NER from Tweets: SRI-JU System @MSM 2013
Amitava Das, Utsab Burman, Sivaji Bandyopadhyay, Samsung, Research India · 2013
Now a day Twitter has become an interesting source of experiment for different NLP experiments like entity extractio n, user opinion analysis and more. Due to the noisy nature of user generated con tent it is hard to run standard NLP tools to obtain a better result. The t ask of named entity extraction from tweets is one of them. Traditional NER approaches on tweets do not perform well. Tweets are usually informal in nature and short (up to 140 characters). They often contain grammatical errors, misspellings, and unreliable capitalization. These unreliable linguistic feature s cause traditional methods to perform poorly on tweets. This article reports the author's participation in the Concept Extraction Challenge, Making Sense of micro posts (#MSM2013). Three different systems runs have been submitted. T he first run is the baseline, second run is with capitalization and syntactic fea ture and the last run is with dictionary features. The last run yielded than all other. The accuracy of the final run has been checked is 79.57 (precision), 71.00 (r ecall) and 74.79 (f-measure) respectively.