NLP Tasks

  • Information Extraction (IE)
  • Text Generation: Natural Language Generation (NLG)
  • Code generation
  • Sentence Segmentation
  • Relation Prediction: Relation Prediction is the task of recognizing a named relation between two named semantic entities.
  • Relationship extraction: Relationship extraction is the task of extracting semantic relationships from a text.
  • Semantic textual similarity: Semantic textual similarity deals with determining how similar two pieces of texts are.
  • Semantic role labeling: Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering “Who did what to whom”.
  • Shallow syntax: Shallow syntactic tasks provide an analysis of a text on the level of the syntactic structure of the text.
  • Simplification: Simplification consists of modifying the content and structure of a text in order to make it easier to read and understand, while preserving its main idea and approximating its original meaning.
  • Stance detection: Stance detection is the extraction of a subject’s reaction to a claim made by a primary actor. It is a core part of a set of approaches to fake news assessment.
  • Taxonomy Learning: Taxonomy learning is the task of hierarchically classifying concepts in an automatic manner from text corpora.
  • Text Grouping:
  • Keyphrase extraction: A classic task to extract salient phrases that best summarize a document, which essentially has two stages: candidate generation and keyphrase ranking.
  • Collocations: Collocations are two or more words that tend to appear frequently together.
  • Temporal Processing: Automatic extraction of document date based on linguistic clues,
  • Temporal Information Extraction: Temporal information extraction is the identification of chunks/tokens corresponding to temporal intervals, and the extraction and determination of the temporal relations between those.
  • Word Embedding(Word Vector):
  • Knowledge Base Population (KBP): Knowledge Base Population is the task of taking an incomplete knowledge base (e.g., Freebase, or the structured information in Wikipedia infoboxes), and a large corpus of text (e.g., Wikipedia), and completing the incomplete elements of the knowledge base.
  • Language modeling: The task of predicting the next word or character in a document
  • Natural Language Inference (NLI): Used inside or as an complimentary tool for Inference Engines to extract information(facts and relationships) from text using logical rules.
  • Summarization
    • Extraction-based summarization
    • abstraction-based summarization
  • Parsing: Extracting grammatical structure of sentences.
  • Dependency Parsing
  • Lexico-Syntatic Analysis
  • Formal Concept Analysis
  • Association Rule Mining
  • Sentiment Analysis