Lemur Toolkit Introduction
http://net.pku.edu.cn/~wbia
彭波
pb@net.pku.edu.cn
北京大学信息科学技术学院
3/21/2011
Recap

Information Retrieval Models

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Vector Space Model
Probabilistic models
Language model
Some formulas for Sim(VSM)
Dot product
Cosine
Sim( D, Q)  D  Q 
Sim( D, Q) 

 (a * b )
i
i
ai 2 *
i
Dice

bi 2
 (a * b )
t
Sim( D, Q) 
 a  b
 (a * b )
Sim( D, Q) 
 a   b   (a * b )
2
i
D
θ
i
Q
t1
i
2
i
2
2
i
i
i
Jaccard
t3
(ai * bi )
i

i
i
i
i
i
2
2
i
i
i
i
i
i
i
3
BM25 (Okapi system) – Robertson et al.
Consider tf, qtf, document length
(k1 +1)tfi (k3 +1)qtfi
avdl - dl
Score(D,Q) = å ci
+ k2 | Q |
K + tfi k3 + qtfi
avdl + dl
ti ÎQ
dl
K = k1 ((1- b) + b
)
avdl - dl
TF factors
Doc. length
normalization
k1, k2, k3, b: parameters
 qtf: query term frequency
 dl: document length
 avdl: average document length

4
Standard Probabilistic IR
Information
need
P(R | Q, d)
matching
query
d1
d2
…
dn
document collection
5
IR based on Language Model (LM)
Information
need
P(Q | M d )
generation
query
M d1
d1
M d2
d2
A query generation process



For an information need, imagine an ideal
document
Imagine what words could appear in that
document
Formulate a query using those words
M dn
…
…

dn
document collection
6
Language Modeling for IR
Estimate a multinomial
probability distribution
from the text
Smooth the distribution
with one estimated from
the entire collection
P(w|D) = (1-) P(w|D)+  P(w|C)
Query Likelihood
?
P(Q|D) =  P(q|D)

Estimate probability that document generated the
query terms
Kullback-Leibler Divergence

Estimate models for document and query and
compare
?
=
KL(Q|D) =  P(w|Q) log(P(w|Q) / P(w|D))
Question



Among the three classic information retrieval
model, which one is your best choice in designing
your retrieval system?
How can you tune the model parameters to
achieve optimized performance?
When you have a new idea on retrieval problem,
how can you prove it?
A Brief History of IR
Slides from Prof. Ray Larson
University of California, Berkeley
School of Information
http://courses.sims.berkeley.edu/i240/s11/
Experimental IR systems










Probabilistic indexing – Maron and Kuhns, 1960
SMART – Gerard Salton at Cornell – Vector space
model, 1970’s
SIRE at Syracuse
I3R – Croft
Cheshire I (1990)
TREC – 1992
Inquery
Cheshire II (1994)
MG (1995?)
Lemur (2000?)
IS 240 – Spring 2011
Historical Milestones in IR Research

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
1958 Statistical Language Properties (Luhn)
1960 Probabilistic Indexing (Maron & Kuhns)
1961 Term association and clustering (Doyle)
1965 Vector Space Model (Salton)
1968 Query expansion (Roccio, Salton)
1972 Statistical Weighting (Sparck-Jones)
1975 2-Poisson Model (Harter, Bookstein,
Swanson)
1976 Relevance Weighting (Robertson, SparckJones)
1980 Fuzzy sets (Bookstein)
1981 Probability without training (Croft)
IS 240 – Spring 2011
Historical Milestones in IR Research (cont.)

