Character-String Auto-Completion System

An Character-String Auto-Completion System is a string generation system that can solve a auto-completion task by implementing Auto-Completion Algorithm.

• Context:
  • It can range from being a Syntactic Autocompletion System, to being a Semantic Autocompletion System, to being a Contextual Autocompletion System.
  • It can range from an Online Autocompletion System to being an Offline Autocompletion System.
  • It can range from being a Language Model Based Autocompletion System to being a Structured Prediction Based Autocompletion System, to being a Sequence-Based Autocompletion System.
  • …
• Example(s):
  • a Text Auto-Completion System such as:
    • a Academic Writing Auto-Completion System,
    • a Typing Assistant Auto-Completion System;
  • a Software Code Auto-Completion System such as:
    • Intelligent Code Completion System (Bruch et al., 2009),
    • Qt Creator Auto-Completion System;
  • a Query Auto-Completion System;
  • a WikiText Auto-Completion System;
  • a Hand-Drawn Symbol Auto-Completion System (e.g. Costagliola et al., 2013);
  • an Error-Tolerant Auto-Completion System (Chaudhuri & Kaushik, 2009).
  • …
• Counter-Example(s):
  • an Artificial Grammar Learning System,
  • an Autoreplace System,
  • an Auto-Encoding System,
  • a Spelling Error Correction System,
  • a Parsing System,
  • a Text Wikification System.
• See: Human-Computer Interaction, Editing System, Language Model, Natural Language Inference System, Natural Language Processing System.

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References

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Autocomplete Retrieved:2019-8-13.
  • Autocomplete, or word completion, is a feature in which an application predicts the rest of a word a user is typing. In graphical user interfaces, users can typically press the tab key to accept a suggestion or the down arrow key to accept one of several.

    Autocomplete speeds up human-computer interactions when it correctly predicts the word a user intends to enter after only a few characters have been typed into a text input field. It works best in domains with a limited number of possible words (such as in command line interpreters), when some words are much more common (such as when addressing an e-mail), or writing structured and predictable text (as in source code editors).

    Many autocomplete algorithms learn new words after the user has written them a few times, and can suggest alternatives based on the learned habits of the individual user.

2013

• (Costagliola et al., 2013) ⇒ Gennaro Costagliola, Mattia De Rosa, and Vittorio Fuccella. (2013). "Investigating Human Performance in Hand-Drawn Symbol Autocompletion". In: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics. ISBN:978-1-4799-0652-9 doi:10.1109/SMC.2013.54

2009a

• (Chaudhuri & Kaushik, 2009) ⇒ Surajit Chaudhuri, and Raghav Kaushik. (2009). “Extending Autocompletion to Tolerate Errors.” In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data. ISBN:978-1-60558-551-2 doi:10.1145/1559845.1559919

2009b

• (Bruch et al., 2009) ⇒ Marcel Bruch, Martin Monperrus, and Mira Mezini. (2009). “Learning from Examples to Improve Code Completion Systems.” In: Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering. ISBN:978-1-60558-001-2 doi:10.1145/1595696.1595728

2006

• (Hyvonen & Makela, 2006) ⇒ (2006, September). "Semantic autocompletion". In Asian Semantic Web Conference (pp. 739-751). Springer, Berlin, Heidelberg. DOI:10.1007/11836025_72

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