WMES3103 : INFORMATION RETRIEVAL TEXT OPERATIONS

INTRODUCTION Not all words in a document = significant to represent the contents/meanings of a document Some word carry more meaning than others Noun words or group of noun words = most representative of a document content Therefore, need to preprocess the text of a document in a collection to be used as index terms

Using the set of all words in a collection to index documents = too much noise for the retrieval task Reduce noise = reduce words which can be used to refer to the document Preprocessing = process of controlling the size of the vocabulary or the number of distinct words used as index terms Preprocessing will lead to an improvement in the information retrieval performance

However, some search engines on the Web omit preprocessing Every word in the document is an index term Suppose to make the retrieval task simpler and easier for the user

DOCUMENT PREPROCESSING Text operations = text transformations 5 main operations : a. Lexical analysis of the text - digits, hyphens, punctuations marks, and the case of letters b. Elimination of stop words - filter out words which are not useful in the retrieval process c. Stemming of the remaining words - remove affixes (prefixes and suffixes)

a – d = production of a set of good index terms d. Selection of index terms – choose words/stems (or groups of words) to be used as indexing terms e. Construction of term categorization structures such as thesaurus, or extraction of structure directly represented in the text, for allowing the expansion of the original query with related terms a – d = production of a set of good index terms e = building of categorization hierarchies to capture relationship

LEXICAL ANALYSIS OF TEXT Change text of the documents into words to be adopted as index terms Objective - identify words in the text Digits, hyphens, punctuation marks, case of letters Numbers not good index terms – 1910, 1999 - but 510 B.C. – unique Hyphen – break up the words (eg. state-of-the-art = state of the art)- but some words, eg. gilt-edged, B-49 - unique words which require hyphens Punctuation marks – remove totally unless significant , eg. program code x.id and xid Case of letters – not important and can convert all to upper or lower

ELIMINATION OF STOPWORD A word which occurs in 80% of the documents in a collection = useless for retrieval= stopwords and filtered out as potential index terms (eg. articles, prepositions, conjunctions) Reduces size of indexing structure Indexing structure compressed by 40% Some verbs, adverbs and adjectives can also be treated as stopwords

425 stopwords identified by W. B. Frakes and R. Baeza-Yates 425 stopwords identified by W.B. Frakes and R. Baeza-Yates. Information retrieval : data structures & algorithms. Englewood Cliffs : Prentice Hall, 1992. Programs in C for lexical analysis are also provided Elimination of stopwords might reduce recall (eg. “To be or not to be” – all eliminated except “be” – no or irrelevant retrieval)

STEMMING Stem = a portion of a word which is left after the removal of it affixes (i.e. prefixes and suffixes) Reduces variants of the same root to a common concept Reduces size of indexing structure because number of distinct index terms is reduced Many Web search engines do not use stemming

INDEX TERM SELECTION If a full text representation of the text is adopted, then all words in the text are used as index terms = full text indexing Need to select the words to be used as index terms Not all words will be selected Bibliographic sciences – done by a specialist Other alternative method is automatic selection

THESAURI Consists of : Aim a precompiled list of important words in a given discipline for each word, a set of related words Words and concepts Aim 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 request according to user needs

Main components of a thesaurus – index terms, relationship among terms (BT, NT, RT) and a layout design for the term relationships, sometimes a definition or explanation (eg. seal (animal) and seal (document) Controlled vocabulary for indexing and searching – useful for established body of knowledge with established terms. Web – thesaurus or free-text searching ?????

eg. Yahoo – present user with term classification hierarchy that reduces the space to be searched

OTHERS Document clustering – group similar or related documents in classes, operation on all documents in the collection and not operation of the text for a document Text compression – ways to represent the data in fewer bits and bytes, greatly reduces amount of space to store text on computers, text – compression – original text reconstructed, takes less time to transmit