Inverted Indexing for Text Retrieval

1. Inverted Indexing for Text Retrieval Chapter 4 Lin and Dyer

2. Introduction • Web search is a quintessential large-data problem. • So are any number of problems in genomics. • Google, amazon (aws) all are involved in research and discovery in this area • Web search or full text search depends on a data structure called inverted index. • Web search problem breaks down into three major components: • Gathering the web content (crawling) (like project 1) • Construction of inverted index (indexing) • Ranking the documents given a query (retrieval) (exam 1)

3. Issues with these components • Crawling and indexing have similar characteristics: resource consumption is high • Typically offline batch processing except of course on twitter model • There are many requirements for a web crawler or in general a data aggregator.. • Etiquette, bandwidth resources, multilingual, duplicate contents, frequency of changes… • How often to collect: too few may miss important updates, too often may have too much info

4. Web Crawling • Start with a “seed” URL , say wikipedia page, and start collecting the content by following the links in the seed page; the depth of traversal is also specified by the input • What are the issues? • See page 67

5. Retrieval • Retrieval is a online problem that demands stringent timings: sub-second response times. • Concurrent queries • Query latency • Load on the servers • Other circumstances: day of the day • Resource consumption can be spikey or highly variable • Resource requirement for indexing is more predictable

6. Indexes • Regular index: Document  terms • Inverted index termdocuments • Example: term1  {d1,p}, {d2, p}, {d23, p} term2  {d2, p}. {d34, p} term3  {d6, p}, {d56, p}, {d345, p} Where d is the doc id, p is the payload (example for payload: term frequency… this can be blank too)

7. Inverted Index • Inverted index consists of postings lists, one associated with each term that appears in the corpus. • <t, posting>n • <t, <docid, tf> >n • <t, <docid, tf, other info>>n • Key, value pair where the key is the term (word) and the value is the docid, followed by “payload” • Payload can be empty for simple index • Payload can be complex: provides such details as co-occurrences, additional linguistic processing, page rank of the doc, etc. • <t2, <d1, d4, d67, d89>> • <t3, <d4, d6, d7, d9, d22>> • Document numbering typically do not have semantic content but docs from the same corpus are numbered together or the numbers could be assigned based on page ranks.

8. Retrieval • Once the inverted index is developed, when a query comes in, retrieval involves fetching the appropriate docs. • The docs are ranked and top k docs are listed. • It is good to have the inverted index in memory. • If not , some queries may involve random disk access for decoding of postings. • Solution: organize the disk accesses so that random seeks are minimized.

9. Pseudo Code Pseudo code  Baseline implementation  value-key conversion pattern implementation…

10. Inverted Index: Baseline Implementation using MR • Input to the mapper consists of docid and actual content. • Each document is analyzed and broken down into terms. • Processing pipeline assuming HTML docs: • Strip HTML tags • Strip Javascript code • Tokenize using a set of delimiters • Case fold • Remove stop words (a, an the…) • Remove domain-specific stop works • Stem different forms (..ing, ..ed…, dogs – dog)

11. Baseline implementation procedure map (docid n, doc d) H  new Associative array for all terms in doc d H{t}  H{t} + 1 for all term in H emit(term t, posting <n, H{t}>)

12. Reducer for baseline implmentation procedure reducer( term t, postings[<n1, f1> <n2, f2>, …]) P  new List for all posting <a,f> in postings Append (P, <a,f>) Sort (P) // sorted by docid Emit (term t, postings P)

13. Shuffle and sort phase • Is a very large group by term of the postings • Lets look at a toy example • Fig. 4.3 some items are incorrect in the figure

14. Baseline MR for II class Mapper procedure Map(docid n; doc d) H =new AssociativeArray for all term t in doc d do H(t) H(t) + 1 for all term t in H do Emit(term t; posting (n,H[t]) class Reducer procedure Reduce(term t; postings [hn1; f1i; hn2; f2i : : :]) P = new List for all posting (t,f) in postings [(n1,f1); (n2, f2) : : :] do Append(P, (t, f)) Sort(P) Emit(term t; postings P)

15. Revised Implementation • Issue: MR does not guarantee sorting order of the values.. Only by keys • So the sort in the reducer is an expensive operation esp. if the docs cannot be held in memory. • Lets check a revised solution • (term t, posting<docid, f>) to • (term<t,docid>, tf f)

16. Inverted Index: Revised implementation • From Baseline to an improved version • Observe the sort done by the Reducer. Is there any way to push this into the MR runtime? • Instead of • (term t, posting<docid, f>) • Emit • (tuple<t, docid>, tf f) • This is our previously studied value-key conversion design pattern • This switching ensures the keys arrive in order at the reducer • Small memory foot print; less buffer space needed at the reducer • See fig.4.4

17. Modified mapper Map (docid n, doc d) H  new AssociativeArray For all terms t in doc H{t}  H{t} + 1 For all terms in H emit (tuple<t,n>, H{t})

18. Modified Reducer Initialize tprev 0 P  new PostingList method reduce (tuple <t,n>, tf[f1, ..]) if t # tprev ^ tprev # 0 { emit (term t, posting P); reset P; } P.add(<n,f>) tprev  t Close emit(term t, postings P)

19. Improved MR for II class Mapper method Map(docid n; doc d) H = new AssociativeArray for all term t in doc d do H[t] = H[t] + 1 for all term t in H do Emit(tuple <t; n>, tfH[t]) class Reducer method Initialize tprev = 0; P = new PostingsList method Reduce(tuple <t, n>; tf[f]) if t <> tprev ^ tprev <> 0; then Emit(term t; postings P) P:Reset() P:Add(<n, f>) tprev = t method Close

20. Other modifications • Partitionerand shuffle have to deliver all related <key, value> to same reducer • Custom partitioner so that all terms t go to the same reducer. • Lets go through a numerical example

21. What about retrieval? • While MR is great for indexing, it is not great for retrieval.

22. Index compression for space • Section 4.5 • (5,2), (7,3), (12,1), (49,1), (51,2)… • (5,2), (2,3), (5,1), (37,1), (2,2)…

23. Miscellaneous Stuff • How to MR Spam Filtering (Naïve Bayes solution) discussed in Ch.4 DDS? In training the model. • Write solution in the form of your main workflow configuration. • Prior is What is random probability of x occurring? Eg. What is the probability that the next person who walks into the class is a female?

24. NIH Solicitation in Big Data (2014) • .. • This opportunity targets four topic areas of high need for researchers working with biomedical Big Data, 1. Data Compression/Reduction 2. Data Provenance 3. Data Visualization 4. Data Wrangling

25. Odds Ratio Example from 4/16/2014 news article • Woods is still favored to with the U.S. Open. He and Rory McIlroy are each 10/1 favorites on online betting site, Bovada. Adam Scott has the next best odds at 12/1….. • How to interpret this? • = • = • = • Woods is also the favorite to win the Open Championship at Hoylake in July. He's 7/1 there. =