Suggest the best content spinner

Suggest the best content spinner
Please suggest me the best content spinner. Out should be like manually written.
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Everyone going to recommend Wordai and its good one but nowadays you can get handwritten for $2 a piece.
Spin Rewriter works quite well. And its a lot cheaper than Wordai which I find isnt worth the cost.
Spin rewriter is good and cheap also.
Everyone going to recommend Wordai and its good one but nowadays you can get handwritten for $2 a piece.
Yes mate I know that I need more articles to written and I can't spend that amount in this situataion
Sorry, OP. I still say human hands are better than Spinning software. If you don't have time, you can get it done cheap.
As barrier to entry increases, many of the legacy domains which could have one day been worth developing have lost much of their value.
And the picked over new TLDs are an even worse investment due to the near infinite downside potential of price hikes, registries outright folding, etc.
Most of the registration graphs for new TLDs are far uglier than the one posted above. China will not save the new gTLDs.
Looking at the chart as we have from over 300K to 65K red is the appropriate color; over 90% registered in China https://t.co/eJMHSwoTVV https://t.co/JlrJ7sMPc5
— The Domains (@thedomains) March 14, 2020

This is a look back at a big change in search but which continues to be important: understanding synonyms. How people search is often different from information that people write

solutions about. pic.twitter.com/sBcR4tR4eT— Danny Sullivan (@dannysullivan) September 24, 2020
Last few months, Google has been using neural matching, --AI method to better connect words to concepts. Super synonyms, in a way, and impacting 30% of queries. Don't know what

"soapopera effect" is to search for it? We can better figure it out. pic.twitter.com/Qrwp5hKFNz— Danny Sullivan (@dannysullivan) September 24, 2020
The above Tweets capture what the neural matching technology intends to do. Google also stated:
we’ve now reached the point where neural networks can help us take a major leap forward from understanding words to understanding concepts. Neural embeddings, an approach

developed in the field of neural networks, allow us to transform words to fuzzier representations of the underlying concepts, and then match the concepts in the query with the

concepts in the document. We call this technique neural matching.
To help people understand the difference between neural matching & RankBrain, Google told SEL: "RankBrain helps Google better relate pages to concepts. Neural matching helps

Google better relate words to searches."
There are a couple research papers on neural matching.
The first one was titled A Deep Relevance Matching Model for Ad-hoc Retrieval. It mentioned using Word2vec & here are a few quotes from the research paper
"Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements."
"the interaction-focused model, which first builds local level interactions (i.e., local matching signals) between two pieces of text, and then uses deep neural networks to learn

hierarchical interaction patterns for matching."
Suggest the best content spinner
"according to the diverse matching requirement, relevance matching is not position related since it could happen in any position in a long document."
"Most NLP tasks concern semantic matching, i.e., identifying the semantic meaning and infer"ring the semantic relations between two pieces of text, while the ad-hoc retrieval task

is mainly about relevance matching, i.e., identifying whether a document is relevant to a given query."
"Since the ad-hoc retrieval task is fundamentally a ranking problem, we employ a pairwise ranking loss such as hinge loss to train our deep relevance matching model."
The paper mentions how semantic matching falls down when compared against relevancy matching because:
semantic matching relies on similarity matching signals (some words or phrases with the same meaning might be semantically distant), compositional meanings (matching sentences

more than meaning) & a global matching requirement (comparing things in their entirety instead of looking at the best matching part of a longer document); whereas,
relevance matching can put significant weight on exact matching signals (weighting an exact match higher than a near match), adjust weighting on query term importance (one word

might or phrase in a search query might have a far higher discrimination value & might deserve far more weight than the next) & leverage diverse matching requirements (allowing

relevancy matching to happen in any part of a longer document)
Here are a couple images from the above research paper


And then the second research paper is
Deep Relevancy Ranking Using Enhanced Dcoument-Query Interactions
"interaction-based models are less efficient, since one cannot index a document representation independently of the query. This is less important, though, when relevancy ranking

methods rerank the top documents returned by a conventional IR engine, which is the scenario we consider here."
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