Textklassifikation Der Scirus-Classifier

Überblick Komplexes Programm: –Porno-Filter –Extraktion von Namen –Klassifikation aufgrund von Text –Klassifikation nach URL/Title –Feste Klassifikation aufgrund einer URL-Liste –Extraktion von Titel/Autor/Abstract etc bei Artikeln –Ausgabe von Refinement-Termen Hier nur von Interesse: Klassifikation aufgrund des textuellen Inhalts

Textklassifikation Lexikonbasiert: –Phrasen oder Wörter –Erhalten Gewicht für jede Kategorie –Starke Indikatoren Klassifikation durch Berechnung eines Scores: –Für jedes Vorkommen wird für jede Kategorie ein Zähler hochgesetzt –Normalisierung nach Dokumentlänge –Schwellenwert

Konfiguration

Konfigurations-Datein //Number of words to process for subject identification NWDS= MINWORDS=100 THRESHOLD=1 SUBJ=gen all 0 0 SUBJ=chem all 1 0 SUBJ=comp all 2 0 SUBJ=eng all 3 0 SUBJ=env all 4 0 SUBJ=geo all 5 0 SUBJ=astro all 6 0 SUBJ=life all 7 0 SUBJ=math all 8 0 SUBJ=mat all 9 0 SUBJ=med all 10 0 ….

Aufruf CIS Subject Identifier and Content Extractor Version 5.0 USAGE: classifier [-h[elp]] [-os|l[A]] [-it|f|h] [-s[ilent]] [-c CONFIG_FILE] [-nout] [-uat] [-URL ] [-smd ] [-ps] [-t FILES_TO_IDENTIFY] -h: print help -c CONFIG_FILE: Name of the configuration file. Default is././config.txt -os|l[A]: Output format -os: Short: only print well identified subjects(default) -ol: Long: print all subjects -ot: Topics only are output; one line Format: filename:WORDCOUNT#GENERALSCIENCESCORE#TOPICSWITHSCORE ´ -oA: Store and print all phrases for a topic ´ -oT: Print all phrases found in the dictionary ´ (Used for dictionary testing only) -T[t][i][o]: Tasks to carry out and to output (default: all are set) t: Topic identification i: Information from content extractor o: Offensive content filter -it|h|f: Input format -it: Plain text -ih: HTML-file -if: HTML-file preceded by header -nINTEGER :Minumum number of words in a document -MINTEGER :Maximum number of words to be processed in a document tokenizer stops after INTEGER words Documents with less words will get tag 'not_enough_data' -mINTEGER :Minimum score for accepted documents -rINTEGER : maximum relative count for phrase form/thousand In thousand phrases one phrase form will only be counted INTEGER times. -NINTEGER :Maximum number of phrases to output in results for topics -t FILES_TO_IDENTIFY List of files for which subject should be identified. Default: stdin. -D[r] D1|D2[:F1|F2[:FB1|FB2]]: process all files in directory and recurse Dr: descend recursively into subdirectories D1: name of directory to list or recurse F1... : filename patterns (my contain *) FB1: Patterns for forbidden directories (not recursed) -s: print only some important messages, not all. -nout: Turn off URL/Title classifier. -uat: Use all titles for classification (not just those enclosed in ). -URL : Filename of the URL list (format: ). -smd : Maximum number of words for small documents (default see config file). -ps: Print title and url scores -xml: Print XML output

Ablauf Einlesen des Textes bis zur spez. Anzahl von Wörtern Abgleich mit dem Lexikon Berechnen des Scores Ausgabe des Ergebnisses in Abhängigkeit vom Schwellenwert

Scoring Formel Sei: –d Dokument, –c Kategorie, –t Term, –l(t) = Länge von t, –wn(t) = Wortanzahl in t, –q(t,c) Gewicht von t für c und –s(t,c) starker Indikator t für c –T(c) Klassifikations-Schwellenwert für c –W = min(Wörter im Dokument, max proz. Wörter) Score(d,c) = t d (l(t)/2 + (wn(t) -1) x 2) x q(t,c))/W Si-score(d,c) = t d s(tc) d wird als c klassifiziert gdw. Si-score(d,c) > 1 && score(d,c) > T(c)

Klassifikations-Lexikon Format: TERM.INFO1/INFO2/... INFO: TOPICS#FREQUENCY#QUALITY#LENGTH#TYPE#ALONE#OUT PUT –TOPICS: MAIN:SUB –FREQUENCY: 1 (not used) –QUALITY: –LENGTH (number of words) –TYPE: : genuine topic-subtopic indicator 1: only to distinguish between subtopics, not indicating topic itself 2: as 0, but word is to be counted only if there are other phrases for same subtopic, with TYPE 0 3: as 1, but word is to be counted only if there are other phrases for same subtopic, with TYPE 0 –ALONE: 0/1 : strong indicator –OUTPUT: Ø,$, PHRASE

Klassifikations-Lexikon Beispiel –a vinculo matrimonii.18:0#1#0#3#0#0#$ –a-37 aircraft.14:0#1#1#3#0#1#a 37 aircraft –a-address register.2:0#1#1#3#0#1#a address register –a-bomb survivors.7:0#1#8#3#0#1#a bomb survivors –a-c substitutions.15:0#1#8#3#0#1#a c substitutions/7:0#1#8#3#0#1#a c substitutions –a-calcium-calmodulin kinase.11:0#1#8#4#0#1#a calcium-calmodulin kinase –a-chromanoxyl radical.7:0#1#8#3#0#1#a chromanoxyl radical –a-crystallin gene.15:0#1#8#3#0#1#a crystallin gene/7:0#1#8#3#0#1#a crystallin gene –a-d conversion.3:0#1#1#3#0#1#a d conversion –a-d converter.13:0#1#1#3#0#1#a d converter/3:0#1#1#3#0#1#a d converter/9:0#1#1#3#0#1#a d converter –a-deficient mice.11:0#1#7#3#0#1#a deficient mice/15:0#1#8#3#0#1#a deficient mice –a-delta activity.11:0#1#8#3#0#1#a delta activity