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1 Information Retrieval and Web Search Text processing Instructor: Rada Mihalcea Class web page: (Note: This slide set was adapted from an IR course taught by Prof. Ray Mooney at UT Austin)

2 Slide 1 Last time Architecture of a classic IR system –Including main IR components Main IR models –Boolean –Vectorial –Probabilistic

3 Slide 2 IR System Architecture Text Database Manager Indexing Index Query Operations Searching Ranking Ranked Docs User Feedback Text Operations User Interface Retrieved Docs User Need Text Query Logical View Inverted file

4 Slide 3 IR System Components Text Operations forms index words (tokens). –Tokenization –Stopword removal –Stemming Indexing constructs an inverted index of word to document pointers. –Mapping from keywords to document ids I did enact Julius Caesar I was killed i' the Capitol; Brutus killed me. Doc 1 So let it be with Caesar. The noble Brutus hath told you Caesar was ambitious Doc 2

5 Slide 4 IR System Components Searching retrieves documents that contain a given query token from the inverted index. Ranking scores all retrieved documents according to a relevance metric. User Interface manages interaction with the user: –Query input and document output. –Relevance feedback. –Visualization of results. Query Operations transform the query to improve retrieval: –Query expansion using a thesaurus. –Query transformation using relevance feedback.

6 Slide 5 Today’s topics Text operations in IR systems –Tokenization –Stopword removal –Lemmatization –Stemming –In an IR system, text operations are applied on ??? On metadata and markup languages –(if time permits)

7 Slide 6 Simple Tokenization Analyze text into a sequence of discrete tokens (words). Sometimes punctuation ( ), numbers (1999), and case (Republican vs. republican) can be a meaningful part of a token. –However, frequently they are not. Simplest approach is to ignore all numbers and punctuation and use only case-insensitive unbroken strings of alphabetic characters as tokens. More careful approach: –Separate ? ! ; : “ ‘ [ ] ( ) –Care with. - why? when? –Care with … ??

8 Slide 7 Punctuation Ne’er: use language-specific, handcrafted “locale” to normalize. State-of-the-art: break up hyphenated sequence. U.S.A. vs. USA - use locale. a.out

9 Slide 8 Numbers 3/12/91 Mar. 12, B.C. B –Generally, don’t index as text –Creation dates for docs

10 Slide 9 Case folding Reduce all letters to lower case –exception: upper case in mid-sentence e.g., General Motors Fed vs. fed SAIL vs. sail

11 Slide 10 Tokenizing HTML Should text in HTML commands not typically seen by the user be included as tokens? –Words appearing in URLs. –Words appearing in “meta text” of images. Simplest approach is to exclude all HTML tag information (between “ ”) from tokenization.

12 Slide 11 Stopwords It is typical to exclude high-frequency words (e.g. function words: “a”, “the”, “in”, “to”; pronouns: “I”, “he”, “she”, “it”). Stopwords are language dependent For efficiency, store strings for stopwords in a hashtable to recognize them in constant time. –Simple Perl hashtable for Perl-based implementations How to determine a list of stopwords? –For English? – may use existing lists of stopwords E.g. SMART’s commonword list (~ 400) WordNet stopword list –For Spanish? Bulgarian?

13 Slide 12 Lemmatization Reduce inflectional/variant forms to base form Direct impact on VOCABULARY size E.g., –am, are, is  be –car, cars, car's, cars'  car the boy's cars are different colors  the boy car be different color How to do this? –Need a list of grammatical rules + a list of irregular words –Children  child, spoken  speak … –Practical implementation: use WordNet’s morphstr function Perl: WordNet::QueryData –[ Digression: See “Words and Rules” by Steven Pinker A theory on how human mind combines rules for regular words with memorization of irregular forms ]

14 Slide 13 Stemming Reduce tokens to “root” form of words to recognize morphological variation. –“computer”, “computational”, “computation” all reduced to same token “compute” Correct morphological analysis is language specific and can be complex. Stemming “blindly” strips off known affixes (prefixes and suffixes) in an iterative fashion. for example compressed and compression are both accepted as equivalent to compress. for exampl compres and compres are both accept as equival to compres.

