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Open AccessJournal ArticleDOI

Search optimization of named entities from twitter streams

TLDR
This system contains the search optimization and functionalities for getting information about those tweets that contains grammatical errors, misspellings, non-standard abbreviations, and meaningless capitalization.
Abstract
With Enormous number of tweets, People often face difficulty to get exact information about those tweets. One of the approach followed for getting information about those tweets via Google. There is not any accuracy tool developed for search optimization and as well as getting information about those tweets. So, this system contains the search optimization and functionalities for getting information about those tweets. Another problem faced here are the tweets that contains grammatical errors, misspellings, non-standard abbreviations, and meaningless capitalization. So, these problems can be eliminated by the use of this tool. Lot of time can be saved and as well as by the use of efficient search optimization each information about those particular tweets can be obtained.

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References
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Proceedings ArticleDOI

Normalizing SMS: are Two Metaphors Better than One ?

TL;DR: This paper presents an comparative study of systems aiming at normalizing the orthography of French SMS messages, one drawing inspiration from the Machine Translation task; the other using techniques that are commonly used in automatic speech recognition devices.
Proceedings Article

NERD: A framework for evaluating named entity recognition tools in the Web of data

TL;DR: NERD enables the comparison of different popular Linked Data entity extractors which expose APIs such as AlchemyAPI, DBPedia Spotlight, Extractiv, OpenCalais and Zemanta.