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Adjusting Word Embeddings by Deep Neural Networks paper by Independent / Community
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Paper

Adjusting Word Embeddings by Deep Neural Networks

by Independent / Community 02ac69b4456ddde3a36708161386a4d5b577e8cc
Free2AITools Nexus Index
63.2
S: Semantic 50

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A: Authority 70
P: Popularity 45
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Continuous representations language models have gained popularity in many NLP tasks. To measure the similarity of two words, we have to calculate their cosine distances. However the qualities of word embeddings depend on the corpus selected. As for word2vec, we observe that the vectors are far apart to each other. Furthermore, synonym words with low occurrences or with multiple meanings are even further in distance. In these cases, cosine similarities are no longer appropriate to evaluate how...

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Semantic Scholar 4 Citations
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Registry ID 02ac69b4456ddde3a36708161386a4d5b577e8cc
License ArXiv
Provider semantic_scholar
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BibTeX 
@misc{02ac69b4456ddde3a36708161386a4d5b577e8cc,
  author = {Unknown},
  title = {Adjusting Word Embeddings by Deep Neural Networks Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/02ac69b4456ddde3a36708161386a4d5b577e8cc}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Adjusting Word Embeddings by Deep Neural Networks [Paper]. Free2AITools. https://api.semanticscholar.org/02ac69b4456ddde3a36708161386a4d5b577e8cc

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 70
Popularity (P) 45
Recency (R) 100
Quality (Q) 65

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FNI V2.0 for Adjusting Word Embeddings by Deep Neural Networks: Authority (A:70), Popularity (P:45), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Continuous representations language models have gained popularity in many NLP tasks. To measure the similarity of two words, we have to calculate their cosine distances. However the qualities of word embeddings depend on the corpus selected. As for word2vec, we observe that the vectors are far apart to each other. Furthermore, synonym words with low occurrences or with multiple meanings are even further in distance. In these cases, cosine similarities are no longer appropriate to evaluate how..."

❝ Cite Node 

@article{Unknown2026Adjusting,
  title={Adjusting Word Embeddings by Deep Neural Networks},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ4CitationsSemantic Scholar
πŸ›οΈ70AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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🏷️ Research Topics

vector databasesembeddings
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ArXiv
tags
paper, research, academic

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