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NLP_Chapter #2 Morphological Analysis.pdf

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The document discusses morphological analysis in Amharic, detailing how nouns, adjectives, and verbs can be formed and marked for number, definiteness, gender, and case through various morphological processes. It also introduces the concept of finite state machines (FSMs) and their application in natural language processing, explaining the differences between deterministic and non-deterministic finite state automata. Additionally, examples of how FSMs can recognize single and multiple words in a language are provided.

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COSC 6405 2018/19 Sem I Morphological Analysis
ስነ ‫ו‬እֶድ ‫ו‬እֶድ ָጅነُ አ‫ו‬ድ ስ‫ץ‬ የቃָ ክፍָ
ָጅ ָጅነُ ቤُ ቤِ٤ [ቤ(ُኦ)٤] ቤُ ከቤُ ነُ ָጅነُ ኦ٤ ቤِ٤ [ቤ(ُኦ)٤] ከ ከቤُ
ስብ‫ץ‬ ‫ר‬በ‫ץ‬ ስብ‫ץ‬ ‫רـ‬በ‫נ‬٤ -ና- ُናን ُን አָ…ኧ‫ו‬ አָ‫ר‬በ‫ונ‬ [አָ‫ר‬በ(‫ץ‬ኧ)‫ו‬]
NLP_Chapter #2 Morphological Analysis.pdf
NLP_Chapter #2 Morphological Analysis.pdf
NLP_Chapter #2 Morphological Analysis.pdf
NLP_Chapter #2 Morphological Analysis.pdf
NLP_Chapter #2 Morphological Analysis.pdf
Positive Comparative Superlative good better best bad worse worst little less least much many more most
NLP_Chapter #2 Morphological Analysis.pdf
NLP_Chapter #2 Morphological Analysis.pdf
Verbal Root (Examples) Pattern of Derivation Derived Noun ጥ-ቅ-‫ו‬ CእCእC ጥእቅእ‫ו‬ [ጥቅ‫]ו‬ ‫ו‬-‫ץ‬-ُ CእCC ‫ו‬እ‫ُץ‬ [‫]ُץו‬ ‫ו‬-ָ-ስ CኧCC ‫ו‬ኧָስ [‫ָא‬ስ] ን-ግ-‫ץ‬ CኧCኧC ንኧግኧ‫ץ‬ [ነገ‫]ץ‬ ድ-ክ-‫ו‬ CእCኣC ድእክኣ‫ו‬ [ድካ‫]ו‬ ֱ-‫ו‬-‫ו‬ CእCኧC ֱእ‫ו‬ኧ‫ו‬ [ֱ‫]וא‬ ግ-ብ-ዕ CእC ግእብ [ግብ] ጥ-ው-‫ו‬ CኦC ጥኦ‫ו‬ [ጦ‫]ו‬ ቅ-ው-‫ץ‬-ጥ CኡCC ቅኡ‫ץ‬ጥ [‫ץשּׁ‬ጥ] ድ-ብብ-ቅ CእC1C1እC ድእብብእቅ [ድብቅ] Adjective (Examples) Morpheme Derived Noun ደግ -ነُ ደግ-ነُ [ደግነُ] ቅ‫ץ‬ብ -ኧُ ቅ‫ץ‬ብ-ኧُ [ቅ‫ץ‬በُ] ብֱָ -ኣُ ብֱָ-ኣُ [ብָሃُ] ብָጥ -ኦ ብָጥ-ኦ [ብָጦ]
Stem (Examples) Morpheme Derived Noun ው‫ץ‬ድ- -ኧُ ው‫ץ‬ድ-ኧُ [ው‫ץ‬ደُ] ቅዳስ- -ኤ ቅዳስ-ኤ [ቅዳሴ] እ‫ץ‬ጅ- -እና እ‫ץ‬ጅ-እና [እ‫ץ‬ጅና] ָ‫ו‬- -ኣُ ָ‫ו‬-ኣُ [ ָ‫]ُד‬ ስ‫ץ‬ቅ- -ኦ ስ‫ץ‬ቅ-ኦ [ስ‫בּץ‬ ] ٤ָ- -ኦٍ ٤ָ-ኦٍ [٤ֹٍ] ውጥ- -ኤُ ውጥ-ኤُ [ውጤُ] ፍֳግ- -ኣ ፍֳግ-ኣ [ፍֳጋ] ናፍቅ- -ኦُ ናፍቅ-ኦُ [ናፍ‫]ُבּ‬ ድ‫ץ‬ግ- -ኢُ ድ‫ץ‬ግ-ኢُ [ድ‫ץ‬ጊُ] ‫וֹר‬ክ- -ኢ ‫וֹר‬ክ-ኢ [‫וֹר‬ኪ] ዝ‫ץ‬ፍ- -ኢያ ዝ‫ץ‬ፍ-ኢያ [ዝ‫ץ‬ፊያ] ጠ‫ושׂ‬- -ኤٍ ጠ‫ושׂ‬-ኤٍ [ጠ‫]ٍהשׂ‬ -ְድ ‫א‬- ‫א‬-ְድ [‫ְא‬ድ] -ٍ Stem-like Verb (Examples) Morpheme Derived Noun ዝ‫ו‬- -ٍ ዝ‫ו‬-ٍ [ዝ‫]ٍו‬ ደስ- -ٍ ደስ-ٍ [ደስٍ]
