Key NLP Tasks That Machine Models Can Handle

In my previous article What is Natural Language Processing we understand, NLP deals with processing and understanding textual data. Let's find out some of the common NLP task which machines are helping us out.

• Automatic-speech-recognition (ASR): Converting spoken language into text.

• Conversational: Tasks involving interactions between humans and machines using natural language (e.g., chatbots, virtual assistants).

• Document-question-answering (QA): Extracting answers to questions from a given document.

• Feature-extraction (for text): Converting textual data into numerical features for machine learning models.

• Fill-mask: Predicting the missing word(s) in a sentence based on context.

• NER (Named Entity Recognition): Identifying and classifying named entities in text (e.g., people, locations, organizations).

• Question-answering (QA): Finding answers to open ended questions, possibly from a variety of sources.

• Sentiment-analysis: Determining the emotional tone of a piece of text (e.g., positive, negative, neutral).

• Summarization: Condensing a piece of text while preserving the main points.

• Text-classification: Categorizing text documents based on their content (e.g., spam detection, topic classification).

• Text-generation: Creating new text content, like poems, code, scripts, or translating languages.

• Text-to-speech (TTS): Converting written text into spoken language.

• Text2text-generation: Transforming text from one form to another (e.g., paraphrase generation, machine translation).

• Token-classification: Assigning labels (like part-of-speech) to individual words in a sentence.

• Translation (language to language): Converting text from one language to another.

Aren't you curious on how does machines work on these task? Let's explore that in my next article - Transformer

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