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
1983 Linear Regression (Fox)
1983 Probabilistic Dependence (Salton, Yu)
1985 Generalized Vector Space Model (Wong,
Rhagavan)
1987 Fuzzy logic and RUBRIC/TOPIC (Tong, et
al.)
1990 Latent Semantic Indexing (Dumais,
Deerwester)
1991 Polynomial & Logistic Regression (Cooper,
Gey, Fuhr)
1992 TREC (Harman)
1992 Inference networks (Turtle, Croft)
1994 Neural networks (Kwok)
1998 Language Models (Ponte, Croft)
IS 240 – Spring 2011
Information Retrieval





Research
Boolean model, statistics
of language (1950’s)
Vector space model,
probablistic indexing,
relevance feedback
(1960’s)
Probabilistic querying
(1970’s)
Fuzzy set/logic, evidential
reasoning (1980’s)
Regression, neural nets,
inference networks, latent
semantic indexing, TREC
(1990’s)
– Historical View
Industry





DIALOG, Lexus-Nexus,
STAIRS (Boolean based)
Information industry
(O($B))
Verity TOPIC (fuzzy logic)
Internet search engines
(O($100B?)) (vector
space, probabilistic)
IS 240 – Spring 2011
Research Systems Software



INQUERY (Croft)
OKAPI (Robertson)
PRISE (Harman)




SMART (Buckley)
MG (Witten, Moffat)
CHESHIRE (Larson)




http://potomac.ncsl.nist.gov/prise
http://cheshire.berkeley.edu
LEMUR toolkit
Lucene
Others
IS 240 – Spring 2011
Lemur Project
Some slides from
Don Metzler, Paul Ogilvie & Trevor Strohman
Zoology 101



Lemurs are primates
found only in
Madagascar
50 species (17 are
endangered)
Ring-tailed lemurs

lemur catta
Zoology 101



The indri is the largest
type of lemur
When first spotted the
natives yelled “Indri!
Indri!”
Malagasy for
"Look! Over there!"
About The Lemur Project


The Lemur Project was started in 2000 by the Center for
Intelligent Information Retrieval (CIIR) at the University
of Massachusetts, Amherst, and the Language
Technologies Institute (LTI) at Carnegie Mellon
University. Over the years, a large number of UMass and
CMU students and staff have contributed to the project.
The project's first product was the Lemur Toolkit, a
collection of software tools and search engines designed
to support research on using statistical language models
for information retrieval tasks. Later the project added the
Indri search engine for large-scale search, the Lemur
Query Log Toolbar for capture of user interaction data,
and the ClueWeb09 dataset for research on web search.
Installation


http://www.lemurproject.org
Linux, OS/X:





Extract software/lemur-4.12.tar.gz
./configure --prefix=/install/path
./make
./make install
Windows


Run software/lemur-4.12-install.exe
Documentation in windoc/index.html
Installation



Use Lemur-4.12 instead~
JAVA Runtime(JDK 6) need for evaluation tool.
Environment Variable : PATH


Linux: modify ~/.bash_profile
Windows: MyComputer/Properties…
Indexing



Document Preparation
Indexing Parameters
Time and Space Requirements
Two Index Formats
KeyFile
 Term Positions
 Metadata
 Offline Incremental
 InQuery Query
Language
Indri
 Term Positions
 Metadata
 Fields / Annotations
 Online Incremental
 InQuery and Indri
Query Languages
Indexing – Document Preparation
Document Formats:





The Lemur Toolkit can inherently deal with several
different document format types without any
modification:
 HTML
TREC Text
 XML
TREC Web
 PDF
Plain Text
 Mbox
Microsoft Word(*)
Microsoft PowerPoint(*)
(*) Note: Microsoft Word and Microsoft PowerPoint can only be indexed on a
Windows-based machine, and Office must be installed.
Indexing – Document Preparation

1.
2.
If your documents are not in a format that the
Lemur Toolkit can inherently process:
If necessary, extract the text from the document.
Wrap the plaintext in TREC-style wrappers:
<DOC>
<DOCNO>document_id</DOCNO>
<TEXT>
Index this document text.
</TEXT>
</DOC>
– or –
For more advanced users, write your own parser to
extend the Lemur Toolkit.
Indexing - Parameters

Basic usage to build index:


IndriBuildIndex <parameter_file>
Parameter file includes options for





Where to find your data files
Where to place the index
How much memory to use
Stopword, stemming, fields
Many other parameters.
Indexing – Parameters