Noun (Examples) Morpheme Derived Noun ָጅ -ነُ ָጅ-ነُ [ָጅነُ] እግ‫ץ‬ -ኧኛ እግ‫ץ‬-ኧኛ [እግ‫נ‬ኛ ] ክብ‫ץ‬ -ኧُ ክብ‫ץ‬-ኧُ [ክብ‫]ُנ‬ ከ‫דـ‬ -ኤ ከ‫דـ‬ -ኤ [ከ‫]הـ‬ ጢ‫ו‬ -ኦ ጢ‫ו‬-ኦ [ጢ‫]ז‬ ኢُዮጵያ -ኣዊ ኢُዮጵያ-ኣዊ [ኢُዮጵያዊ] እንግֵዝ -ኛ እንግֵዝ-ኛ [እንግֵዝኛ] ኧ and ኦ Classes of Compound Words Example Derived Noun Noun + Noun ብ‫ُנ‬ + ‫ו‬ጣድ ብ‫ُנ‬ ‫ו‬ጣድ Noun + [ኧ] + Noun ቤُ + [ኧ] + ‫א‬ንግስُ ቤ‫ـ‬ ‫א‬ንግስُ Noun + Verbal Stems ָብ + ወֳድ- ָብ ወֳድ Verbal Stem + [ኦ] + Verbal Stem ‫ُץר‬- + [ኦ] + አደ‫ץ‬- ‫ِץר‬ አደ‫ץ‬ Verbal Stem + [ኦ] + Noun ‫ُץר‬- + [ኦ] + አዳ‫ע‬ ‫ِץר‬ አዳ‫ע‬
Amharic nouns can be marked for: i. Number by affixation of morphemes (and vowel changes) or repetition of words Noun in Singular Form (Examples) Description of the Noun Morpheme Plural Form Ending with consonant - - [ ] Ending with vowel - Personal Pronoun - - [ ] Proper Noun - Plural formation by repetition - - [ ] Loanwords from Geez (do not have similar patterns for plural formation) ii. Definiteness by affixation of morphemes or vowels based on number, gender, and/or ending of the noun. Indefinite Noun (Examples) Ending of the Noun Number Gender Definite Noun Feminine - [ ] / - [ ] Singular Masculine - [ ] Consonant Plural - [ ] Feminine - [ ] / - [ ] Singular Masculine - [ ] Vowel Plural - [ ]
iii. Gender by affixation of the morpheme - , e.g. --> - [ ] iv. Case (a) Objective case by affixation of the morpheme - , e.g. (subjective case) --> - [ ] (b) Possessive case by affixation of morphemes or vowels based on person, number, gender, and/or ending of the noun (personal pronouns by prefixing -, e.g. --> - [ / ]) Subjective Case (Examples) Ending of the Noun Person Number Gender Possessive Case Singular - [ ] First Plural - [ ] Masculine - [ ] Singular Feminine - [ ] Second Plural - [ ] Masculine - [ ] Singular Feminine - [ ] īlj Ending with consonant Third Plural - [ ] Singular - [ ] First Plural - [ ] Masculine - [ ] Singular Feminine - [ ] Second Plural - [ ] Masculine - [ ] Singular Feminine - [ ] Ending with vowel Third Plural - [ ]
Amharic adjectives can be derived from: i. Verbal Roots by infixing vowels between consonants (C) as shown below ii. Nouns by suffixing bound morphemes iii. Stems by suffixing bound morphemes iv. Compound Words of nouns and adjectives by affixing the vowel -ኧ e.g. ሆድ ሰፊ --> ሆድ-ኧ ሰፊ [ሆደ ሰፊ] Verbal Root (Examples) Pattern of Derivation Derived Adjective ድ-ር-ቅ CኧCኧC ድኧርኧቅ [ደረቅ] ጥ-ቅ-ር CECUC ጥEቅUር [ጥቁር] ጥ-ብ-ብ CኧC1C1IC ጥኧብIብ [ጠቢብ] ፍ-ጥ-ን CኧC1C1ኣC ፍኧጥኣን [ፈጣን] Noun (Examples) Morpheme Derived Adjective ነገር -ኧኛ ነገር-ኧኛ [ነገረኛ] ተራራ -ኣማ ተራራ-ኣማ [ተራራማ] ፈርስ -ኣም ፈርስ-ኣም [ፈርሳም] ህዝብ -ኣዊ ህዝብ-ኣዊ [ህዝባዊ] Stems (Examples) Morpheme Derived Adjective ደካም- -ኣ ደካም-ኣ [ደካማ] ንቅ- -U ንቅ-U [ንቁ] በል- -Iታ በል-Iታ [በሊታ]
