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Natural Language Processing >> Morphology <<

Natural Language Processing >> Morphology <<. winter / fall 2010/2011 41.4268. Prof. Dr. Bettina Harriehausen-Mühlbauer Univ. of Applied Science, Darmstadt, Germany www.fbi.h-da.de/~harriehausen b.harriehausen@fbi.h-da.de Bettina.Harriehausen@h-da.de. content. 1 morphemes

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Natural Language Processing >> Morphology <<

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  1. Natural Language Processing>> Morphology << winter / fall 2010/201141.4268 Prof. Dr. Bettina Harriehausen-Mühlbauer Univ. of Applied Science, Darmstadt, Germany www.fbi.h-da.de/~harriehausen b.harriehausen@fbi.h-da.de Bettina.Harriehausen@h-da.de

  2. content • 1 morphemes • 2 compounds / concatenation • 3 idiomatic phrases • multiple word entries (MWE) • spell aid • regular expressions • Finite State Automata (FSA) NLP - Harriehausen

  3. content • 1 morphemes • 2 compounds / concatenation • 3 idiomatic phrases • multiple word entries (MWE) • spell aid • regular expressions • Finite State Automata (FSA) NLP - Harriehausen

  4. definition • Morphemes • morpheme = smallest possible item in a language that carries meaning • lexeme (man, house, dog,...) • inflectional affixes (dog-s, want-ed,...) • other affixes (pre-/in-/suff-): unwanted, atypical, antipathetic,... • esp. in technical language (-itis = „infection“, gastro = stomach...gastroenteritis) NLP - Harriehausen

  5. morphemes NLP - Harriehausen

  6. morphemes free morphemes : stand-alone, carry lexical and morphological meaning (e.g. house= sing, neuter, nominative ; case/number/gender) bound morphemes : legal wordformonly in combination with another morpheme, stand-alone, carry lexical and morphological meaning (e.g. un-happy, gastroenteritis) NLP - Harriehausen

  7. morphemes inflectional morphemes : create words and carry morphological meaning (e.g. dogs, laughed, going derivational morphemes : create wordforms and carry morphological meaning ( happily, intellectually, instruction, instructor, insulator, the pounding, limpness, blindness...) Question: which string (~morpheme) do we include in our dictionary ? NLP - Harriehausen

  8. content • 1 morphemes • 2 compounds / concatenation • 3 idiomatic phrases • multiple word entries (MWE) • spell aid • regular expressions • Finite State Automata (FSA) NLP - Harriehausen

  9. compounds / concatenation in addition to single morphemes, we need to consider „multiple morpheme strings / multi word expressions“ (fixed phrases): increasing the idiomatic rigidity increasing the formal complexity • independent of the context: dog, cat, ... • compounding: combine lexical meanings: carseat, houseboat,... • compounding: not a combination of the lexical meanings: nosebag, nosedive, paperback, ladybug,... • depending on the context: bite the dust, lose face, kick the bucket,... = NLP - Harriehausen

  10. Samples for long compounds in German • die Armbrust • die Mehrzweckhalle • das Mehrzweckkirschentkerngerät • die Gemeindegrundsteuerveranlagung • die Nummernschildbedruckungsmaschine • der Mehrkornroggenvollkornbrotmehlzulieferer • der Schifffahrtskapitänsmützenmaterialhersteller • die Verkehrsinfrastrukturfinanzierungsgesellschaft • die Feuerwehrrettungshubschraubernotlandeplatzaufseherin • der Oberpostdirektionsbriefmarkenstempelautomatenmechaniker • das Rindfleischetikettierungsüberwachungsaufgabenübertragungsgesetz • die Donaudampfschifffahrtselektrizitätenhauptbetriebswerkbauunterbeamtengesellschaft NLP - Harriehausen

  11. compounds / concatenation decompounding: principles / rules: FANO rule: „the analysis is unambiguous, when a morpheme is not the beginning of another morpheme“ (= principle of longest match) e.g. but / butter Segmentation has to be done recursively in order to find all possibilities: horseshoe: horses – hoe (?) vs. horse-shoe Staubecken: Stau – Becken vs. Staub - Ecken NLP - Harriehausen

