| 词条 | Noisy text |
| 释义 |
Language usage over computer mediated discourses, like chats, emails and SMS texts, significantly differs from the standard form of the language. An urge towards shorter message length facilitating faster typing and the need for semantic clarity, shape the structure of this text used in such discourses. Various business analysts estimate that unstructured data constitutes around 80% of the whole enterprise data. A great proportion of this data comprises chat transcripts, emails and other informal and semi-formal internal and external communications. Usually such text is meant for human consumption, but – given the amount of data – manual processing and evaluation of those resources is not practically feasible anymore. This raises the need for robust text mining methods.[3] Techniques for noise reduction{{unreferenced section|date=December 2017}}The use of spell checkers and grammar checkers can reduce the amount of noise in typed text. Many word processors include this in the editing tool. Online, Google search includes a search term suggestion engine to guide users when they make mistakes with their queries. See also
References1. ^{{cite journal| title=Special Issue on Noisy Text Analytics|author=Knoblock, C., Lopresti, D., Roy, S., Subramaniam, L. V.|journal=International Journal on Document Analysis and Recognition|volume=10|issue=3–4|pages=127–128|year=2007|doi=10.1007/s10032-007-0058-9}} 2. ^{{cite journal|doi=10.1109/TPAMI.2005.248 |pmid=16355657|title=Noisy text categorization|year=2005|journal=IEEE Transactions on Pattern Analysis and Machine Intelligence Volume|last=Vinciarelli|first=A.|volume=27|issue=12|pages=1882–1895}} 3. ^{{cite conference|author=Subramaniam, L. V., Roy, S., Faruquie, T. A., Negi, S.|title=A survey of types of text noise and techniques to handle noisy text|conference=Third Workshop on Analytics for Noisy Unstructured Text Data (AND)|year=2009}} 1 : Coding theory |
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