Standard parameter file specification an XML
document:
<parameters>
<option></option>
<option></option>
…
<option></option>
</parameters>
Indexing – Parameters


where to find your source files and what type to
expect
BuildIndex



<dataFiles>
name of file containing list of datafiles to index.
IndriBuildIndex
<parameters>
<corpus>
<path>/path/to/source/files</path>
<class>trectext</class>
</corpus>
</parameters>
Indexing - Parameters

The <index> parameter tells IndriBuildIndex where to
create or incrementally add to the index
 If index does not exist, it will create a new one
 If index already exists, it will append new documents
into the index.
<parameters>
<index>/path/to/the/index</index>
</parameters>
Indexing - Parameters

<memory> - used to define a “soft-limit” of the
amount of memory the indexer should use before
flushing its buffers to disk.

Use K for kilobytes, M for megabytes, and G for
gigabytes.
<parameters>
<memory>256M</memory>
</parameters>
Indexing - Parameters


Stopwords defined within
<stopwords>filename</stopwords>
IndriBuildIndex
<parameters>
<stopper>
<word>first_word</word>
<word>next_word</word>
…
<word>final_word</word>
</stopper>
</parameters>
Indexing – Parameters

Term stemming can be used while indexing as
well via the <stemmer> tag.


Specify the stemmer type via the <name> tag within.
Stemmers included with the Lemur Toolkit include the
Krovetz Stemmer and the Porter Stemmer.
<parameters>
<stemmer>
<name>krovetz</name>
</stemmer>
</parameters>
Retrieval



Parameters
Query Formatting
Interpreting Results
Retrieval - Parameters

Basic usage for retrieval:


IndriRunQuery/RetEval <parameter_file>
Parameter file includes options for





Where to find the index
The query or queries
How much memory to use
Formatting options
Many other parameters.
Retrieval - Parameters

The <index> parameter tells
IndriRunQuery/RetEval where to find the
repository.
<parameters>
<index>/path/to/the/index</index>
</parameters>
Retrieval - Parameters

The <query> parameter
specifies a query

plain text or using the
Indri query language
<parameters>
<query>
<number>1</number>
<text>this is the
first query</text>
</query>
<query>
<number>2</number>
<text>another query
to run</text>
</query>
</parameters>

Query file format
<DOC>
<DOCNO> 1 </DOCNO>
What articles exist which
deal with TSS (Time
Sharing System),
anoperating system for IBM
computers?
</DOC>
<DOC>
<DOCNO> 2 </DOCNO>
I am interested in articles
written either by Prieve or
Udo PoochPrieve, B.Pooch,
U.
</DOC>
Retrieval – Query Formatting

TREC-style topics are not directly able to be
processed via IndriRunQuery/RetEval.

Format the queries accordingly:
 Format by hand
 Write a script to extract the fields (可爱的Python~)
Retrieval – Parameters

To specify a maximum number of results to
return, use the <count> tag:
<parameters>
<count>50</count>
</parameters>
Retrieval - Parameters

Result formatting options:

IndriRunQuery/RetEval has built in formatting
specifications for TREC and INEX retrieval tasks
Retrieval – Parameters

TREC – Formatting directives:


<runID>: a string specifying the id for a query run,
used in TREC scorable output.
<trecFormat>: true to produce TREC scorable
output, otherwise use false (default).
<parameters>
<runID>runName</runID>
<trecFormat>true</trecFormat>
</parameters>
Outputting INEX Result Format

Must be wrapped in <inex> tags





<participant-id>: specifies the participant-id attribute used
in submissions.
<task>: specifies the task attribute (default CO.Thorough).
<query>: specifies the query attribute (default automatic).
<topic-part>: specifies the topic-part attribute (default T).
<description>: specifies the contents of the description tag.
<parameters>
<inex>
<participant-id>LEMUR001</participant-id>
</inex>
</parameters>
Retrieval - Evaluation

To use trec_eval:

format IndriRunQuery results with appropriate
trec_eval formatting directives in the parameter file:



