Ire presentation team56
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Medical Named Entity
Recognition on Twitter Data
Group:56Chinmay Bapna Juhi Tandon Kalpish Singhal201302182 201225032 201505513
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● Medical Entity Recognition is a crucial step towards efficient medical texts analysis. The task of a Medical Name Entity Recognizer is two fold. -
■ Identification of entity boundaries in the sentences
■ Entity categorization.
Our objective is to extend medical entity recognition for tweets.
● Medical Entities
a. Diseases
b. Drugs
c. Symptoms
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1. Each tweet is stored in a separate file along with a corresponding annotation
file with manually labelled entities
2. Next comes the word-context feature, along with the term, a 2-term context
window, preceding and succeeding the concerned term were added as
another feature
3. Metamap tagging: For each tweet, metamap tags are extracted
4. POS tagging: For each tweet, part of speech tags are extracted
5. Orthographic tagging: For each term, orthographic features are identified and
labelled
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❖ The dataset provided to us consists of 85% of ‘None’ entity tags.
❖ Thus with any random designation of tags too we will have 80% or more accuracy
❖ This conveys that accuracy is not the correct metric for correct evaluation of feature models.
❖ Since this metric isn’t good-enough to tell us significance of one feature model over another,
❖ we tried following metrics:
1. Precision,
2. Recall
3. F-Score
❖ In this project we experimented with different feature sets and analyzed their significance based on intuition and
prior knowledge .
❖ Thereafter we evaluated their efficiency in Medical-NER.
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