Amharic adjectives can be marked for: i. Number by affixation of morphemes or repetition of consonants (and affixing the vowel - ) Adjective in Singular Form (Examples) Description of the Adjective Morpheme Plural Form Ending with consonant - - [ ] Ending with vowel - - [ăȀ ň] Plural formation by repetition of consonant - - [ ] ii. Definiteness by affixation of morphemes or vowels based on number, gender, and/or ending of the adjective. Indefinite Adjective (Examples) Ending of the Adjective Number Gender Definite Adjective Feminine - [ ] / - [ ] Singular Masculine - [ ] Consonant Plural - [ ] Feminine - [ ] / - [ ] Singular Masculine - [ ] Vowel Plural - [ ] iii. Gender by affixation of the morpheme - , e.g. --> - [ ] iV. Case (Objective Case) by affixation of the morpheme - , e.g. --> - [ ]
Amharic verbal stems (from which various forms of verbs are formed) can be derived from: i. Verbal Roots by (a) affixing the vowel - - to produce C C1C1 C-, e.g. - - --> - [ -] (b) repeating penultimate consonants and affixing the vowels - - and - - to produce C C1 C1C1 C-, e.g. - - --> - [ -] ii. Verbal Stems by affixing morphemes Verbal Stem (Examples) Morpheme Derived Verbal Stem - - - - [ -] - - - - [ -] - - - - [ -] iii. Compound Words of (a) stems and verbs, e.g. - + --> (b) sub-words and verbs, e.g. + -->
Amharic verbs are marked for: i. Person, gender, number, case, and tense/ / / /aspect Singular Plural Person (Subjective Case) Gender Past Tense Non-Past Tense Past Tense Non-Past Tense First - /- - - - Masculine - /- - - - - Second Feminine - - - - - - Masculine - - - - - Third Feminine - - - - - Objective Case Tense Subjective Case Person Gender Singular Plural First - - - - Masculine - - /- Second Feminine - - - - Masculine - - Third Person, Singular, Masculine Third Feminine - - - - First - - - - Masculine - - Second Feminine - - - - Masculine - - Past Tense Third Person, Singular, Feminine Third Feminine - - -- - . . . etc . . . etc . . . etc . . . etc . . . etc . . . etc
ii. Mood Mood Number Person Gender Completed Action Command Request Negative First - /- - - - - Masculine - /- - - - Second Feminine - - - - - - - Masculine - - - - - Singular Third Feminine - - - - - First - - - - - Second - - - - - - - Plural Third - - - - - - - - Amharic verbs in general show high degree of inflection since person, case, gender, number, tense, aspect, mood and others are marked on the verb. For example, indicates: ™ the subject (third person, masculine, singular) ™ the object (first person, plural) ™ negation … ™ past tense
