1. WMES3103 : INFORMATION RETRIEVAL
TEXT OPERATIONS

2. 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

3. 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

4. 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

5. 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)

6. 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

7. 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, 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

8. 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

9. 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)

10. 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

11. 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

12. 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

15. 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 ?????

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

21. 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