  12. concatenation Problems: not all morphemes can be concatenated NLP - Harriehausen

  13. content • 1 morphemes • 2 compounds / concatenation • 3 idiomatic phrases • multiple word entries (MWE) • spell aid • regular expressions • Finite State Automata (FSA) NLP - Harriehausen

  14. idiomatic phrases (http://www.geo.de/GEOlino/mensch/redewendungen/englisch) • Out of the blue • To be on Cloud Nine • A leopard cannot change its spots • Head over heels • Fair Play • As cool as a cucumber • The early bird catches the worm • An apple a day keeps the doctor away • As fit as a fiddle • Beat about the bush • The Big Apple • The apple of my eye • Wet behind the ears • A bird in the hand is worth two in the bush • It's raining cats and dogs • A friend in need is a friend indeed • It's all greek to me NLP - Harriehausen

  15. idiomatic phrases (http://www.geo.de/GEOlino/mensch/redewendungen/deutsch) • Wie bei Hempels unterm Sofa • Schmetterlinge im Bauch • Jemanden übers Ohr hauen • Ein Bäuerchen machen • Mit jemandem durch dick und dünn gehen • Seine Pappenheimer kennen • Jemandem die Würmer aus der Nase ziehen • Die Arschkarte ziehen • Mit jemandem Pferde stehlen können • Sich aus dem Staub machen • Hummeln im Hintern haben • Im siebten Himmel sein • Viele Wege führen nach Rom • Mit einem lachenden und einem weinenden Auge • Nah am Wasser gebaut haben • Da ist der Bär los • Nachtigall, ick hör dir trapsen • Mein lieber Scholli! NLP - Harriehausen

  16. idiomatic phrases (http://www.geo.de/GEOlino/mensch/redewendungen/deutsch) • Jemandem einen Denkzettel verpassen • Sich auf den Schlips getreten fühlen • Alles für die Katz • Wo drückt denn der Schuh? • Gegen den Strich gehen • Den Faden verlieren • Etwas ausbaden müssen • Einen Stein im Brett haben • Bahnhof verstehen • Der springende Punkt • Der Sündenbock sein • Einen Ohrwurm haben • Das ist doch zum Mäusemelken! • Schmiere stehen • Den Teufel an die Wand malen • Auf dem Holzweg sein • Eselsbrücke • In der Kreide stehen NLP - Harriehausen

  17. idiomatic phrases (http://www.geo.de/GEOlino/mensch/redewendungen/deutsch) • Die Ohren steif halten • Auf Vordermann bringen • Um die Ecke bringen • Hals- und Beinbruch • Auf dem Kerbholz haben • Eine Schlappe einstecken • Frosch im Hals • Es zieht wie Hechtsuppe • Jemandem einen Bärendienst erweisen • Damoklesschwert • Tomaten auf den Augen haben • Jemandem raucht der Kopf • Für 'n Appel und 'n Ei • Etwas an die große Glocke hängen • Das ist Jacke wie Hose • Etwas aus dem Ärmel schütteln • Ein X für ein U vormachen • Jemandem nicht das Wasser reichen können NLP - Harriehausen

  18. idiomatic phrases (http://www.geo.de/GEOlino/mensch/redewendungen/deutsch) • Alles im grünen Bereich • Die Hand ins Feuer legen • Auf Draht sein • Sein blaues Wunder erleben • Der hat es faustdick hinter den Ohren • Mein Name ist Hase, ich weiß von nichts • Aus dem Stegreif • Der Groschen ist gefallen • Einen Vogel haben • Den Kürzeren ziehen • Bis in die Puppen • Etwas hinter die Ohren schreiben • Ins Fettnäpfchen treten • Beleidigte Leberwurst • Jemanden auf dem Kieker haben • Ich verstehe immer nur Bahnhof! • Die Katze im Sack kaufen • Das kann kein Schwein lesen! NLP - Harriehausen