• State machines are widely used in NLP for modeling phonology, morphology and syntax. • State machines are formal models that consist of states, transitions among states, and an input representation. ♦ States – represent the set of properties of an abstract machine ♦ Transitions – represent jumps from one state to another ♦ Inputs – sequences of symbols or letters that can be read by the machine • A machine with finite number of states is called finite state machine (FSM). • FSM has two special states: start state and final state. • There are two types of FSMs: finite state automata and finite state transducers. S0 S2 0 0 1 1 Input symbol Final state Transition Start state S1 1
• Finite state automaton (FSA) is finite state machine that only accepts a set of given strings (a language). • FSA can be deterministic or non-deterministic. • In deterministic FSA, every state has one transition for each possible input. ♦ Example: A deterministic FSA that determines if a binary string contains an even number of 0's. ♦ Strings accepted by this deterministic FSA are: ε, 1, 11, 111, 00, 010, 1010, 10110, etc. S1 0 0 1 S2 1 S0 ε
• In non-deterministic FSA, an input can lead to one, more than one or no transition for a given state. ♦ Example: A non-deterministic FSA that determines if a binary string contains an even number of 0’s or an even number of 1’s. ♦ Strings accepted by this non-deterministic FSA are: ε, 1, 11, 111, 00, 010, 1010, 10110, 011, 11011, 1010101, etc. S1 0 0 1 S2 1 S0 ε S3 1 1 0 S4 0 ε
• FSAs can be used to recognize words in a language. • Examples: ♦ Single word recognition S0 S1 ሰ S2 S3 በ ረ S0 S1 w S2 S3 a l S4 k S0 S1 ሰበረ S0 S1 walk
♦ Recognition of multiple words S0 ሰበ S1 ብ ረ S2 ቀ e tern in al S4 eth c i anol S5 opia S3 S2 S0 S1 ሰበረ, ሰበቀ, ሰበብ internal, eternal, ethical, ethiopia, ethanol
♦ Recognition of multiple words (for instance, Amharic pronouns: Eኔ, Eኛ, Aንተ, Aንቺ, Eናንተ, Eስዎ, Eርስዎ, Eሱ, Eርሱ, Eሷ, Eርሷ, Eሳቸው, Eርሳቸው, Eነሱ, Eነርሱ) E Aን ሷ ቺ ር ሱ S0 S3 S6 S4 S2 S5 ተ E ሳቸው ስዎ ኔ ኛ ናነተ ነ ር ነ ሱ S1
• One word and multiple inflections S0 walk S1 S2 s ed ing S2 ሰበር S1 ኧን ኧህ ኣት ኧው ኣቸው ኧኝ ኧሽ ኣችሁ ኣችሁት . . . . . . S0
• Multiple words and multiple inflections S0 walk S1 S2 s ed ing jump help . . . . . . S2 ሰበር S1 ኧን ኧህ ኣት ኧው ኣቸው ኧኝ ኧሽ ኣችሁ ኣችሁት . . . . . . S0 ገደል ማረክ . . . . . .
• One word and multiple inflections with affixes S3 ሰብር S0 S2 Eንዲ Eንዳይ ከሚ ሊ የሚ ኧን ህ ኣት ኧው ኣቸው ብን በት ለት ባቸው . . . . . . . . . . . . S1
• Multiple words and multiple inflections with affixes S3 ሰብር S0 S2 Eንዲ Eንዳይ ከሚ ሊ የሚ ኧን ህ ኣት ኧው ኣቸው ብን በት ለት ባቸው . . . . . . . . . . . . S1 ማርክ ገድል . . . . . .
