Natural Language Processing

Natural language processing (NLP) is a sub-field of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.

  • Sentence breaking (also known as sentence boundary disambiguation)

Given a chunk of text, find the sentence boundaries. Sentence boundaries are often marked by periods or other punctuation marks, but these same characters can serve other purposes (e.g. marking abbreviations).

  • Stemming

The process of reducing inflected (or sometimes derived) words to their root form. (e.g. "close" will be the root for "closed", "closing", "close", "closer" etc.).

  • Word segmentation

Separate a chunk of continuous text into separate words. For a language like English, this is fairly trivial, since words are usually separated by spaces. However, some written languages like Chinese, Japanese and Thai do not mark word boundaries in such a fashion, and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language. Sometimes this process is also used in cases like Bag of Words (BOW) creation in data mining.

  • Lexical semantics

What is the computational meaning of individual words in context?

  • Distributional semantics

How can we learn semantic representations from data?

  • Machine translation

Automatically translate text from one human language to another. This is one of the most difficult problems, and is a member of a class of problems colloquially termed "AI-complete", i.e. requiring all of the different types of knowledge that humans possess (grammar, semantics, facts about the real world, etc.) in order to solve properly.

  • Named entity recognition (NER)

Given a stream of text, determine which items in the text map to proper names, such as people or places, and what the type of each such name is (e.g. person, location, organization). Although capitalization can aid in recognizing named entities in languages such as English, this information cannot aid in determining the type of named entity, and in any case is often inaccurate or insufficient.

  • Natural language generation

Convert information from computer databases or semantic intents into readable human language.

  • Natural language understanding

Convert chunks of text into more formal representations such as first-order logic structures that are easier for computer programs to manipulate. Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural language concepts.

  • Optical character recognition (OCR)

Given an image representing printed text, determine the corresponding text.

  • Relationship extraction

Given a chunk of text, identify the relationships among named entities (e.g. who is married to whom).

  • Sentiment analysis (see also multi-modal sentiment analysis)

Extract subjective information usually from a set of documents, often using online reviews to determine "polarity" about specific objects. It is especially useful for identifying trends of public opinion in the social media, for the purpose of marketing.

  • Topic segmentation and recognition

Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the segment.

  • Automatic summarization

Produce a readable summary of a chunk of text. Often used to provide summaries of text of a known type, such as research papers, articles in the financial section of a newspaper.

  • Speech recognition

Given a sound clip of a person or people speaking, determine the textual representation of the speech. This is the opposite of text to speech and is one of the extremely difficult problems colloquially termed "AI-complete". In natural speech there are hardly any pauses between successive words, and thus speech segmentation is a necessary subtask of speech recognition.

  • Text-to-speech

Given a text, transform those units and produce a spoken representation. Text-to-speech can be used to aid the visually impaired.

  • Dialogue

The first published work by an artificial intelligence was published in 2018, 1 the Road, marketed as a novel, contains sixty million words.

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