Pith. sign in

REVIEW 3 cited by

Natural Language Processing Advancements By Deep Learning: A Survey

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2003.01200 v4 pith:RA377JHB submitted 2020-03-02 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords deeplearninglanguageapplicationsapproachesareasdata-drivendifferent
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Context Attribution Handles What the Model Already Knows

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Context attribution methods cannot disentangle in-context from in-weight knowledge and assign unfaithful scores under overlap; new metrics and WMDP-Cyber++ quantify the failure.

  2. Detoxify: A framework for abusive text transformation using LLMs

    cs.CL 2025-07 reject novelty 3.0 of 10

    A comparative study claims Groq produces the most positive but least semantically faithful detoxified text, but the comparison is undermined by inconsistent methodology.

  3. Enhancing Classification with Semi-Supervised Deep Learning Using Distance-Based Sample Weights

    cs.LG 2025-05 reject novelty 2.0 of 10

    A distance-based sample weighting scheme, taken from the authors' prior Semi-Cart work, is plugged into a neural network loss and reported to beat a supervised baseline on tabular benchmarks.

Pith tools