Natural Language Processing Flashcards

1
Q

What is NLP?

A

Natural language processing (NLP) is the area of AI that deals with creating software that understands written and spoken language.

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2
Q

What kinds of software can NLP help create?

A

NLP enables you to create software that can:

  • Analyze text documents to extract key phrases and recognize entities (such as places, dates, or people).
  • Perform sentiment analysis to determine how positive or negative the language used in a document is.
  • Interpret spoken language, and synthesize speech responses.
  • Automatically translate spoken or written phrases between languages.
  • Interpret commands and determine appropriate actions.
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3
Q

Which cognitive services help build NLP solutions?

A

Text Analytics:Use this service to analyze text documents and extract key phrases, detect entities (such as places, dates, and people), and evaluate sentiment (how positive or negative a document is).

Translator Text: Use this service to translate text between more than 60 languages.

Speech: Use this service to recognize and synthesize speech, and to translate spoken languages.

Language Understanding: Use this service to train a language model that can understand spoken or text-based commands.

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4
Q

When should you choose Text Analytics resource?

A

choose this resource type if you only plan to use the Text Analytics service, or if you want to manage access and billing for the resource separately from other services.

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5
Q

When should you choose Cognitive Services resource?

A

choose this resource type if you plan to use the Text Analytics service in combination with other cognitive services, and you want to manage access and billing for these services together.

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6
Q

What are some things text analytics can do?

A

Language detection - determines which language the text is written in

** note if words are ambiguous or mixed will result in unknown for language name and NaN for confidence score

sentiment analysis - used to determine positive, negative or neutral sentiment. 0 represents negative, 0.5 is neutral and 1 is positive.

*** can also be indeterminate at 0.5 due to wrong language or sentence with no grammar

key phrase extraction - concept of evaluating the text of a document, or documents, and then identifying the main points around the context or context of the document(s).

entity detection - recognizes entities in text

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7
Q

When should you use a speech resource?

A

choose this resource type if you only plan to use the Speech service, or if you want to manage access and billing for the resource separately from other services.

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8
Q

When should you use a Cognitive Services resource?

A

choose this resource type if you plan to use the Speech service in combination with other cognitive services, and you want to manage access and billing for these services together.

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9
Q

What are some uses of the speech to text API?

A

Real time transcription - You can use real-time transcription for presentations, demos, or any other scenario where a person is speaking.

Batch transcription - reads speech from files. Should run asynchronously because the batch jobs are scheduled on a best-effort basis.

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10
Q

What are some uses of the text to speech API?

A

Speech Synthesis Voices - can choose voice type and leverages neural networks to make speech sound more natural.

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11
Q

What is a literal translation?

A

A literal translation is where each word is translated to the corresponding word in the target language.

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12
Q

What is a semantic translation?

A

Translates considering context, grammar and formality to give true equivalent interpretation of phrase

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13
Q

What are the 2 azure services for translation?

A

The Translator Text service, which supports text-to-text translation.
The Speech service, which enables speech-to-text and speech-to-speech translation.

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14
Q

What are the optional configurations of the Translator Text API?

A

Profanity filtering. Without any configuration, the service will translate the input text, without filtering out profanity. Profanity levels are typically culture-specific but you can control profanity translation by either marking the translated text as profane or by omitting it in the results.

Selective translation. You can tag content so that it isn’t translated. For example, you may want to tag code, a brand name, or a word/phrase that doesn’t make sense when localized.

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15
Q

What API’s are included in the Speech Service?

A

Speech-to-text - used to transcribe speech from an audio source to text format.

Text-to-speech - used to generate spoken audio from a text source.

Speech Translation - used to translate speech in one language to text or speech in another.

**You can use the Speech Translation API to translate spoken audio from a streaming source, such as a microphone or audio file, and return the translation as text or an audio stream.

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16
Q

What is an utterance?

A

An utterance is an example of something a user might say, and which your application must interpret.

17
Q

What is an entity?

A

An entity is an item to which an utterance refers.

18
Q

What is an intent?

A

An intent represents the purpose, or goal, expressed in a user’s utterance.

19
Q

How do you create a Language understanding?

A

Creating a language understanding application consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.

20
Q

What are the 2 types of resources available for creating authoring and predicting tasks?

A

Language Understanding: A dedicated resource for Language Understanding, which can be either an authoring or a prediction resource.

Cognitive Services: A general cognitive services resource that includes Language Understanding along with many other cognitive services. You can only use this type of resource for prediction.

21
Q

What are the 4 types of entities?

A

Machine-Learned: Entities that are learned by your model during training from context in the sample utterances you provide.

List: Entities that are defined as a hierarchy of lists and sublists. For example, a device list might include sublists for light and fan. For each list entry, you can specify synonyms, such as lamp for light.

RegEx: Entities that are defined as a regular expression that describes a pattern - for example, you might define a pattern like [0-9]{3}-[0-9]{3}-[0-9]{4} for telephone numbers of the form 555-123-4567.

Pattern.any: Entities that are used with patterns to define complex entities that may be hard to extract from sample utterances.