Channel
@nlpblog
On this record: Topic · Growth · Engagement · Reactions · Posts · Cite this entry
22subscribers
+2 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001607612095 |
|---|---|
| Type | Channel |
| Username | @nlpblog |
| Created | 10 May 2022 — measured — dated from the channel’s first post |
| First recorded | 11 August 2026 |
| Last confirmed live | 7 October 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 7 October 2026 |
| On Telegram | t.me/nlpblog |
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 1 October 2026 and assigned it the closest of 31 fixed categories, at 95% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Oct 2026, 12:17 | 22 | +1 |
| 26 Sept 2026, 22:01 | 21 | +2 |
| 20 Aug 2026, 15:16 | 19 | -1 |
| 11 Aug 2026, 16:15 | 20 | no change |
| 7 Aug 2026, 22:38 | 20 | first reading |
16 posts held, back to 10 May 2022 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 16 posts for this entry, the most recent from 21 May 2025. An engagement rate over an empty window would be a number about nothing.
1 reaction across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 1 | 100.0% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 1 of the 16 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 1 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 16 most recent posts we hold, published 10 May 2022 to 21 May 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
The owner of this channel has been inactive for the last 11 months. If they remain inactive for the next 8 days, they may lose their account and admin rights in this channel. The contents of the channel will remain accessible for all users.
Came across an interesting solution for Optical Character Recognition.
We have just launched a new python wrapper for website classification: https://pypi.org/project/websiteclassificationapi/. Python wrappers or packages are useful because they can abstract away many of the complexities and simplify the usage of APIs. Hope they will prove helpful.
Another important field where NLP is used is in the product categorization. This is assignment of product names to one or more predefined categories. The collection of categories is generally also known as product taxonomies. Product categorization can be e.g. very useful for online stores that want to categorize the products that they sell so that the customers can more easily find them. With categorization they can…
When doing cluster analysis of domains, trying to find similar domains based on NLP analysis of texts, a useful tool can be something which is not at all connected to the textual representations. Rather one can sometimes find related domains simply by mapping domains to IPs and then finding domains that are sharing the same IP. Often, what happens is that a writer or operator of some domain A is also operating other …
The benefits of website and URL categorization for adtech Categorical data is the cornerstone of many adtech campaigns. It's what allows advertisers to know exactly who their target market is and where they can find them. When implemented properly, categorical data can be used to build targeted campaigns that are laser-focused on getting the right message in front of the right people at the right time, because it us…
Machine learning models for natural language processing typically consist of three components: A pre-processing part that cleans up the input text. This might involve tokenization, part-of-speech tagging, or simply removing punctuation. A feature extraction stage that generates numerical representations of the text. These features can be used to train classifiers or other machine learning models. Examples include …
One trend in the recent years is that if your content wants to stand out then it needs to have great visualizations, e.g. infographics have become very popular. Data visualizations have thus become an important field because it can help you prepare great content which then has a higher likelihood of becoming very popular.
- It's much easier for humans to communicate using natural language than it is for them to communicate using code. This means that NLP will help make technology more accessible to those who don't have specialized knowledge about coding or other computer languages. - It helps computers understand context and meaning, which allows them to respond more accurately when they're asked questions by humans. This can help ma…
There are many other advantages to using NLP, including: -It can be used to interpret user intent based on natural language inputs, which improves the efficiency of your business and reduces the number of errors in your operations. -It can improve customer service by understanding what customers are saying and responding appropriately. For example, if a customer asks for help with an order, NLP could automatically …
Some further advantages of NLP:
What are the advantages of using natural language processing in your company? There are many advantages to the use of natural language processing in any business setting, e.g. in saas companies. Let's look at some of them: - It makes it easier for humans to communicate with machines. Natural language processing allows us to communicate with machines in the same way we communicate with each other! This is one of the…
Showing the 12 most recent of 16 posts we hold for @nlpblog. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 7 October 2026 — this entry's latest reading, not the date you are reading this.
“Natural Language Processing - NLP” (@nlpblog), 22 subscribers as measured 7 October 2026. Telegram Register, tgregister.com/channel/nlpblog.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.