2017
DOI: 10.1609/aaai.v31i1.10985
|View full text |Cite
|
Sign up to set email alerts
|

Learning Context-Specific Word/Character Embeddings

Abstract: Unsupervised word representations have demonstrated improvements in predictive generalization on various NLP tasks. Most of the existing models are in fact good at capturing the relatedness among words rather than their ''genuine'' similarity because the context representations are often represented by a sum (or an average) of the neighbor's embeddings, which simplifies the computation but ignores an important fact that the meaning of a word is determined by its context, reflecting not only the surrounding wor… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
5
3
1

Relationship

0
9

Authors

Journals

citations
Cited by 13 publications
(4 citation statements)
references
References 11 publications
0
4
0
Order By: Relevance
“…The same word in Chinese may express different meanings in different words. 18 Because word embedding is out of the word, it cannot express the difference in different contexts, so it is not advisable to use the word embedding directly. The position of a single word in the entire word can be used as an index of the word in the entire word.…”
Section: Character and Word Embeddingsmentioning
confidence: 99%
“…The same word in Chinese may express different meanings in different words. 18 Because word embedding is out of the word, it cannot express the difference in different contexts, so it is not advisable to use the word embedding directly. The position of a single word in the entire word can be used as an index of the word in the entire word.…”
Section: Character and Word Embeddingsmentioning
confidence: 99%
“…Xu et al (2021) proposed a hybrid model by using BERT to capture the word vector with global sentence features, then using TextCNN to capture the local features. Chen et al (2015) decomposed Chinese words into characters and proposed a character-enhanced word embedding model.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Datasets similar to BATS exist in Japanese and Icelandic (Karpinska et al, 2018;Friðriksdóttir et al, 2022), whereas GATS has been translated in Portuguese, Hindi, French, Polish, and Spanish (Hartmann et al, 2017;Grave et al, 2018;Cardellino, 2019). Other independently constructed datasets do exist (e.g., Venekoski and Vankka, 2017;Svoboda and Brychcín, 2018)crucially, covering all languages of interest to this study: in Chinese (Jin and Wu, 2012;Chen et al, 2015;, Dutch (Garneau et al, 2021), English (Turney 2008Mikolov et al 2013b, a.o. ), French (Grave et al, 2018), German (Köper et al, 2015), Italian (Berardi et al, 2015), and Spanish (Cardellino, 2019).…”
Section: Related Workmentioning
confidence: 99%