25 NLP tasks at a glance.
Undoubtedly Natural Language Processing (NLP) has come a long way over the recent years with the advancements in the area of language…
Undoubtedly Natural Language Processing (NLP) has come a long way over the recent years with the advancements in the area of language modelling and ever-increasing computational efforts put in. This has enabled many capabilities and tasks related to text processing, leading to several high-impact applications. This is a comprehensive list of different tasks and applications possible with current NLP techniques. Here we go…
1. Information retrieval
Finds documents of text that satisfies an information need from within large collections
2. Named entity recognition
Seeks to locate and classify entities into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
3. Relation extraction
Extracts semantic relationships from the text, which usually occur between two or more entities
4. Text classification/Document Classification
Assigns a text/document to one or more classes or categories.
5. Document Ranking
6. Annotation
7. Topic modelling
Discovers the abstract “topics” that occur in a collection of documents
8. Keyword Extraction
9. Machine translation
10. Parts of speech tagging
Process of marking up a word in a text as corresponding to a particular part of speech
11. Semantic Role Labeling
Indicates the semantic role in the sentence, such as that of an agent, goal, or result
12. Word Sense Disambiguation
Identifies which sense of a word is used in a sentence
13. Grammatical Error Correction
14. Semantic textual similarity
determines how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.
15. Text summarization/Meeting Summarization
16. Reading comprehension
17. Question and answering
18. Question Generation
19. Image captioning
20. Fake News Detection/Hate Speech Detection
21. Text generation
22. Sentiment/emotion analysis
Interprets and classifies of emotions (positive, negative and neutral) with text data
23. Speech-to-text
Translation of spoken language into text
24. Text-to-speech
Converts text into spoken voice output
25. Dialogue Understanding
I hope this list is useful to whoever wanting to dive deeper into the NLP realm, either for research or industry application ideas. Good, that was a quick one.
Thanks 🙏.