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Language Processing for Different Domains and Genres: Machine Learning Introduction Caroline Sporleder, Ines Rehbein Universit at des Saarlandes Wintersemester 2009/10 5.11.2009 Caroline Sporleder, Ines Rehbein Introduction Machine


  1. Language Processing for Different Domains and Genres: Machine Learning Introduction Caroline Sporleder, Ines Rehbein Universit¨ at des Saarlandes Wintersemester 2009/10 5.11.2009 Caroline Sporleder, Ines Rehbein Introduction

  2. Machine Learning Basics Goal: develop computer programs that automatically improve with experience by learning from representative input (and output) data Motivation: for many problems, the best way of computing the correct output from the input is not known. manually determining input output rules by (informed) trial and error is time consuming and typically results in low coverage (but high precision) Example: predicting the plural form of a German noun Caroline Sporleder, Ines Rehbein Introduction

  3. Example: German Plural Formation Nine possibilities: 1 no ending, no umlaut: das Zimmer - die Zimmer (rooms) 2 no ending, but umlaut: der Faden - die F¨ aden (thread) 3 -e: der Hund - die Hunde (dogs) 4 -e plus umlaut: der Stuhl - die St¨ uhle (chairs) 5 -er: das Kind - die Kinder (children) 6 -er plus umlaut: das Lamm - die L¨ ammer (lambs) 7 -n: die Straße - die Straßen (streets) 8 -en: die Bank - die Banken (banks) 9 -s: das Trio - die Trios (trios) Caroline Sporleder, Ines Rehbein Introduction

  4. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Caroline Sporleder, Ines Rehbein Introduction

  5. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Caroline Sporleder, Ines Rehbein Introduction

  6. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: Caroline Sporleder, Ines Rehbein Introduction

  7. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) Caroline Sporleder, Ines Rehbein Introduction

  8. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) ⇒ die Kinder Caroline Sporleder, Ines Rehbein Introduction

  9. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) ⇒ die Kinder der Hund (m) Caroline Sporleder, Ines Rehbein Introduction

  10. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) ⇒ die Kinder der Hund (m) ⇒ die Hunde Caroline Sporleder, Ines Rehbein Introduction

  11. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) ⇒ die Kinder der Hund (m) ⇒ die Hunde die Bank (f) Caroline Sporleder, Ines Rehbein Introduction

  12. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) ⇒ die Kinder der Hund (m) ⇒ die Hunde die Bank (f) ⇒ die Banken Caroline Sporleder, Ines Rehbein Introduction

  13. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) ⇒ die Kinder der Hund (m) ⇒ die Hunde die Bank (f) ⇒ die Banken das Zimmer (n) Caroline Sporleder, Ines Rehbein Introduction

  14. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) ⇒ die Kinder der Hund (m) ⇒ die Hunde die Bank (f) ⇒ die Banken das Zimmer (n) ⇒ die Zimmerer Caroline Sporleder, Ines Rehbein Introduction

  15. Hand-crafting Rules Hypothesis 1: ending is determined by noun’s grammatical gender Rule 1: masculine ⇒ -e neuter ⇒ -er feminine ⇒ -en Applying Rule 1: das Kind (n) ⇒ die Kinder der Hund (m) ⇒ die Hunde die Bank (f) ⇒ die Banken das Zimmer (n) ⇒ *die Zimmerer (die Zimmer) Caroline Sporleder, Ines Rehbein Introduction

  16. Hand-crafting Rules (2) Hypothesis 2: morpho-phonological form also influences ending Caroline Sporleder, Ines Rehbein Introduction

  17. Hand-crafting Rules (2) Hypothesis 2: morpho-phonological form also influences ending Rule 2: don’t add ending if noun already ends in -e, -en, or -er Caroline Sporleder, Ines Rehbein Introduction

  18. Hand-crafting Rules (2) Hypothesis 2: morpho-phonological form also influences ending Rule 2: don’t add ending if noun already ends in -e, -en, or -er Applying Rules 1 and 2: Caroline Sporleder, Ines Rehbein Introduction

  19. Hand-crafting Rules (2) Hypothesis 2: morpho-phonological form also influences ending Rule 2: don’t add ending if noun already ends in -e, -en, or -er Applying Rules 1 and 2: das Zimmer (n) Caroline Sporleder, Ines Rehbein Introduction