<runID>runName</runID>
<trecFormat>true</trecFormat>
Resulting output will be in standard TREC format
ready for evaluation:
<queryID> Q0 <DocID> <rank> <score> <runID>
150 Q0 AP890101-0001 1 -4.83646 runName
150 Q0 AP890101-0015 2 -7.06236 runName
Use RetEval for TF.IDF

First run ParseToFile to convert doc formatted queries
into queries
<parameters>
<docFormat>web</docFormat>
<outputFile>filename</outputFile>
<stemmer>stemmername</stemmer>
<stopwords>stopwordfile</stopwords>
</parameters>


ParseToFile paramfile queryfile
http://www.lemurproject.org/lemur/parsing.html#parseto
file
Use RetEval for TF.IDF

Then run RetEval
<parameters>
<index>index</index>
<retModel>0</retModel>
// 0 for TF-IDF, 1 for Okapi,
// 2 for KL-divergence,
// 5 for cosine similarity
<textQuery>querie filename</textQuery>
<resultCount>1000</resultCount>
<resultFile>tfidf.res</resultFile>
</parameters>


RetEval paramfile
http://www.lemurproject.org/lemur/retrieval.html#RetEva
l
Evluate Results

TREC qrels

Ground Truth: judge by human assessors.
Ireval tool

java -jar
“D:\Program
Files\Lemur\Lemur
4.12\bin\ireval.jar”re
sult qrels >pr.result
Use Lemur API &
Make Extension to Lemur
Task

When you have a new idea on retrieval problem,
how can you prove it?
Introducing the API

Lemur “Classic” API




Many objects, highly customizable
May want to use this when you want to change how
the system works
Support for clustering, distributed IR, summarization
Indri API



Two main objects
Best for integrating search into larger applications
Supports Indri query language, XML retrieval, “live”
incremental indexing, and parallel retrieval
Lemur “Classic” API




Primarily useful for retrieval operations
Most indexing work in the toolkit has moved to
the Indri API
Indri indexes can be used with Lemur “Classic”
retrieval applications
Extensive documentation and tutorials on the
website (more are coming)
Lemur Index Browsing


The Lemur API gives access to the index data
(e.g. inverted lists, collection statistics)
IndexManager::openIndex



Returns a pointer to an index object
Detects what kind of index you wish to open, and
returns the appropriate kind of index class
Index::




docInfoList (inverted list),
termInfoList (document vector),
termCount,
documentCount
Lemur: DocInfoList
Index::docInfoList( int termID )
Returns an iterator to the inverted list for termID.
The list contains all documents that contain
termID, including the positions where termID
occurs.
Lemur: TermInfoList
Index::termInfoList( int docID )
Returns an iterator to the direct list for docID.
The list contains term numbers for every term
contained in document docID, and the number
of times each word occurs.
(use termInfoListSeq to get word positions)
Lemur Index Browsing
Index::termCount
termCount() : Total number of terms indexed
termCount( int id ) : Total number of occurrences of term number id.
Index::documentCount
docCount() : Number of documents indexed
docCount( int id ) : Number of documents that contain term number
id.
Lemur Index Browsing
Index::term
term( char* s ) : convert term string to a number
term( int id ) : convert term number to a string
Index::document
document( char* s ) : convert doc string to a number
document( int id ) : convert doc number to a string
Lemur Index Browsing
Index::docLength( int docID )
The length, in number of terms, of document number
docID.
Index::docLengthAvg
Average indexed document length
Index::termCountUnique
Size of the index vocabulary
Browsing Posting List
Lemur Retrieval
Class Name
Description
TFIDFRetMethod
BM25
SimpleKLRetMethod
KL-Divergence
InQueryRetMethod
Simplified InQuery
CosSimRetMethod
Cosine
CORIRetMethod
CORI
OkapiRetMethod
Okapi
IndriRetMethod
Indri (wraps QueryEnvironment)
Lemur Retrieval

