Research update

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Research update Language modeling,n-grams and
search engines

• David Johnson
• UTas September 2005

Supervisors Dr Vishv Malhotra and Dr Peter
Vamplew
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Overview

• AISAT 2004
• SWIRL 2004
• Language modeling
• A New Information Retrieval Tool
• Questions

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AISAT 2004

• AISAT 2004 - The 2nd International Conference on
  Artificial Intelligence in Science and Technology
• November 2004, UTAS, Hobart
• Hosted by The School of Engineering

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AISAT 2004

• Johnson DG, Malhotra VM, Vamplew PW, Patro S,
  Refining Search Queries from Examples Using
  Boolean Expressions and Latent Semantic Analysis
• Extension of prior research (Vishv M. Malhotra,
  Sunanda Patro, David Johnson Synthesize Web
  Queries Search the Web by Examples. ICEIS 2005.)
  the contribution of this paper was
  investigating the application of LSA to improve
  the refined queries
• This is only a brief overview the full paper is
  available o from the UTAS Eprints Repository

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AISAT 2004

• Consider the problems facing a web searcher
  looking for information in an unfamiliar domain
• Lack of knowledge of domain specific terms,
  jargon and concepts
• Difficulty rating the importance of newly
  discovered terms in targeting relevant
  information
• Lack of understanding about how extra terms and
  changing the structure of the query will affect
  the search results

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AISAT 2004

• This often leads to problems with the resulting
  query
• Poor recall most of the relevant documents are
  not located
• Poor precision many of the retrieved documents
  are not relevant
• Frustration often results
• MSN-Harris Survey (August 2004) reports there is
  a significant minority -- 29 percent of search
  engine users -- who only sometimes or rarely
  find what they want (http//www.microsoft.com/pre
  sspass/press/2004/aug04/08-02searchpollpr.mspx)

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AISAT 2004

• However it is usually relatively easy for a
  searcher to classify some example documents (from
  an initial search) as relevant or not
• The text from these documents can then be
  analyzed to build a better query one that will
  select more relevant and less irrelevant
  documents

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AISAT 2004
Resubmit to Web Search Engine
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AISAT 2004

• The new query is initially built in conjunctive
  normal form (CNF)
• Original query (a OR b OR ) AND (a OR c OR )
  AND
• Each maxterm is chosen to select all documents in
  set relevant and reject as many of the
  documents from irrelevant as possible
• The CNF expression (i.e. the conjunction of all
  maxterms) is chosen to reject all documents from
  irrelevant
• In order to minimise the size of the CNF
  expression, terms with high selective potential
  must be used
• In some cases further optimization was required
  for instance (at the time) Google would only
  accept queries of up to 10 keywords in length

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AISAT 2004
The potential of a candidate term, t, in a
partially constructed maxterm is calculated as
TR relevant documents not yet selected TIR
irrelevant docs still selected by conjunction of
prior maxterms TRt new relevant documents
selected by term t TIRt irrelevant documents
from TIR selected by term t
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AISAT 2004

• The Potential(t) function enabled the formulation
  of effective queries, but they were often
  counterintuitive, e.g. searching for information
  related to the Sun (as a star)
• The synthesised query was
• sun AND (solar AND (recent OR (field AND tour))
• OR (lower AND home) OR (million AND core))
• This was felt to be at least partially due to
  overtraining on the relatively small sets of
  example documents but could we improve the
  generated queries?

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AISAT 2004

• Latent Semantic Analysis (LSA) was investigated
  as a technique with the potential to help in
  selecting more meaningful terms
• It is A theory and method for extracting and
  representing the contextual-usage meaning of
  words by statistical computations applied to a
  large corpus of text (Landauer and Dumais, 1997)
• It uses no humanly constructed dictionaries or
  knowledge bases, yet has been shown to overlap
  with human scores on some language-based judgment
  tasks

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AISAT 2004

• LSA can help overcome the problems of polysemy
  (same word different meaning) and synonymy
  (different word same meaning) by associating
  words with abstract concepts via its analysis of
  word usage patterns
• It can be very computationally intensive
  (requiring the singular value decomposition (SVD)
  of a large matrix), but for the small collections
  of relevant / irrelevant documents we are
  analysing it can be performed quickly (lt 1 second
  CPU time)

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AISAT 2004

• The term weighting was adjusted to account for
  the LSA weighting as follows
• Weight(t) Potential(t) ((Normalised LSA
  weight in set relevant) (Normalised LSA
  weight in set irrelevant))
• During the experimentation that lead to the
  development of this weighting scheme, we noted
  that not allowing the Potential(t) values
  sufficient weight resulted in very long queries
• Note The Potential(t) value is recalculated
  after each term is selected, but the LSA values
  are based on the entire document sets

