1. (Some issues in) Text Ranking

2. Recall General Framework Crawl – Use XML structure – Follow links to get new pages Retrieve relevant documents – Today Rank – PageRank, HITS – Rank Aggregation

3. Relevant documents Usually: relevant with respect to a keyword, set of keywords, logical expression.. Closely related to ranking – “How” relevant is it can be considered another measure Usually done as a separate step – Recall the Online vs. offline issue.. But some techniques are reusable

4. Defining Relevant Documents Common strategy: treat text documents as “bag of words” (BOW) – Denote BOW(D) for a document D – Bag rather than set (i.e. multiplicity is kept) – Words are typically stemmed Reduced to root form – Loses structure, but simplifies life Simple definition: – A document D is relevant to a keyword W if W is in BOW(D)

5. Cont. Simple variant – The level of relevance of D to W is the multiplicity of W in BOW(D) – Problem: Bias towards long documents – So divide by the document length |BOW(D)| – This is called term frequency (TF)

6. A different angle Given a document D, what are the “most important” words in D? Clearly high term frequency should be considered Rank terms according to TF?

7. Ranking according to TF A 2022 Is 1023 He 350. Liverpool 25 Beatles 12

8. IDF Observation: if w is rare in the documents set, but appears many times in a document D, then w is “important” for D IDF(w) = log(|Docs| / |Docs’|) – Docs is the set of all documents in the corpus, Docs’ is the subset of documents that contain w TFIDF(D,W)=TF(W,D)*IDF(W) – “Correlation” of D and W

9. Inverted Index For every term we keep a list of all documents in which it appears The list is sorted by TFIDF scores Scores are also kept Given a keyword it is then easy to give the top-k

10. Ranking Now assume that these documents are web pages How do we return the most relevant? How do we combine with other rankings? (e.g. PR?) How do we answer boolean queries? – X1 AND (X2 OR X3)

11. Rank Aggregation To combine TFIDF, PageRank.. To combine TFIDF with respect to different keywords

12. Part-of-Speech Tagging So far we have considered documents only as bags-of-words Computationally efficient, easy to program, BUT We lost the structure that may be very important: – E.g. perhaps we are interested (more) in documents for which W is often the sentence subject? Part-of-speech tagging – Useful for ranking – For machine translation – Word-Sense Disambiguation – …

13. Part-of-Speech Tagging Tag this word. This word is a tag. He dogs like a flea The can is in the fridge The sailor dogs me every day

14. A Learning Problem Training set: tagged corpus – Most famous is the Brown Corpus with about 1M words – The goal is to learn a model from the training set, and then perform tagging of untagged text – Performance tested on a test-set

15. Simple Algorithm Assign to each word its most popular tag in the training set Problem: Ignores context Dogs, tag will always be tagged as a noun… Can will be tagged as a verb Still, achieves around 80% correctness for real-life test-sets – Goes up to as high as 90% when combined with some simple rules

16. (HMM) Hidden Markov Model Model: sentences are generated by a probabilistic process In particular, a Markov Chain whose states correspond to Parts-of-Speech Transitions are probabilistic In each state a word is outputted – The output word is again chosen probabilistically based on the state

17. HMM HMM is: – A set of N states – A set of M symbols (words) – A matrix NXN of transition probabilities Ptrans – A vector of size N of initial state probabilities Pstart – A matrix NXM of emissions probabilities Pout “Hidden” because we see only the outputs, not the sequence of states traversed

18. Example

19. 3 Fundamental Problems 1) Compute the probability of a given observation Sequence (=sentence) 2) Given an observation sequence, find the most likely hidden state sequence This is tagging 3) Given a training set find the model that would make the observations most likely

20. Tagging Find the most likely sequence of states that led to an observed output sequence Problem: exponentially many possible sequences!

21. Viterbi Algorithm Dynamic Programming V t,k is the probability of the most probable state sequence – Generating the first t + 1 observations (X0,..Xt) – And terminating at state k

22. Viterbi Algorithm Dynamic Programming V t,k is the probability of the most probable state sequence – Generating the first t + 1 observations (X0,..Xt) – And terminating at state k V 0,k = Pstart(k)*Pout(k,X 0 ) V t,k = Pout(k,X t )*max{V t-1k’ *Ptrans(k’,k)}

23. Finding the path Note that we are interested in the most likely path, not only in its probability So we need to keep track at each point of the argmax – Combine them to form a sequence What about top-k?

24. Complexity O(T*|S|^2) Where T is the sequence (=sentence) length, |S| is the number of states (= number of possible tags)

25. Computing the probability of a sequence Forward probabilities: α t (k) is the probability of seeing the sequence X 1 …X t and terminating at state k Backward probabilities: β t (k) is the probability of seeing the sequence X t+1 …X n given that the Markov process is at state k at time t.

26. Computing the probabilities Forward algorithm α 0 (k)= Pstart(k)*Pout(k,X 0 ) α t (k)= Pout(k,X t )*Σ k’ {α t-1k’ *Ptrans(k’,k)} P(O 1,… On )= Σ k α n (k) Backward algorithm β t (k) = P(O t+1 …O n | state at time t is k) β t (k) = Σ k’ {Ptrans(k,k’)* Pout(k’,X t+1 )* β t+1 (k’)} β n (k) = 1 for all k P(O)= Σ k β 0 (k)* Pstart(k)

27. Learning the HMM probabilities Expectation-Maximization Algorithm 1.Start with initial probabilities 2.Compute Eij the expected number of transitions from i to j while generating a sequence, for each i,j (see next) 3.Set the probability of transition from i to j to be Eij/ (Σ k Eik) 4. Similarly for omission probability 5. Repeat 2-4 using the new model, until convergence

28. Estimating the expectancies By sampling – Re-run a random a execution of the model 100 times – Count transitions By analysis – Use Bayes rule on the formula for sequence probability – Called the Forward-backward algorithm

29. Accuracy Tested experimentally Exceeds 96% for the Brown corpus – Trained on half and tested on the other half Compare with the 80-90% by the trivial algorithm The hard cases are few but are very hard..

30. NLTK http://www.nltk.org/ Natrual Language ToolKit Open source python modules for NLP tasks – Including stemming, POS tagging and much more