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Text analytics APIs, Part 1: The bigger players

  • ROBERT DALE (a1)

Abstract

If you’re in the market for an off-the-shelf text analytics API, you have a lot of options. You can choose to go with a major player in the software world, for whom each AI-related service is just another entry in their vast catalogues of tools, or you can go for a smaller provider that focusses on text analytics as their core business. In this first of two related posts, we look at what the most prominent software giants have to offer today.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

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1 All the information provided here was accurate, as far as I can determine, in January 2018; but things can change rapidly, so you’d be best advised to double check any critical information before making a decision based on this post.

12 Strangely, as the example in Figure 1 shows, the API fails to find the DBpedia resource for New Zealand.

13 This is Reuters file 239046newsML.

15 IBM’s poor performance on the MISC category appears to be largely due to it failing to recognise any nationality adjectives, like Croat and German, which CoNLL tags as MISC.

16 Again, the Microsoft results are not represented here since these do not contain type information for the recognised entities.

20 Of course, you could use different providers for different functionalities, but that’s a risky road; for example, as noted earlier, tokenisation isn’t handled the same way across the four providers, so you might hit alignment problems if you try a mix-and-match approach.

21 Again, Amazon does not do any entity linking at all.

Text analytics APIs, Part 1: The bigger players

  • ROBERT DALE (a1)

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