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AISAT 2004

• Results - Example 1
• Naïve query elephants searching for
  information suitable for a high school project
• 7 out of first 20 documents, and around 30 of
  all initial documents from the naïve query were
  judged relevant
• The initial number of relevant documents was
  quite high, reflecting the reasonably broad
  nature of the information need

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AISAT 2004

• Results Example 1
• Refined query no LSA
• elephant AND (forests OR lived OR calves OR
  poachers OR maximus OR threaten OR electric OR
  tel OR kudu)
• Refined query with LSA
• elephant AND (climate OR habitats OR grasslands
  OR quantities OR africana OR threaten OR insects
  OR electric OR kudu)
• LSA did help in the selection of intuitive
  words, although not as much as hoped it avoided
  lived and tel (abbreviation for telephone
  no), although it still selected quantities

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AISAT 2004

• Results - Example 2
• Naïve query mushrooms searching for
  information on growth, lifecycle and structure of
  mushrooms
• NOT interested in recipes, magic mushrooms, or
  identifying wild edible mushrooms
• 3 of first 20 documents and 13 of initial
  documents relevant

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AISAT 2004

• Results - Example 2
• Refined query no LSA
• mushrooms AND (cylindrical OR ancestors OR hyphae
  OR cellulose OR issued OR hydrogen OR developing
  OR putting)
• Refined query LSA
• mushrooms AND (ascospores OR hyphae OR itis OR
  peroxide OR discharge OR developing OR pulled OR
  jean)
• In this example the LSA query has identified some
  additional technical terms, although still
  including the quite general term pulled. Note
  that the selection of the term jean was due to
  Jean being the first name of several authors
  mentioned in relevant documents
• ITIS Integrated Taxonomic Information System

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AISAT 2004

• Conclusions
• The refined search queries show significant
  improvement over original naïve queries, without
  the need for detailed domain knowledge
• LSA did assist in the formulation of more
  meaningful Boolean web queries
• LSA did not significantly improve retrieval
  performance compared to the original query
  enhancement algorithm

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AISAT 2004

• Further Comments (Not in original
  presentation/paper)
• A criticism of this approach is that the
  requirement for the user to review and classify
  several documents requires quite a bit of effort
  were we really helping the user or not?
• While it is our assertion that the review /
  classification process required is not very time
  consuming, we have tried to address this
  criticism by taking a different approach in
  current work

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SWIRL 2004

• Strategic Workshop on Information Retrieval in
  Lorne, December 2004
• Organized by Justin Zobel from RMIT and Alistair
  Moffat from the University of Melbourne
• Funded by the Australian Academy of Technological
  Sciences and Engineering under their Frontiers
  of Science and Technology Missions and Workshops
  program
• 17 International and 13 Australian researchers
  plus 8 research students
• Included researchers from industry and government
  as well as academia (Microsoft, NIST, CSIRO)
• ( NIST National Institute of Standards and
  Technology, a US Government research organization)

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SWIRL 2004

• It was a discussion-based residential workshop
• The aim for the workshop was to try and define
  what we know (and also don't know) about
  Information Retrieval, examining past work to
  identify fundamental contributions, challenges,
  and turning points and then to examine possible
  future research directions, including possible
  joint projects. That is, our goal was to pause
  for a few minutes, reflect on the lessons of past
  research, and reconsider what questions are
  important and which research might lead to
  genuine advances.
• From the SWIRL 2004 web site -
  http//www.cs.mu.oz.au/alistair/swirl2004/

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SWIRL 2004

• Attendees included researchers responsible for
  many of the key innovations in web searching and
  the implementation of effective and efficient
  information retrieval systems.

John Tait (University of Sunderland) Dave Harper
(The Robert Gordon University, Scotland)
Alistair Moffat (University of Melbourne) Justin
Zobel (RMIT)
Andrew Turpin (University of Melbourne) Bruce
Croft (University of Massachusetts, Amherst) Ross
Wilkinson (CSIRO)
Bill Hersh, M.D. (Oregon Health Science
University)
Robert Dale (Macquarie University) Kal Järvelin
(University of Tampere, Finland)
Jamie Callan (Carnegie Mellon University ) David
Harper (CSIRO)
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SWIRL 2004

• Lorne Beach during the site visit (Winter)

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SWIRL 2004

• Lorne Beach when we arrived (Summer)

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SWIRL 2004

• Program - Day 1
• Travel from Melbourne to Lorne (group bus)
• Keynote Presentation
• The IR Landscape Bruce Croft
• Presentation
• Adventures in IR evaluation Ellen Voorhees
• Group Discussion
• IR where are we, how did we get here, and where
  might we go? Mark Sanderson
• Workshop Dinner

