List of Chatterbots - Processes of NLP - Applications

Applications

• Automated essay scoring (AES) – the use of specialized computer programs to assign grades to essays written in an educational setting. It is a method of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades—for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification.
• Automatic summarization – process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. Often used to provide summaries of text of a known type, such as articles in the financial section of a newspaper.
  • Types
    • Keyphrase extraction –
    • Document summarization –
      • Multi-document summarization –
  • Methods
    • Extraction-based summarization –
    • Abstraction-based summarization –
    • Maximum entropy-based summarization –
    • Aided summarization –
      • Human aided machine summarization (HAMS) –
      • Machine aided human summarization (MAHS) –
• Coreference resolution – in order to derive the correct interpretation of text, or even to estimate the relative importance of various mentioned subjects, pronouns and other referring expressions need to be connected to the right individuals or objects. Given a sentence or larger chunk of text, coreference resolution determines which words ("mentions") refer to which objects ("entities") included in the text.
  • Anaphora resolution – concerned with matching up pronouns with the nouns or names that they refer to. For example, in a sentence such as "He entered John's house through the front door", "the front door" is a referring expression and the bridging relationship to be identified is the fact that the door being referred to is the front door of John's house (rather than of some other structure that might also be referred to).
• Dialog system –
• Grammar checking –
• Machine translation (MT) – aims to 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.
  • Classical approach of machine translation – rules-based machine translation.
  • Computer-assisted translation –
    • Interactive machine translation –
    • Translation memory – database that stores so-called "segments", which can be sentences, paragraphs or sentence-like units (headings, titles or elements in a list) that have previously been translated, in order to aid human translators.
  • Example-based machine translation –
  • Knowledge-based machine translation – another name for rule-based machine translation
  • Rule-based machine translation –
• Natural language generation – task of converting information from computer databases into readable human language.
• Natural language programming – interpreting and compiling instructions communicated in natural language into computer instructions (machine code).
• Natural language search –
• Natural language understanding – converts 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 languages concepts. Introduction and creation of language metamodel and ontology are efficient however empirical solutions. An explicit formalization of natural languages semantics without confusions with implicit assumptions such as closed world assumption (CWA) vs. open world assumption, or subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics formalization.
• Optical character recognition (OCR) – given an image representing printed text, determine the corresponding text.
• Question answering – given a human-language question, determine its answer. Typical questions have a specific right answer (such as "What is the capital of Canada?"), but sometimes open-ended questions are also considered (such as "What is the meaning of life?").
  • Open domain question answering –
• Sentiment analysis – extracts 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.
• 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" (see above). In natural speech there are hardly any pauses between successive words, and thus speech segmentation is a necessary subtask of speech recognition (see below). Note also that in most spoken languages, the sounds representing successive letters blend into each other in a process termed coarticulation, so the conversion of the analog signal to discrete characters can be a very difficult process.
• Text-proofing –
• Text simplification –
• Text-to-speech –