  20. Hand-crafting Rules (2) Hypothesis 2: morpho-phonological form also influences ending Rule 2: don’t add ending if noun already ends in -e, -en, or -er Applying Rules 1 and 2: das Zimmer (n) ⇒ die Zimmer Caroline Sporleder, Ines Rehbein Introduction

  21. Hand-crafting Rules (2) Hypothesis 2: morpho-phonological form also influences ending Rule 2: don’t add ending if noun already ends in -e, -en, or -er Applying Rules 1 and 2: das Zimmer (n) ⇒ die Zimmer die Ampel (f) Caroline Sporleder, Ines Rehbein Introduction

  22. Hand-crafting Rules (2) Hypothesis 2: morpho-phonological form also influences ending Rule 2: don’t add ending if noun already ends in -e, -en, or -er Applying Rules 1 and 2: das Zimmer (n) ⇒ die Zimmer die Ampel (f) ⇒ die Ampelen Caroline Sporleder, Ines Rehbein Introduction

  23. Hand-crafting Rules (2) Hypothesis 2: morpho-phonological form also influences ending Rule 2: don’t add ending if noun already ends in -e, -en, or -er Applying Rules 1 and 2: das Zimmer (n) ⇒ die Zimmer die Ampel (f) ⇒ *die Ampelen (die Ampeln) Caroline Sporleder, Ines Rehbein Introduction

  24. Hand-crafting Rules (3) Rule 3: -e and -en become ø and -n if the last syllable of the singular contains a schwa. Caroline Sporleder, Ines Rehbein Introduction

  25. Hand-crafting Rules (3) Rule 3: -e and -en become ø and -n if the last syllable of the singular contains a schwa. Applying Rules 1, 2 and 3: Caroline Sporleder, Ines Rehbein Introduction

  26. Hand-crafting Rules (3) Rule 3: -e and -en become ø and -n if the last syllable of the singular contains a schwa. Applying Rules 1, 2 and 3: die Ampel (f) Caroline Sporleder, Ines Rehbein Introduction

  27. Hand-crafting Rules (3) Rule 3: -e and -en become ø and -n if the last syllable of the singular contains a schwa. Applying Rules 1, 2 and 3: die Ampel (f) ⇒ die Ampeln Caroline Sporleder, Ines Rehbein Introduction

  28. Hand-crafting Rules (3) Rule 3: -e and -en become ø and -n if the last syllable of the singular contains a schwa. Applying Rules 1, 2 and 3: die Ampel (f) ⇒ die Ampeln der Nachbar (m) Caroline Sporleder, Ines Rehbein Introduction

  29. Hand-crafting Rules (3) Rule 3: -e and -en become ø and -n if the last syllable of the singular contains a schwa. Applying Rules 1, 2 and 3: die Ampel (f) ⇒ die Ampeln der Nachbar (m) ⇒ die Nachbare Caroline Sporleder, Ines Rehbein Introduction

  30. Hand-crafting Rules (3) Rule 3: -e and -en become ø and -n if the last syllable of the singular contains a schwa. Applying Rules 1, 2 and 3: die Ampel (f) ⇒ die Ampeln der Nachbar (m) ⇒ *die Nachbare (die Nachbarn) Caroline Sporleder, Ines Rehbein Introduction

  31. Machine Learning German Plural Formation Learn from input-output pairs: Zimmer, Zimmer Faden, F¨ aden Hund, Hunde Stuhl, St¨ uhle Kind, Kinder Lamm, L¨ ammer Straße, Straßen Bank, Banken Trio, Trios Ampel, Ampeln Nachbar, Nachbarn Maus, M¨ ause ⇒ Input is typically represented as a feature vector. Caroline Sporleder, Ines Rehbein Introduction

  32. Machine Learning German Plural Formation (2) What information needs to be represented for the task to be learnable (i.e., which features need to be modelled)? Caroline Sporleder, Ines Rehbein Introduction

  33. Machine Learning German Plural Formation (2) What information needs to be represented for the task to be learnable (i.e., which features need to be modelled)? 1 Zimmer: < n > , Zimmer 2 Faden: < m > , F¨ aden 3 Hund: < m > , Hunde 4 Stuhl: < m > , St¨ uhle 5 Kind: < n > , Kinder 6 Lamm: < n > , L¨ ammer 7 Straße: < f > , Straßen 8 Bank: < f > , Banken 9 Trio: < n > , Trios 10 Ampel: < f > , Ampeln 11 Nachbar: < m > , Nachbarn 12 Maus: < f > , M¨ ause Caroline Sporleder, Ines Rehbein Introduction

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