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SWIRL 2004

• Program - Day 2
• Group Discussion
• What motivates research Justin Zobel / Alistair
  Moffat
• Small Group Discussion
• Challenges in information retrieval and language
  modeling David Harper, Phil Vines and David
  Johnson Challenges in Contextual Retrieval,
  Challenges in Metasearch, Challenges in Cross
  Language Information Retrieval (CLIR)
• Group Discussion
• Important papers for new research students to be
  aware of David Hawking
• Small Group Project
• How to spend 1M per year for five years Ross
  Wilkinson

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SWIRL 2004

• Program - Day 3
• Presentations from Group Projects
• Mark Sanderson, Ellen Voorhees, David Johnson, et
  al Experimental Educational Search Engine
  Targeting information needs for upper primary to
  grade 10
• Group Discussion
• Where to now with SIGIR? Jamie Callan
• Group Discussion
• Writing the ideal SIGIR 2005 paper Susan Dumais
• Return to Melbourne
• Via Port Campbell National Park and Great Ocean
  Road

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SWIRL 2004

• Summary
• An excellent opportunity to meet and talk to many
  respected IR researchers
• Provided guidance on the current state of the
  art and ideas for the future direction of my
  research
• Provided many useful contacts

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Language Modeling - Introduction

• The seminal paper for language modeling in modern
  IR A Language Modeling Approach to Information
  Retrieval Jay Ponte and Bruce Croft 1998
• Like LSA, it can deal with the problems of
  polysemy (same word different meaning) and
  synonymy (different word same meaning)
• Unlike LSA, it has a firm theoretical
  underpinning
• Less computationally demanding than LSA
  particularly important for large document
  collections

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Language Modeling - Introduction

• Documents and queries are considered to be
  generated stochastically using their underlying
  language model
• The document in a collection that is considered
  most relevant to a given query is the one with
  the highest probability of generating the query
  from its language model
• The assumption of word order independence is
  usually made to make the model mathematically
  tractable, although bi-grams, tri-grams, etc. can
  be accommodated

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Language Modeling - Introduction

• A language model of a document is a function that
  for any given word calculates the probability of
  that word appearing in the document
• The probability of a phrase (or query) is
  calculated by multiplying together the individual
  word probabilities (applying the word order
  independence assumption)
• All we need is a method to estimate the language
  models of the documents!

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Language Modeling Model Estimation

• Obviously the document itself is the primary data
  source, but
• If a word, w1,doesnt appear in a document,
  should we have P(w1Md) 0? (in other words
  meaning that it is impossible for language model
  Md to generate word w1)
• If word w2 appears 5 times in a 1,000 word
  document, is P(w2Md) 0.005 really a reasonable
  estimate?
• It is important to smooth the language model to
  overcome problems caused by lack of training data

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Language Modeling Model Estimation

• There are a number of smoothing methods in use,
  the basic premise being that some of the
  probability mass in the model should be taken
  from the observed data to be used as an estimate
  for unseen words
• They take into account Zipfs law nature of
  word usage (a few words used many times, many
  words used infrequently) to improve estimates
  for instance the Good-Turing algorithm
• Corpus or general English word usage data may
  also be used to augment the document data, but
  care has to be taken. The Ponte-Croft paper
  addresses this problem by calculating a
  risk-adjustment factor, based on the difference
  between corpus and document word usage

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Language Modeling Model Estimation

• Zipfs law

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Language Modeling Results

• A basic implementation of the Ponte-Croft method
  proved very useful in quickly locating relevant
  documents in small local document collections
• It is planned to implement an improved version as
  part of the information retrieval tool that is
  currently under development discussed next

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A New Information Retrieval Tool

• Goals
• Assist the user to get required information from
  an ad-hoc web search more quickly and effectively
• Data driven to allow quick access to key parts of
  retrieved documents
• In some cases provide answers to questions
  directly without the user needing to view
  documents
• Assist in refining web queries
• Use language modeling techniques locally on
  retrieved documents to satisfy more complex
  information needs i.e. those not able to be
  expressed adequately in a web query

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A New Information Retrieval Tool

• Information Flow

Web Query
Search Engine
Links to Results
WWW

• From bi-gram List
• Question may be answered
• Jump directly to relevant parts of documents
  containing bi-grams
• Explore documents further using language modeling
• Formulate and run a refined web query
  including/rejecting selected bi-grams

Retrieved Document Text 150 docs 35 seconds 200
docs 50 seconds
User
Bi-gram List
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A New Information Retrieval Tool

