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Microsoft Linguistic Analysis API

4.0
(2)

Microsoft Linguistic Analysis APIs is a tool that provide access to natural language processing (NLP) that identify the structure of text and it provides three types of analysis:Sentence separation and tokenization, Part-of-speech tagging and Constituency parsing.

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Microsoft Linguistic Analysis API review by Mitchell A.
Mitchell A.
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"A great API for part of speech tagging"

What do you like best?

I have personally only used Microsoft's Linguistic Analysis API for part of speech tagging, but its POS tagger has served me well for several years. I rarely have issues with it being inaccurate, and its response time is usually pretty quick. Additionally, it is an Azure service, so if you're currently an active Azure user, it is relatively easy to gain access to the Linguistic Analysis API.

What do you dislike?

The rate limit was somewhat prohibitive for the project I was working on since I needed to make several different calls every few seconds. Additionally, there were several outages that obstructed my work throughout my time using the API.

Recommendations to others considering the product

Microsoft's Linguistic Analysis API offers services aside from part of speech tagging, but if you're looking for the best part of speech tagger to use, I would personally recommend using Stanford's open-source tagger. Unlike Microsoft's, you don't need any API keys, you don't need to worry about being dependent on an online solution, and you can make as many calls as your system can handle.

What business problems are you solving with the product? What benefits have you realized?

I previously used this API for my conversational artificial intelligence program, Aurora. The only component of the Linguistic Analysis API I used was the part of speech analyzer, but I ultimately ended up switching to Stanford's open source offline POS tagger to avoid being obstructed by API outages.

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Microsoft Linguistic Analysis API review by Sherri W.
Sherri W.
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"MS Linguistics API"

What do you like best?

I enjoy the data structuring and collection process of this build. The ease of use of the application in general is nice. Although not to be supported after this summer, the Azure sight documentation is quite sufficient.

What do you dislike?

It actually seems like a framework of a larger program snippet that doesn't yet exist. Simpler is not always better and then there is the question if consistency and overall stability.

Recommendations to others considering the product

Analyze trends and repetition

What business problems are you solving with the product? What benefits have you realized?

It has provided a generally enlightened experience in solving obstacles in our customer relations and feedback when attempting to decipher how to best meet our consumers needs.

What Natural Language Processing (NLP) solution do you use?

Thanks for letting us know!

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