Presentation on theme: "Text Processing. Slide 1 Simple Tokenization Analyze text into a sequence of discrete tokens (words). Sometimes punctuation (e-mail), numbers (1999),"— Presentation transcript:
Slide 1 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 … ??
Slide 2 Punctuation Children’s: use language-specific mappings to normalize (e.g. Anglo-Saxon genitive of nouns, verb contractions: won’t -> wo ‘nt) State-of-the-art: break up hyphenated sequence. U.S.A. vs. USA a.out
Slide 3 Numbers 3/12/91 Mar. 12, B.C. B –Generally, don’t index as text –Creation dates for docs
Slide 4 Case Folding Reduce all letters to lower case –exception: upper case in mid-sentence e.g., General Motors Fed vs. fed SAIL vs. sail
Slide 5 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. Note: on the class webpage you can find a link to a more sophisticated, ready to use tokenizer.
Slide 6 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?
Slide 7 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 (first returned value from validForms function)
Slide 8 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.
Slide 9 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.
Slide 10 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
Slide 11 Porter Stemmer Errors Errors of “comission”: –organization, organ organ –police, policy polic –arm, army arm Errors of “omission”: –cylinder, cylindrical –create, creation –Europe, European
Slide 12 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
Slide 13 Web Metadata META tag in HTML – META “HTTP-EQUIV” attribute allows server or browser to access information: –
Slide 14 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
Slide 15 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
Slide 16 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.
Slide 18 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.
Slide 19 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.
Slide 20 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.
Slide 21 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.
Slide 22 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.
Slide 23 DTD Example *: 0 or more repetitions ?: 0 or 1 (optional) | : alternation (or) PCDATA: Parsed Character Data (may contain tags)
Slide 24 DTD (cont’d) Tag attributes are also defined: CDATA: Character data (string) IMPLIED: Optional Can define DTD in a separate file: