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https://doi.org/10.18653/v1/20...
Conference object . 2021
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Dynamic Contextualized Word Embeddings

Authors: Valentin Hofmann; Janet B. Pierrehumbert; Hinrich Schütze;

Dynamic Contextualized Word Embeddings

Abstract

Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic context. Based on a pretrained language model (PLM), dynamic contextualized word embeddings model time and social space jointly, which makes them attractive for a range of NLP tasks involving semantic variability. We highlight potential application scenarios by means of qualitative and quantitative analyses on four English datasets.

Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science business.industry media_common.quotation_subject Context (language use) computer.software_genre Range (mathematics) Word meaning Artificial intelligence Language model business Function (engineering) computer Natural language processing Word (computer architecture) media_common

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    8
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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visibility
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
8
Top 10%
Average
Top 10%
1
Funded by
EC| NonSequeToR
Project
NonSequeToR
Non-sequence models for tokenization replacement
  • Funder: European Commission (EC)
  • Project Code: 740516
  • Funding stream: H2020 | ERC | ERC-ADG
Validated by funder
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