RetMethodManager::runQuery









query: text of the query
index: pointer to a Lemur index
modeltype: “cos”, “kl”, “indri”, etc.
stopfile: filename of your stopword list
stemtype: stemmer
datadir: not currently used
func: only used for Arabic stemmer
model->scoreCollection()
RetMethodManager :: createModel
Lemur Retrieval Method

lemur::api::RetrievalMethod Class Reference
TextQueryMethod

A text query retrieval method is determined by
specifying the following elements:




A method to compute the query representation
A method to compute the doc representation
The scoring function
A method to update the query representation based on
a set of (relevant) documents
TextQueryMethod Scoring Function


Given a query q =(q1,q2,...,qN) and a document
d=(d1,d2,...,dN), where q1,...,qN and d1,...,dN are
terms:
s(q,d) = g(w(q1,d1,q,d) + ... + w(qN,dN,q,d),q,d)


function w gives the weight of each matched term
function g makes it possible to perform any further
transformation of the sum of the weight of all matched
terms based on the "summary" information of a query
or a document (e.g., document length).
TextQueryMethod Scoring Function

ScoreFunction::matchedTermWeight


compute the score contribution of a matched term
ScoreFunction:: adjustedScore

score adjustment (e.g., appropriate length
normalization)
Lemur: Other tasks



Clustering: ClusterDB
Distributed IR: DistMergeMethod
Language models: UnigramLM,
DirichletUnigramLM, etc.
More Stories about Indri
Why Can’t We All Get Along?
Structured Data and Information
Retrieval
Prof. Bruce Croft
Center for Intelligent Information Retrieval (CIIR)
University of Massachusetts Amherst
Similarities and Differences

Common interest in providing efficient access to
information on a very large scale




indexing and optimization key topics
Until recently, concern about effectiveness
(accuracy) of access was domain of IR
Focus on structured vs. unstructured data is
historically true but less relevant today
Statistical inference and ranking are central to IR,
becoming more important in DB
Similarities and Differences

IR systems have focused on providing access to
information rather than answers





e.g. Web search
evaluation typically based on topical relevance and
user relevance rather than correctness (except QA)
IR works with multiple databases but not
multiple relations
IR query languages more like calculus than
algebra
Integrity, security, concurrency are central for
DB, less so in IR
What is the Goal?

An IR system with extended capability for
structured data




i.e. extend IR model to include combination of
evidence from structured and unstructured
components of complex objects (documents)
backend database system used to store objects (cf.
“one hand clapping”)
many applications look like this (e.g. desktop search,
web shopping)
users seem to prefer this approach (simple queries or
forms and ranking)
Combination of Evidence

Example:



Where was George Washington born?
Returns a ranked list of sentences containing the
phrase George Washington, the term born, and a
snippet of text tagged as a PLACE named entity
“Structured Query” .vs. TextQuery
Indri – A Candidate IR System


Indri is a separate, downloadable component of the
Lemur Toolkit
Influences

INQUERY [Callan, et. al. ’92]



Lemur [http://www.lemurproject.org]


Language modeling (LM) toolkit
Lucene [http://jakarta.apache.org/lucene/docs/index.html]



Inference network framework
Structured Query language
Popular off the shelf Java-based IR system
Based on heuristic retrieval models
Designed for new retrieval environments

i.e. GALE, CALO, AQUAINT, Web retrieval, and XML retrieval
Zoology 101



The indri is the largest
type of lemur
When first spotted the
natives yelled “Indri!
Indri!”
Malagasy for "Look! Over
there!"
Design Goals

Off the shelf (Windows, *NIX, Mac platforms)



Robust retrieval model


Inference net + language modeling [Metzler and Croft ’04]
Powerful query language



Simple to set up and use
Fully functional API w/ language wrappers for Java, etc…
Designed to be simple to use, yet support complex information
needs
Provides “adaptable, customizable scoring”
Scalable



Highly efficient code
Distributed retrieval
Incremental update
Model

Based on original inference network retrieval framework
[Turtle and Croft ’91]


Casts retrieval as inference in simple graphical model
Extensions made to original model


Incorporation of probabilities based on language modeling rather
than tf.idf
Multiple language models allowed in the network (one per
indexed context)
Model
Model hyperparameters (observed)
Document node (observed)
α,βbody
D
α,βh1
α,βtitle
Context language models
θtitle
r1
…
θbody
rN
Representation nodes
(terms, phrases, etc…)
r1
…
q1
Information need node
(belief node)
θh1
rN
r1
…
rN
q2
I
Belief nodes
(#combine, #not, #max)
Model
α,βbody
D
α,βh1
α,βtitle
θtitle
r1
…
θbody
rN
r1
…
q1
θh1
rN
r1
q2
I
…
rN
P( r | θ )

Probability of observing a term, phrase,
or feature given a context language
model


Assume r ~ Bernoulli( θ )


ri nodes are binary
“Model B” – [Metzler, Lavrenko, Croft ’04]
Nearly any model may be used here

tf.idf-based estimates (INQUERY)

Mixture models
Model
α,βbo
D
dy
α,βtitle
θtitle
r1
…
α,βh1
θbody
rN
r1
…
q1
θh1
rN
r1
q2
I
…
rN
P( θ | α, β, D )


Prior over context language model determined
by α, β
Assume P( θ | α, β ) ~ Beta( α, β )




Bernoulli’s conjugate prior
αr = μP( r | C ) + 1
βr = μP( ¬ r | C ) + 1
μ is a free parameter
P(ri |  ,  , D)   P(ri |  ) P( |  ,  , D) 

tfr , D  P(r | C )
| D | 
Model
α,βbo
D
dy
α,βtitle
θtitle
r1
…
α,βh1
θbody
rN
r1
…
q1
θh1
rN
r1
q2
I
…
rN
P( q | r ) and P( I | r )


Belief nodes are created dynamically based
on query
Belief node estimates are derived from
standard link matrices




Combine evidence from parents in various ways
Allows fast inference by making marginalization
computationally tractable
Information need node is simply a belief
node that combines all network evidence into
a single value
Documents are ranked according to P( I | α,
β, D)
Belief Inference
p1
p2
q
……
Pn
Example: #AND
P(Q=true|a,b)
A
B
0
false
false
0
false
true
0
true
false
1
true
true
A
B
Q
P#and (Q  true)   P(Q  true | A  a, B  b) P( A  a) P( B  b)
a ,b
 P(t | f , f )(1  p A )(1  p B )  P(t | f , t )(1  p A ) pB  P(t | t , f ) p A (1  pB )  P(t | t , t ) p A pB
 0(1  p A )(1  p B )  0(1  p A ) pB  0 p A (1  pB )  1 p A pB
 p A pB
Other Belief Operators
Document Representation
<html>
<head>
<title>Department
Descriptions</title>
</head>
<body>
The following list describes …
<h1>Agriculture</h1> …
<h1>Chemistry</h1> …
<h1>Computer Science</h1> …
<h1>Electrical Engineering</h1> …
…
<h1>Zoology</h1>
</body>
</html>
<title>
context
<title>department
descriptions</title>
<title>
extents
<body>
context
<body>the following
list describes …
<h1>agriculture</h1
> … </body>
<body>
extents
<h1>
context
<h1>agriculture</h1
>
<h1>chemistry</h1>
…
<h1>zoology</h1>
<h1>
extents
.
.
.
1. department
descriptions
1. the following
list describes
<h1>agricultur
e
</h1> …
1. agriculture
2. chemistry
…
36. zoology
Terms
Type
Example
Matches
Stemmed term
dog
All occurrences of dog
(and its stems)
Surface term
“dogs”
Exact occurrences of dogs
(without stemming)
Term group (synonym
group)
<”dogs” canine>
All occurrences of dogs
(without stemming) or
canine (and its stems)
POS qualified term
<”dogs”
canine>.NNS
Same as previous, except
matches must also be
tagged with the NNS POS
tag
Proximity
Type
#odN(e1 … em) or
#N(e1 … em)
Example
#od5(dog cat) or
#5(dog cat)
Matches
#uwN(e1 … em)
#uw5(dog cat)
All occurrences of dog and
cat that appear in any
order within a window of 5
words
#phrase(e1 … em)
#phrase(#1(willy
wonka)
#uw3(chocolate
factory))
#syntax:np(fresh
powder)
System dependent
implementation (defaults
to #odm)
#syntax:xx(e1 …
em)
All occurrences of dog and
cat appearing ordered
within a window of 5
words
System dependent
implementation
Context Restriction
Example
Matches
dog.title
All occurrences of dog appearing in the title
context
dog.title,paragraph
All occurrences of dog appearing in both a title
and paragraph contexts (may not be possible)
<dog.title
dog.paragraph>
#5(dog cat).head
All occurrences of dog appearing in either a title
context or a paragraph context
All matching windows contained within a head
context
Context Evaluation
Example
Evaluated
dog.(title)
The term dog evaluated using the title context as the
document
dog.(title,
paragraph)
The term dog evaluated using the concatenation of
the title and paragraph contexts as the document
dog.figure(paragra
ph)
The term dog restricted to figure tags within the
paragraph context.
Belief Operators
INQUERY
#sum / #and
#wsum*
#or
#not
#max
INDRI
#combine
#weight
#or
#not
#max
* #wsum is still available in INDRI, but should be used with
discretion
Extent Retrieval
Example
Evaluated
#combine[section](dog canine) Evaluates #combine(dog canine) for
each extent associated with the section
context
#combine[title, section](dog
Same as previous, except is evaluated for
canine)
each extent associated with either the
title context or the section context
#sum(#sum[section](dog))
Returns a single score that is the #sum of
the scores returned from #sum(dog)
evaluated for each section extent
#max(#sum[section](dog))
Same as previous, except returns the
maximum score
Ad Hoc Retrieval

