230215-nlp-common-task.md

NLP common tasks:

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• Classification

  • Text Classification: assigning a category to a sentence or document ( e.g. spam filtering).
  • Sentiment Analysis: identifying the polarity of a piece of text.

• Information Retrieval and Document Ranking

  • Sentence/document similarity: determining how similar two texts are.
  • Question Answering: the task of answering a question in natural language.

• Text-to-Text Generation

  • Machine Translation: translating from one language to another.
  • Text Generation: creating text that appears indistinguishable from human-written text.
  • Text Summarization: creating a shortened version of several documents that preserves most of their meaning.
  • Text Simplification: making a text easier to read and understand, while preserving its main ideas and approximate meaning.
  • Lexical Normalization: translating/transforming a non-standard text to a standard register.
  • Paraphrase Generation: creating an output sentence that preserves the meaning of input but includes variations in word choice and grammar.

• Knowledge bases, entities and relations

  • Relation extraction: extracting semantic relationships from a text. Extracted relationships usually occur between two or more entities and fall into specific semantic categories (e.g. lives in, sister of, etc).
  • Relation prediction: identifying a named relation between two named semantic entities.
  • Named Entity Recognition: tagging entities in text with their corresponding type, typically in BIO notation.
  • Entity Linking: recognizing and disambiguating named entities to a knowledge base (typically Wikidata).

• Topics and Keywords

  • Topic Modeling: identifying abstract “topics” underlying a collection of documents.
  • Keyword Extraction: identifying the most relevant terms to describe the subject of a document

• Chatbots

  • Intent Detection: capturing the semantics behind messages from users and assigning them to the correct label.
  • Slot Filling: aims to extract the values of certain types of attributes ( or slots, such as cities or dates) for a given entity from texts.
  • Dialog Management: managing of state and flow of conversations.

• Text Reasoning

  • Common Sense Reasoning: use of “common sense” or world knowledge to make inferences.
  • Natural Language Inference: determining whether a “hypothesis” is true ( entailment), false (contradiction), or undetermined (neutral) given a “premise”.

• Fake News and Hate Speech Detection

  • Fake News Detection: detecting and filtering out texts containing false and misleading information.
  • Stance Detection: determining an individual’s reaction to a primary actor’s claim. It is a core part of a set of approaches to fake news assessment.
  • Hate Speech Detection: detecting if a piece of text contains hate speech.

• Text-to-Data and viceversa

  • Text-to-Speech: technology that reads digital text aloud.
  • Speech-to-Text: transcribing speech to text.
  • Text-to-Image: generating photo-realistic images which are semantically consistent with the text descriptions.
  • Data-to-Text: producing text from non-linguistic input, such as databases of records, spreadsheets, and expert system knowledge bases.

• Text Preprocessing

  • Coreference Resolution: clustering mentions in text that refer to the same underlying real-world entities.
  • Part Of Speech (POS) tagging: tagging a word in a text with its part of speech. A part of speech is a category of words with similar grammatical properties, such as noun, verb, adjective, adverb, pronoun, preposition, conjunction, etc.
  • Word Sense Disambiguation: associating words in context with their most suitable entry in a pre-defined sense inventory (typically WordNet).
  • Grammatical Error Correction: correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors.
  • Feature Extraction: extraction of generic numerical features from text, usually embeddings.