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AI-900 > NLP > Flashcards

NLP Flashcards

1
Q

What NLP services does Azure provide?

A

Text Analytics
Translator
Speech
Language Understanding Intelligent Service (LUIS)

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2
Q

What would you use Translator Text for?

A

Automatically translating spoken or written word between languages.

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3
Q

What would you use LUIS for?

A

Interpreting commands and determining appropriate actions.

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4
Q

What is Text Analytics?

A

A cloud-based service that provides advanced natural language processing over raw text for sentiment analysis, key phrase extraction, named entity recognition and language detection.

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5
Q

What would you use Text Analytics for?

A

Analysing and interpreting text in documents, email messages, and other sources.

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6
Q

What is frequency analysis?

A

Counting how often each word appears in text.

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7
Q

What is an N-gram?

A

A contiguous sequence of n items from a given sample of text or speech.

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8
Q

What is an entity in Text Analytics?

A

People, places, organisations or everyday items like dates, times, quantities, and so on.

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9
Q

What does the language capability of Text Analytics detect for each document submitted to it?

A

The language (e.g. ‘English’)
The ISO 6391 code (e.g. ‘en’)
A score indicating the level of confidence in the language detection.

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10
Q

What happens if a text with a mix of languages is submitted to Text Analytics service?

A

The service will focus on the predominant language in the text, and may return a confidence score less than 1.0

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11
Q

What could happen if you submitted ambiguous content to the Text Analytics service?

A

The service would return the language name and the language identifier as unknown and a score of NaN. (not a number)

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12
Q

What would you use the language detection capability of Text Analytics for?

A

To identify the language a text is written in.

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13
Q

What would you use the sentiment analysis capability of Text Analytics for?

A

Evaluating text to return sentiment scores and labels for each sentence.

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14
Q

What is the range of the sentiment score?

A

0 - 1

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15
Q

What is a positive sentiment score?

A

Values close to 1.

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16
Q

What is a negative sentiment score?

A

Values close to 0.

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17
Q

What is a neutral or indeterminate sentiment score?

A

0.5

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18
Q

What could cause an indeterminate sentiment score?

A

A sentence without structure (like a list of words), or using the wrong language code. (e.g. telling the service a document is en(glish) but its actually fr(ench))

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19
Q

What would you use the key phrase extraction capability of Text Analytics for?

A

Identifying the main talking points of a document(s).

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20
Q

What would you use the entity recognition capability of Text Analytics for?

A

Getting a list of entities from a piece of text. The service can also provide links to more information about that entity on the web.

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21
Q

What is Entity Linking?

A

The ability to disambiguate entities by linking to a specific reference. (Wikipedia)

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22
Q

What is Speech?

A

The unification of speech-to-text, text-to-speech, and speech-translation into a single Azure subscription.

23
Q

What would you use Speech for?

A

Interpreting spoken language, and synthesizing speech responses.

24
Q

What is speech-to-text?

A

An API that performs real-time or batch transcription of audio into a text format.

25
What is text-to-speech?
An API that enables you to convert text input to audible speech, which can either be played directly through computer speakers or written to an audio file.
26
What model is used by the speech-to-text API?
The Universal Language Model trained by Microsoft using Microsoft owned data, and is deployed to Microsoft Azure.
27
What is the speech-to-text model optimised for?
Dictation and conversation.
28
Can you use your own models for speech-to-text?
Yes, you can create and train your own custom models including acoustics, language, and pronunciation if the pre-built models from Microsoft are insufficient for your use case.
29
What is needed in order for real-time transcription (real-time speech-to-text) to work?
An application has to be listening to incoming audio from a microphone, or other audio input source such as an audio file.
30
How should batch transcription (batch speech-to-text) jobs be run?
Asynchronously as the batch jobs are scheduled on a best-effort basis. Normally, jobs start within minutes of the request, but there's no estimate for when a job changes into the running state.
31
How would you personalised your speech synthesis solution?
By specifying the voice to be used to vocalise the text.
32
What voices are available with text-to-speech?
Multiple pre-defined voices with support for multiple languages and regional pronunciation, including standard voices as well as neural voices. Additionally, you can develop custom voices for use with the service.
33
What is a neural voice?
A voice that leverages neural networks to overcome common limitations in speech synthesis with regards to intonation, resulting in a more natural sounding voice.
34
True or false: Speech-to text and text-to-speech only supports English?
False; They support a variety of languages.
35
What is speech recognition?
The ability to detect and interpret spoken input.
36
What is speech synthesis?
The ability to generate spoken output.
37
What is Language Understanding (LUIS)?
A cloud-based conversational AI service that applied custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
38
How can you access Language Understanding?
Through its web portal, APIs, and SDK client libraries.
39
What are Language Understanding's 3 core concepts?
Utterances, Intents, and Entities.
40
What is an utterance?
An example of something a user might say, and which an application must interpret.
41
What is an entity?
An item to which an utterance refers.
42
What is an intent?
The goal expressed in a user's utterance.
43
What is the None intent?
A fall-back for providing a generic response to users when their requests don't match any other intent. It is a required intent that can't be deleted or renamed.
44
What are the 2 types of Language Understanding service?
Authoring and prediction.
45
What are the three choices when creating a Language Understanding resource?
Authoring, prediction or both.
46
What happens if you choose both when creating a Language Understanding resource?
Two resources will be made, a prediction resource and an authoring resource.
47
What is the best practise for using Language Understanding?
Use the portal for authoring and the SDK for runtime predictions.
48
What are the 4 types of entity?
Machine-Learned List RegEx Pattern.any
49
What is a Machine-Learned entity?
Entities that are learned by your model during training from context in the sample utterances you provide.
50
What is a List entity?
Entities that are defined as a hierarchy of lists and sublists.
51
What is a RegEx entity?
Entities that are defined as a regular expression that describes a pattern.
52
What is a Pattern,any entity?
Entities that are used with patterns to define complex entities that may be hard to extract from sample utterances.
53
What is training in Language Understanding?
The process of using your sample utterances to teach your model to match natural language expressions that a user might say to probable intents and entities.

AI-900 (7 decks)

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