  19. idiomatic phrases (http://www.geo.de/GEOlino/mensch/redewendungen/deutsch) • Bekannt wie ein bunter Hund • Den Kopf in den Sand stecken • Mit dem ist nicht gut Kirschen essen • Aller guten Dinge sind drei • Lampenfieber • Das kommt mir spanisch vor • Schwein haben • Das hast du dir selbst eingebrockt • Seinen Senf dazugeben • Jemandem ist eine Laus über die Leber gelaufen • Kalte Füße bekommen • Im Stich lassen • Schwedische Gardinen • Alles in Butter • Geld auf den Kopf hauen • Das Handtuch werfen • Sich mit fremden Federn schmücken NLP - Harriehausen

  20. content • 1 morphemes • 2 compounds / concatenation • 3 idiomatic phrases • multiple word entries (MWE) • spell aid • regular expressions • Finite State Automata (FSA) NLP - Harriehausen

  21. multiple word entries (MWE) in addition to single morphemes, we need to consider „multiple morpheme strings“ (fixed phrases): • independent of the context: dog, cat, ... • compounding (a): combine lexical meanings: carseat, houseboat,... • compounding (b): not a combination of the lexical meanings: nosebag, nosedive, paperback, ladybug, soap opera... • depending on the context: bite the dust, lose face, kick the bucket,... • electronic dictionaries • all NLP applications • machine translation ! NLP - Harriehausen

  22. multiple word entries (MWE) Problems: the relationships among the components change the „Schnitzel“ problem • sirloin steak (made from certain parts of..) • soy steak (made out of material...) • „Wiener Schnitzel“ (according to a certain receipe) • pepper steak (served with...) • ... • Even though the single lexical meanings remain untouched in the compound, the relationship between the compounds varies tremendously ! NLP - Harriehausen

  23. multiple word entries (MWE) the 3 main relationships (default ?) between parts of a compound word: (the role of global knowledge in decompounding) compound meaning relationship doorknob knob of the door is-a / is-part-of/ carseat seat of the car genitive glasdoor door made of glas made from / material nutbread ‡ bread of the nut waterglas glas filled with water used for oiltruck truck that carries oil ‡ truck made of oil 1 2 3 NLP - Harriehausen

  24. multiple word entries (MWE) decompounding: the orange bowl problem Can you please bring me the orange bowl ? ? bowl of orange colour ? bowl filled with oranges ? ? bowl that was formerly / usually filled with oranges bowl having the shape of an orange ? bowl with an orange pattern NLP - Harriehausen

  25. content • 1 morphemes • 2 compounds / concatenation • 3 idiomatic phrases • multiple word entries (MWE) • spell aid • regular expressions • Finite State Automata (FSA) NLP - Harriehausen

  26. spell aid in NLP, decompounding algorithms are essential for spell-checking / spell aid : How do we define lexical error in NLP terms ? An error is a string that cannot be found in / matched with a dictionary entry. It is not necessarily an incorrect word (esp. neologisms). NLP - Harriehausen

  27. spell aid • spell checking algorithms are based on the following types of mistakes (statistics !): • phonetic similarities (ph – f : telephone – telefone) • deletion of multiple entries ( mouuse - mouse) • wrong order (from – form ; mouse – muose) • substitution of neighbouring letters on the keyboard (miuse – mouse) • include missing letters (vowels in between consonants...) (telephne) • typos occur towards the end of a word (assumption:first letter is correct) • segmentation / decomposition into substrings (horeshoe – horseshoe) NLP - Harriehausen

  28. spell aid • phonetic similarities (ph – f : telephone – telefone) • deletion of multiple entries ( mouuse - mouse) • wrong order (from – form ; mouse – muose) • substitution of neighbouring letters on the keyboard (miuse – mouse) • include missing letters (vowels in between consonants...) (telephne) • typos occur towards the end of a word (assumption:first letter is correct) • segmentation / decomposition into substrings (horeshoe – horseshoe) NLP - Harriehausen

  29. spell aid • include missing letters (vowels in between consonants...) (telephne) • certain rules apply: e.g. in German: never concatenate „l“, „n“ or „r“ with „tz“ and „ck“: • _ltz_ *Holtz_lck__ntz__nck__rlz__rck_ NLP - Harriehausen

