gravitation-based model for information retrieval shuming shi, ji-rong wen, qing yu, ruihua song,...

Gravitation-Based Model for Information Retrieval

Shuming Shi, Ji-Rong Wen, Qing Yu, Ruihua Song, Wei-Ying Ma

Microsoft Research AsiaSIGIR 2005

INTRODUCTION Most IR models fail to satisfy some basic int

uitive heuristic constraints

IR models commonly lack intuitive physical interpretations

View information retrieval from the perspective of physics (theory of gravitation)

INTRODUCTION Derived a ranking formulas which satisfy al

l heuristic constraints

The BM25 term weighting function can be easily derived from our basic model

A more reasonable approach for structured document retrieval

Newton’s Theory of Gravitation

masses m1, m2 distance d universal gravitation constant G

Okapi’s BM25 formula

Origin representation of w(t) has the potential “negative IDF” so we will use ln((N+1)/df(t))Amati and Rijsbergen propose a nonparametric term weighting functions. They claim that the BM25 function with some special parameters (k1=1.2,b=0.75; or k1=2, b=0.75) can be approximated numerically by one of their generated functions

Structured Document Retrieval The most commonly used approach f

or structured document retrieval may be score/rank linear combination

Robertson combines term frequencies instead of field scores

GRAVITATION-BASED MODEL A term is viewed as a physical object

composed of some amount of particles A particle has two attributes

Type: Determined by the term it composes Mass

Term Object A term is composed of particles with a

specified structure (or shape). Sphere ideal cylinder - a cylinder whose radius is

small enough and can be viewed as a line There are three attributes related to a

term type, mass, and diameter

Term Object

Sphere-shape terms Ideal-cylinder-shape terms

Document Object There are two categories of term objects i

n each document explicit (or seen) terms - all the terms occurre

d in the content of the document implicit (or hidden) terms - not occurred in th

e document, used to represent the hidden meaning of the document

We use |D| and |H(D)| to represent the number of explicit and implicit terms

Document Object Mass of a document is the total masses of all its seen a

nd hidden terms

The diameter of a document is defined as the sum of the diameters of all its terms

a query is modeled as an object composed of its terms (no hidden terms are assumed for simplicity)

Basic Model : Discrete Version

Basic Model : continuous version

Mass and Diameter Estimation

Assumption 1 For any two terms, their mass ratio in any documen

t is equal to the ratio of their average masses in the whole collection

Assumption 2 The average mass of a term depends on and only o

n the inverse document frequency

Mass and Diameter Estimation Assumption 3

The average global importance of all terms in a document is constant

Now we try to estimate the mass of a term in a document

Mass and Diameter Estimation Assumption 4

The number of hidden terms in a document relies only on the statistic information of the whole collection

Model Analysis

Continuous model

Model Analysis Discrete model

Relationship with the BM25 formula

In previous section, if m(D) and di(D) are constant, then it is easy to prove that Continuous model is equivalent to simplified BM25

Field Functions

Structured Document Retrieval Because masses of terms in field 1 are

larger than those of field 2, field 1’s terms are nearer to the query

Analysis and Comparison Score combination

A single query term over many fields could get unreasonably higher score than matches all the query terms in a few fields

TF combination Fields weights F1=5 , F2=1

score of d3 contains t in its high-weight field, and enough term in its low-weight field should be larger than that of d4

EXPERIMENTS Experiments on two corpora and seven query s

ets used from TREC 2000 to 2004

Term Weighting Experiments

VSM-Piv : Pivoted normalization VSM formula LM-Dir :Language model formula with Dirichlet prior smoothing GBM-Inv :the GBM formula derived by gravitational field function

1/x GBM-Exp :the GBM formula derived by e^ -x GBM-Std :equivalent to the BM25 formula

Field Structure Experiments The scores for the baseline, ScoreComb, and Fr

eqComb are all generated by the BM25(b=0.75)

CONCLUSION AND FUTURE WORK In this paper, a gravitation-based IR m

odel (GBM) was proposed GBM model can not only give a physic

al interpretation of BM25, but also derive new effective ranking formulas

The derived term weighting functions satisfies all the heuristic constraints

CONCLUSION AND FUTURE WORK A novel approach for structured docu

ment retrieval Use static document ranks e.g. PageR

ank to derive m(D) Study the relationship and possible c

ombination between our model and existing models