• Why use bi-grams?
• Express a concept much better than a single word

Peanut
Foods/cooking Peanut butter Peanut candy Roasted
peanut Chocolate peanut Peanut brittle Peanut
cookie Peanut recipe Peanut lover Peanut
soup Peanut oil
Agriculture Peanut institute Peanut
commission Peanut grower Peanut farmer Peanut
producer Peanut plant
Commercial/Brands Peanut software Peanut
linux Peanut van Peanut inn Peanut
clothing Peanut Apparel Baby peanut Peanut
sandals Peanut ties Mr peanut
Medical Peanut allergy
Other Peanut gallery
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A New Information Retrieval Tool

• Why use bi-grams?
• Can be used directly as a web search term by
  using quotes
• Easily combined into well targeted searches using
  OR and - operators
• Reasonably easy to extract meaningful bi-grams
  from document text using simple rules
• Tri-grams or higher n-grams tend to occur too
  infrequently
• Also, bi-grams seem to be somewhat neglected in
  current IR research possibly because of the
  sparse data problem a corpus with a
  vocabulary of 50,000 words has the potential for
  2,500,000,000 bi-grams causing difficulty in
  language modeling and many other IR techniques

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A New Information Retrieval Tool

• Simple bi-gram extraction rules
• Ignore a bi-gram if it contains a stop word (a
  word that doesnt convey much meaning, for
  instance - a, an, and, of, the, etc. without
  this step the most frequent bi-grams are usually
  of the, in the, to the and so on)
• If bi-grams are found that are the same except
  for plurals (e.g. african elephant and african
  elephants) only present the most common form to
  the user
• Sort bi-grams by descending occurrence count
  within descending document occurrence count
• Alternative method
• Part of speech filtering almost all interesting
  bi-grams are of the form Adjective Noun or
  Noun Noun
• It isnt always possible to determine the part of
  speech exactly (for example unknown words, words
  with multiple possible parts of speech), but we
  can certainly reject many bi-grams that could
  never be of the required form

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A New Information Retrieval Tool

• Example of an automatically generated bi-gram
  list
• 150 documents from simple Google search
  Elephants

• elephant jokes
• baby elephant
• elephant elephas
• 70 years
• indian elephant
• elephant society
• large ears
• wild elephants
• land mammal
• white elephant
• forest elephant
• baby elephants
• elephants eat
• ivory tusks
• family elephantidae
• species survival

• 13 feet
• largest living
• largest land
• elephant seal
• natural history
• young elephants
• female elephants
• elephant conservation
• long time
• elephant range
• give birth
• incisor teeth
• 20 years
• ivory trade
• 60 years
• blood vessels

1. african elephant
2. elephant man
3. loxodonta africana
4. elephant loxodonta
5. asian elephant
6. south africa
7. privacy policy
8. united states
9. years old
10. elephas maximus
11. 22 months
12. national park
13. endangered species
14. elephants live
15. forest elephants
16. small objects

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A New Information Retrieval Tool

• From browsing the bi-gram list the user
• Gets an idea of the topic areas in the retrieved
  data
• Can jump directly to relevant parts of retrieved
  documents
• Can mark bi-grams to include/exclude from a new
  web search
• Can mark portions of retrieved documents as
  relevant/irrelevant to use in a more targeted
  local search using language modeling (this allows
  drilling down to discover information on concepts
  that are difficult to express as a web query)
• The user can also use natural language and/or
  keyword queries using language modeling to assist
  in examining local data (i.e. the document text
  downloaded as part of the retrieval process)

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A New Information Retrieval Tool

• Example of a new web query formulated by marking
  relevant/irrelevant bi-grams
• elephant ("african elephant" OR "asian elephants"
  OR "loxodonta africana")
• -"elephant man" -"elephant seal" -"elephant
  jokes" -"white elephant
• Using the criterion of our test information need
  (web pages with suitable information for a high
  school project on the land mammal elephant), 48
  of the first 50 pages returned by the search were
  judged to be relevant.
• The web search also indicated there were about
  228,000 pages matching our query, so we were
  still getting a very wide coverage

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A New Information Retrieval Tool

• Are the generated bi-gram lists always useful?
• At least some of the information in the retrieved
  documents must be relevant for interesting
  bi-grams to be generated
• On the other hand, if the bi-grams are all way
  off track, the user knows immediately to rethink
  the initial search, rather than reviewing many
  irrelevant documents
• Initial testing with 27 different one word Google
  searches (150 documents retrieved for each
  search) generated useful bi-gram lists in 21
  cases
• By using a two word search (e.g. angle geometry
  instead of angle, cobra snake instead of
  cobra), useful bi-gram lists were obtained for
  five of the remaining six cases

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A New Information Retrieval Tool

• Future work
• Continue development and refinement of this
  information retrieval tool
• Identify examples of information needs that are
  difficult to satisfy by direct web search alone
• User survey how does the tool perform in
  practice? How could it be improved?

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Questions/Comments?