Modern Information Retrieval Chapter 7: Text Operations Ricardo Baeza-Yates Berthier Ribeiro-Neto

Document Preprocessing Lexical analysis of the text Elimination of stopwords Stemming Selection of index terms Construction of term categorization structures

Lexical Analysis of the Text Word separators  space  digits  hyphens  punctuation marks  the case of the letters

Elimination of Stopwords A list of stopwords  words that are too frequent among the documents  articles, prepositions, conjunctions, etc. Can reduce the size of the indexing structure considerably Problem  Search for “ to be or not to be ” ?

Stemming Example  connect, connected, connecting, connection, connections  effectiveness --> effective --> effect  picnicking --> picnic  king -\-> k Removing strategies  affix removal: intuitive, simple  table lookup  successor variety  n-gram

Index Terms Selection Motivation  A sentence is usually composed of nouns, pronouns, articles, verbs, adjectives, adverbs, and connectives.  Most of the semantics is carried by the noun words. Identification of noun groups  A noun group is a set of nouns whose syntactic distance in the text does not exceed a predefined threshold

Thesauri Peter Roget, 1988 Example cowardly adj. Ignobly lacking in courage: cowardly turncoats Syns: chicken (slang), chicken-hearted, craven, dastardly, faint-hearted, gutless, lily-livered, pusillanimous, unmanly, yellow (slang), yellow-bellied (slang). A controlled vocabulary for the indexing and searching

The Purpose of a Thesaurus To provide a standard vocabulary for indexing and searching To assist users with locating terms for proper query formulation To provide classified hierarchies that allow the broadening and narrowing of the current query request

Thesaurus Term Relationships BT: broader NT: narrower RT: non-hierarchical, but related

Term Selection Automatic Text Processing by G. Salton, Chap 9, Addison-Wesley, 1989.

Automatic Indexing Indexing:  assign identifiers (index terms) to text documents. Identifiers:  single-term vs. term phrase  controlled vs. uncontrolled vocabularies instruction manuals, terminological schedules, …  objective vs. nonobjective text identifiers cataloging rules define, e.g., author names, publisher names, dates of publications, …

Two Issues Issue 1: indexing exhaustivity  exhaustive: assign a large number of terms  nonexhaustive Issue 2: term specificity  broad terms (generic) cannot distinguish relevant from nonrelevant documents  narrow terms (specific) retrieve relatively fewer documents, but most of them are relevant

Parameters of retrieval effectiveness Recall Precision Goal high recall and high precision

Nonrelevant Items Relevant Items Retrieved Part a b c d

A Joint Measure F-score   is a parameter that encode the importance of recall and procedure.  =1: equal weight  <1: precision is more important  >1: recall is more important

Choices of Recall and Precision Both recall and precision vary from 0 to 1. Particular choices of indexing and search policies have produced variations in performance ranging from 0.8 precision and 0.2 recall to 0.1 precision and 0.8 recall. In many circumstance, both the recall and the precision varying between 0.5 and 0.6 are more satisfactory for the average users.

Term-Frequency Consideration Function words  for example, "and", "or", "of", "but", …  the frequencies of these words are high in all texts Content words  words that actually relate to document content  varying frequencies in the different texts of a collect  indicate term importance for content

A Frequency-Based Indexing Method Eliminate common function words from the document texts by consulting a special dictionary, or stop list, containing a list of high frequency function words. Compute the term frequency tf ij for all remaining terms T j in each document D i, specifying the number of occurrences of T j in D i. Choose a threshold frequency T, and assign to each document D i all term T j for which tf ij > T.

Inverse Document Frequency Inverse Document Frequency (IDF) for term T j where df j (document frequency of term T j ) is the number of documents in which T j occurs.  fulfil both the recall and the precision  occur frequently in individual documents but rarely in the remainder of the collection

TFxIDF Weight w ij of a term T j in a document d i Eliminating common function words Computing the value of w ij for each term T j in each document D i Assigning to the documents of a collection all terms with sufficiently high (tf x idf) factors

Term-discrimination Value Useful index terms  Distinguish the documents of a collection from each other Document Space  Two documents are assigned very similar term sets, when the corresponding points in document configuration appear close together  When a high-frequency term without discrimination is assigned, it will increase the document space density

Original State After Assignment of good discriminator After Assignment of poor discriminator A Virtual Document Space

Good Term Assignment When a term is assigned to the documents of a collection, the few objects to which the term is assigned will be distinguished from the rest of the collection. This should increase the average distance between the objects in the collection and hence produce a document space less dense than before.

Poor Term Assignment A high frequency term is assigned that does not discriminate between the objects of a collection. Its assignment will render the document more similar. This is reflected in an increase in document space density.

Term Discrimination Value Definition dv j = Q - Q j whereQ and Q j are space densities before and after the assignments of term T j. dv j >0, T j is a good term; dv j <0, T j is a poor term.

Document Frequency Low frequency dv j =0 Medium frequency dv j >0 High frequency dv j <0 N Variations of Term-Discrimination Value with Document Frequency

TF ij x dv j w ij = tf ij x dv j compared with  : decrease steadily with increasing document frequency  dv j : increase from zero to positive as the document frequency of the term increase, decrease shapely as the document frequency becomes still larger.

Document Centroid Issue: efficiency problem N(N-1) pairwise similarities Document centroid C = (c 1, c 2, c 3,..., c t ) where w ij is the j-th term in document i. Space density

Probabilistic Term Weighting Goal Explicit distinctions between occurrences of terms in relevant and nonrelevant documents of a collection Definition Given a user query q, and the ideal answer set of the relevant documents From decision theory, the best ranking algorithm for a document D

Probabilistic Term Weighting Pr(rel), Pr(nonrel): document ’ s a priori probabilities of relevance and nonrelevance Pr(D|rel), Pr(D|nonrel): occurrence probabilities of document D in the relevant and nonrelevant document sets

Assumptions Terms occur independently in documents

Derivation Process

Given a document D=(d 1, d 2, …, d t ) Assume d i is either 0 (absent) or 1 (present). Pr(x i =1|rel) = p i Pr(x i =0|rel) = 1-p i Pr(x i =1|nonrel) = q i Pr(x i =0|nonrel) = 1-q i For a specific document D

Term Relevance Weight

Issue How to compute p j and q j ? p j = r j / R q j = (df j -r j )/(N-R)  R: the total number of relevant documents  N: the total number of documents

Estimation of Term-Relevance The occurrence probability of a term in the nonrelevant documents q j is approximated by the occurrence probability of the term in the entire document collection q j = df j / N The occurrence probabilities of the terms in the small number of relevant documents is equal by using a constant value p j = 0.5 for all j.

When N is sufficiently large, N-df j  N,  = idf j Comparison

Estimation of Term-Relevance Estimate the number of relevant documents r j in the collection that contain term T j as a function of the known document frequency tf j of the term T j. p j = r j / R q j = (df j -r j )/(N-R) R: an estimate of the total number of relevant documents in the collection.

Summary Inverse document frequency, idf j  tf ij *idf j (TFxIDF) Term discrimination value, dv j  tf ij *dv j Probabilistic term weighting tr j  tf ij *tr j Global properties of terms in a document collection