15 Slide 14 Porter Stemmer Simple procedure for removing known affixes in English without using a dictionary. Can produce unusual stems that are not English words: –“computer”, “computational”, “computation” all reduced to same token “comput” May conflate (reduce to the same token) words that are actually distinct. Not recognize all morphological derivations.

16 Slide 15 Typical rules in Porter sses  ss ies  i ational  ate tional  tion See class website for link to “official” Porter stemmer site –Provides Perl, C ready to use implementations

17 Slide 16 Porter Stemmer Errors Errors of “comission”: –organization, organ  organ –police, policy  polic –arm, army  arm Errors of “omission”: –cylinder, cylindrical –create, creation –Europe, European

18 Slide 17 Other stemmers Other stemmers exist, e.g., Lovins stemmer Single-pass, longest suffix removal (about 250 rules) Motivated by Linguistics as well as IR Full morphological analysis - modest benefits for retrieval

19 Slide 18 Stemming exercise Stemming procedure?

20 Slide 19 Remainder of today’s lecture On Metadata –Often included in Web pages –Hidden from the browser, but useful for indexing Information about a document that may not be a part of the document itself (data about data). Descriptive metadata is external to the meaning of the document: –Author –Title –Source (book, magazine, newspaper, journal) –Date –ISBN –Publisher –Length

21 Slide 20 Web Metadata META tag in HTML – META “HTTP-EQUIV” attribute allows server or browser to access information: –

22 Slide 21 RDF Resource Description Framework. XML compatible metadata format. New standard for web metadata. –Content description –Collection description –Privacy information –Intellectual property rights (e.g. copyright) –Content ratings –Digital signatures for authority

23 Slide 22 Markup Languages Language used to annotate documents with “tags” that indicate layout or semantic information. Most document languages (Word, RTF, Latex, HTML) primarily define layout. History of Generalized Markup Languages: GML(1969)SGML (1985) HTML (1993) XML (1998) Standard HyperText eXtensible

24 Slide 23 Basic SGML Document Syntax Blocks of text surrounded by start and end tags. – Tagged blocks can be nested. In HTML end tag is not always necessary, but in XML it is.

25 Slide 24 HTML Developed for hypertext on the web. – May include code such as Javascript in Dynamic HTML (DHTML). Separates layout somewhat by using style sheets (Cascade Style Sheets, CSS). However, primarily defines layout and formatting.

26 Slide 25 XML Like SGML, a metalanguage for defining specific document languages. Simplification of original SGML for the web promoted by WWW Consortium (W3C). Fully separates semantic information and layout. Provides structured data (such as a relational DB) in a document format. Replacement for an explicit database schema.

27 Slide 26 XML (cont’d) Allows programs to easily interpret information in a document, as opposed to HTML intended as layout language for formatting docs for human consumption. New tags are defined as needed. Structures can be nested arbitrarily deep. Separate (optional) Document Type Definition (DTD) defines tags and document grammar.

28 Slide 27 XML Example John Doe 38 is shorthand for empty tag Tag names are case-sensitive (unlike HTML) A tagged piece of text is called an element.

29 Slide 28 XML Example with Attributes arroz con pollo 2.30 Attribute values must be strings enclosed in quotes. For a given tag, an attribute name can only appear once.

30 Slide 29 Document Type Definition (DTD) Grammar or schema for defining the tags and structure of a particular document type. Allows defining structure of a document element using a regular expression. Expression defining an element can be recursive, allowing the expressive power of a context-free grammar.

31 Slide 30 DTD Example *: 0 or more repetitions ?: 0 or 1 (optional) | : alternation (or) PCDATA: Parsed Character Data (may contain tags)

32 Slide 31 DTD (cont’d) Tag attributes are also defined: CDATA: Character data (string) IMPLIED: Optional Can define DTD in a separate file:


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