  30. spell aid • include missing letters www.dositey.com/language/spelling/Mislet3.htm NLP - Harriehausen

  31. spell aid How does spell checking work (w.r.t. grammar checking) ? Various degrees of „intelligence“: System A : no match found in the dictionary -> mark entry as incorrect System B: no match found in the dictionary. Initiate a rudimentary parse (left-right-search). Try to identify the wordclass, i.e. limit possibilities and continue a sentential analysis. e.g. the ...man (statistics: DET + ADJ + NOUN) System C: no match found in the dictionary. Initiate a segmentation of the word to identify the wordclass, e.g. look for typical endings (-ly = adverb / capital letters = proper noun, ...). This way new wordcreations can be identified (e.g. any word ending in -ness = noun) NLP - Harriehausen

  32. content • 1 morphemes • 2 compounds / concatenation • 3 idiomatic phrases • multiple word entries (MWE) • spell aid • regular expressions • Finite State Automata (FSA) NLP - Harriehausen

  33. regular expressions (Jurafsky, section 2.1) • In order to figure out whether something is an incorrect word, the machine has to match the string (= a sequence of symbols; any sequence of alphanumeric characters (letters, numbers, spaces, tabs, punctuation) to an entry in the dictionary • other matches: e.g. information retrieval in www-search engines (google, altavista,…) • the standard notation for characterizing text sequences=regular expressions • regular expressions are written in (regular expression) languages: e.g. Perl, grep (Global Regular Expression Print) • formally, regular expressions are algebraic notations for characterizing a set of strings • regular expression search requires a pattern that we want to search for (and a corpus of text to search through) (text mining !) NLP - Harriehausen

  34. regular expressions (Jurafsky, section 2.1) Example: Search for the pattern “linguistics”. • You also want to find documents with “Linguistics” and “LINGUISTICS”. (remember: the computer does EXACTLY do what you tell him to…) • The regular expression /linguistics/ matches any string in any document containing exactly the substring “linguistics” • Regular expressions are case sensitive • samples (Jurafsky, p. 23) regular expression example pattern matched /woodchucks/ “interesting links to woodchucks and lemurs” /a/ “Mary Ann stopped by Mona’s” /Claire says,/ Dagmar, my gift please,” Claire says,” /song/ “all our pretty songs” /!/ “You’ve left the burglar behind again!” said Nori NLP - Harriehausen

  35. regular expressions (Jurafsky, section 2.1) linguistics - Linguistics - LINGUSTICS to search for alternative characters “l” and/or “L” we use square brackets: [l L] Regular expression match sample pattern /[l L] inguistics/ Linguistics or linguistics “computational linguistics is fun” /[1 2 3 4 5 6 7 8 9 0]/ any digit this is Linguistics 5981 NLP - Harriehausen

  36. regular expressions (Jurafsky, section 2.1) to search for a character in a range we use the dash: [-] Regular expression match sample pattern /[A-Z]/ any uppercase letter this is Linguistics 5981 /[0-9]/ any single digitthis is Linguistics 5981 /[1 2 3 4 5 6 7 8 9 0]/ any single digit this is Linguistics 5981 NLP - Harriehausen

  37. regular expressions (Jurafsky, section 2.1) to search for negation, i.e. a character that I do NOT want to find we use the caret: [^] Regular expression match sample pattern /[^A-Z]/ not an uppercase letter this is Linguistics 5981 /[^L l]/ neither L nor lthis is Linguistics 5981 /[^\.]/ not a period this is Linguistics 5981 Special characters: \* an asterisk “L*I*N*G*U*I*S*T*I*C*S”\. a period “Dr.Doolittle”\? a question mark “Is this Linguistics 5981 ?”\n a newline\t a tab NLP - Harriehausen

  38. regular expressions (Jurafsky, section 2.1) to search for optional characters we use the question mark: [?] Regular expression match sample pattern /colou?r/ colour or color beautiful colour to search for any number of a certain character we use the Kleene star: [*] Regular expression match /a*/ any string of zero or more “a”s /aa*/ at least one a but also any number of “a”s NLP - Harriehausen

