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

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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