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Information Retrieval Summary§1 Introduction to IR
Information retrieval (IR): finding material (usually documents) of an unstructured nature
(usually text) that satisfies an information need from within large collections (usually stored
on computers)
Information Retrieval (IR) versus Databases (DB):
IR: unstructured data, loose semantics, partial query matching, probabilistic
DB: structured data, well defined query, exact query matching, deterministic
Criteria for good search engines:
How fast does it search? Latency as function of index size
How fast does it index? Number of documents per hour
Expressiveness of query language: ability to express complex information needs
Disk space requirements
User happiness
Search speed: latency (fastest to slowest)
Main memory reference (read random byte from memory)
Compress 1K of data (compress 1000 bytes in memory)
SSD random read (read random byte from solid-state drive)
Round trip within the same datacenter (send one byte to another computer in the
same fast datacenter network and back)
Hard disk seek (read random byte from hard disk)
Send one byte from the Netherlands to California and back
§2 Indexing and Boolean Retrieval
Term-Document Incidence Matrix: matrix with on the rows the words and on the columns
the documents. This is a sparse matrix: 1 if document contains the word; 0 otherwise.
Inverted Index:
For each term (word) t we need to store a list of all documents that contain t
Identify each term by a document ID
We need variable-size lists (posting lists)
o On disk: continuous run of postings is best
o In memory: linked lists or variable-length arrays
Inverted Index Construction: tokenizer > normalization > indexer
Indexer Step 1: Token sequence: remove capitals and punctuation marks and make a
table of all words together with the corresponding document
Indexer Step 2: sort by terms (and then by doc ID)
, Indexer Step 3: split into dictionary (term) and postings (doc id); merge multiple term
entries and document frequency is added
For AND queries, we need to merge 2 postings lists, one way to do this: walk through the 2
postings simultaneously and only look at the head element, add to the result list if these 2
are the same (and then move 1 element forward), otherwise discard the smaller one by
moving forward for that list. Posting lists are sorted by doc ID
The Boolean retrieval model allows for queries that are Boolean expressions:
Each document is viewed as a set of words
AND, OR, and NOT can be used to join query terms
It is precise: every document either matches the condition or not
Query optimization: process in order of increasing frequency so start with the
smallest set (therefore we keep the doc frequency)
‘to sleep perhance to dream’
Token: instance of a sequence of characters in some particular document that are
grouped together as a useful semantic unit for processing
o = 5: to, sleep, perhance, to, dream
Type: class of all tokens of consisting of exactly the same character sequence
o = 4: to, sleep, perhance, dream
Term: a (normalized) type that is included in the IR system’s dictionary
o = 3: sleep, perhance, dream
An index for small collections (like Reuters) fit into memory, but more realistic collections
like Common Crawl or Google don’t, that should be stored on disk or SSD. The bottleneck is
sorting, this is way too slow. Therefore, we need an external sorting algorithm:
Blocked Sort-Based Indexing (BSBI): define blocks, sort each block in memory and
then write back to disk, in the end merge all blocks
It is most efficient to do an n-way merge, where we read from all (or many) block
simultaneously.
Load and write sorted blocks as buffered streams from and to disk
With proper buffer size, disk access is minimized
Distributed Indexing with MapReduce: see picture slide 37
§3 What to Index?
We want to be able to answer queries like “information retrieval” as a phrase. Such that
“there is no information about the retrieval of raw materials” is not a match. How?
Solution 1 is biword indexes: index every consecutive pair of terms as a phrase:
“Friends, Romans, Countrymen”: would create the biwords:
o friends romans
o romans countrymen
Each of these biwords is now a dictionary term
Problems of biword indexes:
, False positives in answer set (for queries with more than 2 words)
In particular for phrases with frequent words like “beer of the month”
Index blow-up due to bigger dictionary
Infeasible for more than biwords
Solution 2 is positional indexes: store in the positing for each term the position(s) in which
tokens of it appear
<term: number-of-docs-containing-term; <be: 993427;
Doc1: position1, position2, … ; 1: 3, 149;
Doc2: position1, position2, … ; 2: 17, 191, 291, 430, 434;
…> …>
Positional index requires substantially more space in memory and on disk. But it is
the standard solution due to the power and usefulness of phrase queries
Positional index is 2-4 times larger than non-positional index
Size of positional index is 35-50% of volume of original text
Only document 2 can contain “to be or not to be”
Processing a phrase query: extract inverted index entries for each distinct term: to, be, or
not. Merge their document position lists to enumerate all positions with “to be or not to be”
These 2 can also be combined, it requires 26% more space than just positional index but a
query can be executed in ¼ of the time of just a positional index.
Normalization:
How to tokenize/ normalize tokens like Den Haag, or 20/12/21?
Use compound splitting in Dutch for nouns: ziektekostenverzekering
Case folding: reduce all letters to lower-case (exception upper-case mid-sentence?)
Tokenization and normalization may depend on the language and so is intertwined
with language detection
Stop words: create a list of the most common words, such as “of” and “the”, and
remove them from the token list before the indexing step.
Lemmatization: reduce inflectional / variant forms to base form. This implies doing proper
linguistic reduction to dictionary headword form
Am, are, is, were, being -> be
Car, cars, car’s, cars’ -> car
Stemming: reduce terms to their roots before indexing, this is crude affix chopping and
language dependent
Automate, automates, automatic, automation -> automat
Porter’s algorithm is the most common for stemming English, conventions are
applied in 5 phases of reduction, and each phase has a set of rules
o *sses -> *ss
o *ational -> *ate
o *tional ->*tion
Spelling correction: used to correct documents being indexed and used to correct user
queries to retrieve right answers.
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