

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2017-08-28 A propos

Propriétés

sl:creationDate : 2019-01-29
sl:creationTime : 2019-01-29T12:50:46Z
rdf:type : sl:Tag
skos:altLabel : Out of vocabulary@en
skos:prefLabel : OOV