Natural language tool online

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Hi, Our Zend Certified PHP Engineers with 9+ years of experience can do this project precisely and exactly as per your requirements. We have expertise in PHP/ MySQL/JQuery / CSS/ Javascript/HTML etc. USP 1) We prove cheaper as our developers are 3 times faster than others saving us on time=money, the benefit of which we pass on to our clients. 2) Being well qualified, our developers create coding that meets highest quality standards and is highly secure. You may look into our reviews at freelancer.com to get insight of our commitment towards work and customer satisfaction. Working Methodology: 1) We analyze and bid on project. 2) Client award us project and create 100% milestone payment. 3) We discuss project in details and accept and start working on project. 4) Test server details are provided to client so they can track progress. 5) Client can test the work on our server and once satisfied, releases the payment in full after which we deliver the code / install on client server. 6) Once client is satisfied with work, we exchange reviews. 7) We provide after delivery bug fixing free of cost upto 6 months unless the code is modified at client end. 8) Any changes falling out of scope of project are chargeable. Best Regards Nirmala Om Technologies

Hello, To compare the two texts, we will need to determine TF-IDF (Term Frequency Inverse Document Frequency). Then we can calculate their similarity distance to provide a probability of being similar text. NLP work involves lots of process: cleaning the data, stop word removal etc. As you are going to make it a online tool, I need to know some constraints: 1. What would be the max length of the text 2. Do we need to give output instantly or email them the result after computing.. More questions will follow, as we start discussing things. Regards, Samiran

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I am an research engineer working on machine learning and NLP. To accomplish this task, it's better to suppose people's writing style is a closed set. Collect language corpus on each writing style and train a language language model on each corpus. Then we have something like supervised task to classify whether each pair of sentence belongs to the same writing style, or at least has similar writing style probability distribution. To simplify my method, 1) collect different writing style(news, literature, report, research paper and so on) 2) build language model on each writing style corpus. 3) for each sentence of a given pair, compute ngram probability on each sentence as features. 4) classify extracted features into writing style. Note: training data can be extracted from collected corpus btw: if two sentences are in the same professional area, it may be really hard to distinguish whether a pair of sentence is from the same guy.