1. Final Presentation Tong Wang

2. 1.Automatic Article Screening in Systematic Review 2.Compression Algorithm on Document Classification

3. Automatic Article Screening Review Question: Vitamin C for preventing and treating common cold? Data set: 17 References articles. 664 Not references articles.

4. Problem Definition Input : document d classes(c1 = Reference, c2 = not a reference) Output: predicted class of d Goal: find all articles belong to c1(Reference)

5. Build Features “Bag of Words” assumption: the order of words in a document can be neglected Preprocessing: tokenization, lemma, remove stop words, remove some part of speech. Need a step: Name Entity Recognizer(NER), it labels sequences of words which are the name of things. It is implemented by linear chain Conditional Random Field(CRF)

6. Build features Vector space model Extract vocabulary over all articles. Each document can be represented by a vector, value in each dimension is the word frequency in this article N = size of vocabulary w1, w2, w3, w4… wN d1 1 0 2 0 … 0 d2 0 1 0 0 … 0

7. Naïve Bayes

8. Logistic Regression

9. Discuss Define loss matrix, give high penalty for false negative. Another way is to use Cosine distance to compute similarity between articles. Wiki def: Use other nlp probability model, like LSA, LDA

10. Compression The basic idea is the data contains patterns that occur with a certain regularity will be compressed more efficiently It is generally inexpensive

11. d(x, y) = c(x y)/(c(x) + c(y)) x: A document c(x) : size of compressed file x xy: the file obtained by concatenating x and y d(x,y) – 1/2 >= 0 X X y y xy C(x) C(y) C(xy)

12. Compression Matrix a1 a2 a3 a4…. b1 d(b1, a1) d(b1, a2) b2 d(b2, a1) d(b2, a2) b3 b4 …

13. Experiments Two groups of drug review(ADHD) articles. Two groups of machine learning articles. Each group has 15 articles Intuitively d(ADHD, ADHD) < d(ADHD, machine learning) d(machine learning, machine learning) < d(ADHD, machine learning)

15. Future work More experiments Compare cosine(x, y) and d(x, y)