cndict
NLP-progress
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| cndict | NLP-progress | |
|---|---|---|
| - | 17 | |
| 10 | 22,309 | |
| - | - | |
| 0.0 | 2.1 | |
| over 6 years ago | 5 days ago | |
| Shell | Python | |
| LicenseRef-PublicDomain | MIT License |
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cndict
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Tracking mentions began in Dec 2020.
NLP-progress
- [Discussion] Checklist of seminal NLP papers
- NLP research status
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[D] How difficult/easy is to learn NLP once you have experience in a CV?
One thing is that NLP is a set of wildly different problems which share some aspects, but often use quite different techniques and assumptions about their datasets. So even if you would have NLP experience, if you'd need to start on a substantially different NLP task, you can't just apply what you know and succeed, you have to review "how things are done" for that problem domain. For a quick overview, sites like https://nlpprogress.com/ can be helpful to see what methods are used; and, perhaps even more importantly, how people are modeling the actual task.
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Upcoming App Announcement: Lemmatize, a Foreign Language Reader
A standard step in Chinese text processing is word segmentation, which deals with this problem.
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Is there as site tracking computer vision process?
NLP has a github project tracking NLP progress, https://github.com/sebastianruder/NLP-progress. I wanna know if there is one tracking computer vision progress.
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[P] NLP "tl;dr" Notes on Transformers
It would also be cool to have some charts with parameter density and even overall effectiveness (a tl;dr version of SOTA-trackers, maybe?) if that doesn't prove too infeasible.
- What are state-of-the-art methods for abstractive text summarization ?
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BreadPanes 81: "They/Them"
As I said It increase ambiguity and cognitive overheard, needlessly given that "it" exists. Moreover it also make it harder for artificial intelligence to understand human text https://github.com/sebastianruder/NLP-progress/blob/master/english/coreference_resolution.md
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[Request] Curated Advanced NLP Resources
I could not find it on the internet (including on GitHub, Kaggle, Medium, or Reddit.) And, I know about NLP Progress and The Super Duper NLP Repo.
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How do you guys find/ keep up to date with the latest NLP papers?
For someone who needs to be on top of the latest research - Twitter (distraction-prone, marketing-friendly, instantly-gratifying, quick), newsletters in ML + NLP (https://jack-clark.net/, ruder.io, offconvex.org, etc.) (distraction-free, generic, time-consuming), SOTA chasing (https://paperswithcode.com/, http://nlpprogress.com/) (distraction-free, generic + focused, code-friendly)
What are some alternatives?
crf-chain2-tiers - Second-order, tiered, constrained, linear conditional random fields
nlp_tasks - Natural Language Processing Tasks and References
phonetic-code - phonetic codes in Haskell
wtpsplit - Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
concraft-pl - A morphosyntactic tagger for Polish based on conditional random fields
SymSpell - SymSpell: 1 million times faster spelling correction & fuzzy search through Symmetric Delete spelling correction algorithm
PTQ - An implementation of Montague's PTQ (Proper Treatment of Quantification).
awesome-hungarian-nlp - A curated list of NLP resources for Hungarian
chatter - A library of Natural Language Processing algorithms for Haskell.
nlprule - A fast, low-resource Natural Language Processing and Text Correction library written in Rust.
numerals - Convert numbers to number words
OPUS-MT-train - Training open neural machine translation models