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Index

Biword Index

Phrase Index

Positional Index

Inverted Index

Positional Index vs Inverted index

Inverse Document Frequency

Detail

Features of an inverted index.

Key concept of index

  1. Traversing a directory of documents
  2. Reading the document and extracting and tokenizing all of the text
  3. Computing counts of documents and terms
  4. Building a dictionary of unique terms that exist within the corpus
  5. Writing out to a disk file, a sorted term dictionary

Inverted Index Construction

Model

  • Select doc,
  • tokenize,
  • add to dictionary,
  • count occurrences,
  • sort for searching.

Report

documents, terms, unique terms

alt text

  1. Number of documents processed

  2. Total number of terms parsed from all documents

  3. Total number of unique terms found and added to the index

documents

here

index

here

Map-reduce

Implementation of hadoop Search technology Simple db keeping track of dictionary

Problem:

  • Both memory and time consuming at internet scale
  • Potentially billions of documents
  • Need more efficient solution e.g. : document of (see bob run see spot throw)
  • count term: in order to reduce by combine and summerize
see 2
bob 1
spot 1
throw 1
  • key every term then count
  • Then merge each output toghether
  • Then sort them

Alogrithm

  • Take all output, combine and reduce them

  • Map (key=url, val=contents):

    → For each word w in contents, emit (w, "1")

    → Reduce(key=word, values=uniq_counts):

    • Sum all "1"s in value list
    • Emit result "(word, sum)"

DFS : data file system

Automatic parallel execution in mapReduce MapReduce in Hadoop

Dictionary Data Structures

  • To store the term vocabulary,
  • document frequency,
  • pointers to each postings list

alt text

Example: An array of struct as a naïve dictionary

options for dictionary structure:

  • Hashtables
Each vocabulary term is hashed to an interger
Pros: faster lookup than tree
Cons: no easy way to find minor variants; no prefix search; expensive operation of rehasing.
  • Trees
Simplest: Binary tree
More usual: B-trees
Pros: solves the prefix problem
Cons: 
+ Slower O(logM)
+ Rebalancing binary tree is expensive, but B-trees mitigate the rebalancing problem.

e.g. binary tree in sort order

alt text

Always slipping into half for searching.

Every node always has two outputs

e.g. B-Tree

alt text

every node has a number of children

Any particular level may have two or more outcomes. Level multiple options.

Tolerant Retrieval

Wild-card queries

e.g. *mon: to find words ending with mon

Query processing

  • Find everything that maches with term e.g Find word related to home: home*
  • This result in the execution of many Boolean and queries e.g. home* AND house*
  • Handle wildcard:
B-trees handle * at the end of query
Permuterm index: handle * at the middle

e.g. finding hello → hello$, ello$h, lo$hel, o$hell execute different kind of search.

Cons: increase number of term in the dictionary

Permuterm query processing

  • rotate query wild-card to the right
  • use B-tree lookup
  • Permuterm problem : = quadruples lexicon size

Pros: use a lot more space for indexes

Bigram (k-gram) indexes

Finds term based on a query consisting of k-grams

Index Compression

Key Terms

  1. Dictionary compression
Aims to fit in the memory with an at least large portion of dictionaries. 

The dictionary as a string that sorts the vocabulary lexicographically and stores it in an array of fixed-width entries or blocked storage by grouping terms into the string into blocks of size k and keeping a term pointer for the first term of each blog
  1. Rule of 30
The 30 most common words account for 30% of the tokens in the written text. 

Thus, the lossy method could be used for compression without losing its effectiveness in encoding the data.
  1. Lossy Compression
Amount of data is lost during this process
  1. Lossless Compression
No data is lost during compression
  1. Heap’s law
 To estimate the number of unique terms in a collection based upon constants k and b and the number of terms or tokens (T) parsed from all documents.

M = kTß

  in which T is the number of tokens in a collection, k and ß are parameters values

alt text

  1. Zipf’s law

cfi = cik

as one of the types of the power law

  1. Power law

  2. Front Coding

  3. Variable Byte Encoding

  4. Nibble

  5. Unary Code

A string of n 1s followed by a 0
  1. Encoding
Two type of methods such as bytewise and bitwise. 

As such variable byte encoding uses the integral number of byte to encode a gap instead of docID.

  1. Entropy

  2. δ Codes

Asymptotically optimal for entropy H(P) → ∞

Web crawler

What is involved in creating a web crawler?

Purpose: to get the information that is available on a website Process: As described by Manning (2009, chapter 15)

  • Begin with URL(s) constituting a seed set
  • Picking a URL from this seed set then fetches the web page at that URL
  • Parse the fetched page to extract links and texts
  • Feed the extracted texts to a text indexer
  • Add the extracted links to URL frontier
  • Corresponding pages-URL(s) are fetched by the crawler
  • URL frontier contains seed set
  • Corresponding URL are deleted from URL frontier when pages are fetched.
  • Entire process as traversing the web graph

alt text

Figure : The basic crawler architecture extracted from Figure 20.1 (Manning, 2009, chapter 19)

Static vs dynamic web content

Static: the same prebuilt content each time the page is loaded Dynamic: content is changed and can be generated on the fly.

Query

Query Type

Boolean Retrieval vs Wildcard Queries vs Phrase Queries

Improve of Computing Score and Rank

References:

Manning, C.D., Raghaven, P., & Schütze, H. (2009). An Introduction to Information Retrieval (Online ed.). Cambridge, MA: Cambridge University Press. Available at http://nlp.stanford.edu/IR-book/information-retrieval-book.html