Flat documents


Query likelihood retrieval:
q1 … qN ≡ #combine( q1 … qN)
SGML/XML documents


Can either retrieve documents or extents
Context restrictions and context evaluations allow
exploitation of document structure
Web Search


Homepage / known-item finding
Use mixture model of several document
representations [Ogilvie and Callan ’03]
P( w | , )  body P( w |  body )  inlinkP( w |  inlink )  λtitleP( w |  title)

Example query: Yahoo!
#combine( #wsum(
0.2 yahoo.(body)
0.5 yahoo.(inlink)
0.3 yahoo.(title) ) )
Example Indri Web Query
#weight(
0.1 #weight(
1.0 #prior(pagerank) 0.75 #prior(inlinks) )
1.0 #weight(
0.9 #combine(
#wsum( 1 stellwagen.(inlink)
1 stellwagen.(title)
3 stellwagen.(mainbody)
1 stellwagen.(heading) )
#wsum( 1 bank.(inlink)
1 bank.(title)
3 bank.(mainbody)
1 bank.(heading) ) )
0.1 #combine(
#wsum( 1 #uw8( stellwagen bank ).(inlink)
1 #uw8( stellwagen bank ).(title)
3 #uw8( stellwagen bank ).(mainbody)
1 #uw8( stellwagen bank ).(heading)
) ) ) )
Question Answering


More expressive passage- and sentence-level
retrieval
Example:


Where was George Washington born?
#combine[sentence]( #1( george washington )
born #any:place)
Returns a ranked list of sentences containing the
phrase George Washington, the term born, and a
snippet of text tagged as a PLACE named entity
Indri Examples

Paragraphs from news feed articles published
between 1991 and 2000 that mention a person,
a monetary amount, and the company InfoCom
#filreq(#band( NewsFeed.doctype
#date:between(1991 2000) )
#combine[paragraph]( #any:person
#any:money InfoCom ) )
Getting Help

http://www.lemurproject.org


http://www.lemurproject.org/phorum


Central website, tutorials, documentation, news
Discussion board, developers read and respond to
questions
README file in the code distribution
Paul Ogilvie
Trevor Strohman
Don Metzler
Thank You!
Q&A