  39. regular expressions (Jurafsky, section 2.1) Any combination is possible To look for at least one character of a type we use the Kleene “+”: Regular expression match /[0-9]+/ a sequence of digits Regular expression match /[ab]*/ zero or more “a”s or “b”s /[0-9] [0-9]*/ any integer (= a string of digits) NLP - Harriehausen

  40. regular expressions (Jurafsky, section 2.1) The “.” is a very special character -> so-called wildcard Regular expression match sample pattern /b.ll/ any character ball between b and ll bellbullbill Will the search find “Bill” ? NLP - Harriehausen

  41. regular expressions (Jurafsky, section 2.1) Anchors (start of line: “^”, end of line:”$”) Regular expression match sample pattern /^Linguistics/ “Linguistics” at the Linguistics is fun. beginning of a line /linguistics\.$/ “linguistics” at the We like linguistics. end of a line Anchors (word boundary: “\b”, non-boundary:”\B”) Regular expression match sample pattern /\bthe\b/ “the” alone This is the place. /\Bthe\B/ “the” included This is my mother. NLP - Harriehausen

  42. regular expressions (Jurafsky, section 2.1) More on alternative characters: the pipe symbol: “|” (disjunction) Regular expression match sample pattern /colou?r/ colour or color beautiful colour /progra(m|mme)/ program or programme linguistics program NLP - Harriehausen

  43. regular expressions (Jurafsky, section 2.1) What does the following expression match ? /student [0-9] + */ Will it match “student 1 student 2 student 3” ? operator precedence hierarchy NLP - Harriehausen

  44. regular expressions (Jurafsky, section 2.1) Perl expressions are also used for string substitution: (used in ELIZA) s/man/men/ man -> men Perl expressions are also used for string repetition via memory: (the number operator) s/(linguistics)/wonderful \1/ linguistics-> wonderful linguistics ELIZA s/.* YOU ARE (depressed|sad) .*/ I AM SORRY TO HEAR YOU ARE \1/ s/.* YOU ARE (depressed|sad) .*/ WHY DO YOU THINK YOU ARE \1 ?/ NLP - Harriehausen

  45. content • 1 morphemes • 2 compounds / concatenation • 3 idiomatic phrases • multiple word entries (MWE) • spell aid • regular expressions • Finite State Automata (FSA) NLP - Harriehausen

  46. Finite State Automata (FSA) • The regular expression is more than just a convenient metalanguage for text searching. • First, a regular expression is one way of describing a finite-state automaton (FSA).Finite-state automata are the theoretical foundation of a good deal of the computational work we will describe and look at in this lecture. Any regular expression can be implemented as a finite-state automaton*. Symmetrically, any finite-state automaton can be described with a regular expression. • Second, a regular expression is one way of characterizing a particular kind of formal language called a regular language. Both regular expressions and finite-state automata can be used to describe regular languages. The relation among these three theoretical constructions is sketched out in the following figure: * Except regular expressions that use the memory feature – more on that later NLP - Harriehausen

  47. Finite State Automata (FSA) regular expressions Finite regular Automata languages The relationship between finite state automata, regular expressions, and regular languages* * as suggested by Martin Kay in:Kay, M. (1987). Nonconcatenative finite-state morphology. In Proceedings of the Third Conference of the European Chapter of the ACL (EACL-87), Copenhagen, Denmark,pp. 2-10.ACL.). NLP - Harriehausen

  48. Finite State Automata (FSA) • Examples: • Introduction to finite-state automata for regular expressions • Mapping from regular expressions to automata examples NLP - Harriehausen

  49. Finite State Automata (FSA) Using a FSA to recognize sheeptalk After a while, with the parrot‘s help, the Doctor got to learn the language of the animals so well that he could talk to them himself and understand everything they said. Hugh Lofting, The Story of Doctor Doolittle NLP - Harriehausen

  50. Finite State Automata (FSA) Using a FSA to recognize sheeptalk Sheep language can be defined as any string from the following (infinite) set: baa! baaa! baaaa! baaaaa! baaaaaa! .... NLP - Harriehausen

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