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Intelligent Data And Content Streams Using Machine Learning APIs

#artificialintelligence

We have been profiling a number of machine learning APIs lately, not because there is an opportunity to proxy and stream the APIs, but because of the possibilities around applying common machine learning models to the data and content streams our customers are producing. One of the interesting machine learning APIs we are profiling currently is called ParallelDots, which provide a suite of common, yet powerful machine learning models that anyone can integrate into their applications. As we profile the ParallelDots API, we are considering the possibilities for trickling, or streaming updates via the APIs our customer's are proxying using our service. Consider some of the opportunities for posting stream updates to any of the following APIs: - Sentiment - Sentiment API accepts input text, language code and API key to return a JSON response classifying the input text into a sentiment. API can extract this information from any type of text, web page or social media network.


Comparison of the Most Useful Text Processing APIs – ActiveWizards: machine learning company – Medium

#artificialintelligence

Nowadays, text processing is developing rapidly, and several big companies provide their products which help to deal successfully with diverse text processing tasks. In case you need to do some text processing there are 2 options available. The first one is to develop the entire system on your own from scratch. This way proves to be very time and resource consuming. On the other hand, you can use the already accessible solutions developed by well-known companies. This option is usually faster and simpler.



Keyphrase Based Arabic Summarizer (KPAS)

arXiv.org Artificial Intelligence

This paper describes a computationally inexpensive and efficient generic summarization algorithm for Arabic texts. The algorithm belongs to extractive summarization family, which reduces the problem into representative sentences identification and extraction sub-problems. Important keyphrases of the document to be summarized are identified employing combinations of statistical and linguistic features. The sentence extraction algorithm exploits keyphrases as the primary attributes to rank a sentence. The present experimental work, demonstrates different techniques for achieving various summarization goals including: informative richness, coverage of both main and auxiliary topics, and keeping redundancy to a minimum. A scoring scheme is then adopted that balances between these summarization goals. To evaluate the resulted Arabic summaries with well-established systems, aligned English/Arabic texts are used through the experiments.


Comparison of the Most Useful Text Processing APIs

#artificialintelligence

Nowadays, text processing is developing rapidly, and several big companies provide their products which help to deal successfully with diverse text processing tasks. In case you need to do some text processing there are 2 options available. The first one is to develop the entire system on your own from scratch. This way proves to be very time and resource consuming. On the other hand, you can use the already accessible solutions developed by well-known companies. This option is usually faster and simpler.