• Marking part-of-speech S0 [word] y S3 cate S2 S1 S5 ion ism ist er y S4
• Marking part-of-speech S0 [word] y Adj cate N N V ion ism ist er y N
• Collect words in a large corpus and compile into a trie data structure: ...walk walked walking walks wall walls want wanted wanting wants warn warned warning warns ... w a l l k e s d i n g s e d s i n g d n g i s e n r n t
. . . Eንደሚሰብረው Eንደሚሰብሩበት Eንደሚሰብሩባቸው Eንደሚሰብሩት Eንደሚሰብር Eንደሚገድለው Eንደሚገድሉበት Eንደሚገድሉባቸው Eንደሚገድሉት Eንደሚገድል Eንደማይሰብረው Eንደማይሰብሩበት Eንደማይሰብሩባቸው Eንደማይሰብሩት Eንደማይሰብር Eንደማይገድለው Eንደማይገድሉበት Eንደማይገድሉባቸው Eንደማይገድሉት Eንደማይገድል . . . Eንደ ሚ ማይ ሰብር ሰብር ገድል ገድል ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት
• Identify frequent suffix trees Discovered Morphology • Stems - with common suffix tree: ♦ walk ♦ want ♦ warn • Morphemes - frequent suffix tree: ♦ ε ♦ – ed ♦ – s ♦ – ing w a l l k e s d i n g s e d s i n g d n g i s e n r n t
Discovered Morphology • Stems - with common suffix tree: ♦ ሰብር ♦ ገድል • Morphemes - frequent suffix tree: ♦ ε ♦ – ኧው ♦ – Uበት ♦ – Uባቸው ♦ – Uት • Other affixes: ♦ – Eንደ ♦ – ሚ – ♦ – ማይ – Eንደ ሚ ማይ ሰብር ሰብር ገድል ገድል ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት

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NLP_Chapter #2 Morphological Analysis.pdf

  • 1. COSC 6405 2018/19 Sem I Morphological Analysis
  • 3. ָጅ ָጅነُ ቤُ ቤِ٤ [ቤ(ُኦ)٤] ቤُ ከቤُ ነُ ָጅነُ ኦ٤ ቤِ٤ [ቤ(ُኦ)٤] ከ ከቤُ
  • 4. ስብ‫ץ‬ ‫ר‬በ‫ץ‬ ስብ‫ץ‬ ‫רـ‬በ‫נ‬٤ -ና- ُናን ُን አָ…ኧ‫ו‬ አָ‫ר‬በ‫ונ‬ [አָ‫ר‬በ(‫ץ‬ኧ)‫ו‬]
  • 10. Positive Comparative Superlative good better best bad worse worst little less least much many more most
  • 13. Verbal Root (Examples) Pattern of Derivation Derived Noun ጥ-ቅ-‫ו‬ CእCእC ጥእቅእ‫ו‬ [ጥቅ‫]ו‬ ‫ו‬-‫ץ‬-ُ CእCC ‫ו‬እ‫ُץ‬ [‫]ُץו‬ ‫ו‬-ָ-ስ CኧCC ‫ו‬ኧָስ [‫ָא‬ስ] ን-ግ-‫ץ‬ CኧCኧC ንኧግኧ‫ץ‬ [ነገ‫]ץ‬ ድ-ክ-‫ו‬ CእCኣC ድእክኣ‫ו‬ [ድካ‫]ו‬ ֱ-‫ו‬-‫ו‬ CእCኧC ֱእ‫ו‬ኧ‫ו‬ [ֱ‫]וא‬ ግ-ብ-ዕ CእC ግእብ [ግብ] ጥ-ው-‫ו‬ CኦC ጥኦ‫ו‬ [ጦ‫]ו‬ ቅ-ው-‫ץ‬-ጥ CኡCC ቅኡ‫ץ‬ጥ [‫ץשּׁ‬ጥ] ድ-ብብ-ቅ CእC1C1እC ድእብብእቅ [ድብቅ] Adjective (Examples) Morpheme Derived Noun ደግ -ነُ ደግ-ነُ [ደግነُ] ቅ‫ץ‬ብ -ኧُ ቅ‫ץ‬ብ-ኧُ [ቅ‫ץ‬በُ] ብֱָ -ኣُ ብֱָ-ኣُ [ብָሃُ] ብָጥ -ኦ ብָጥ-ኦ [ብָጦ]
  • 14. Stem (Examples) Morpheme Derived Noun ው‫ץ‬ድ- -ኧُ ው‫ץ‬ድ-ኧُ [ው‫ץ‬ደُ] ቅዳስ- -ኤ ቅዳስ-ኤ [ቅዳሴ] እ‫ץ‬ጅ- -እና እ‫ץ‬ጅ-እና [እ‫ץ‬ጅና] ָ‫ו‬- -ኣُ ָ‫ו‬-ኣُ [ ָ‫]ُד‬ ስ‫ץ‬ቅ- -ኦ ስ‫ץ‬ቅ-ኦ [ስ‫בּץ‬ ] ٤ָ- -ኦٍ ٤ָ-ኦٍ [٤ֹٍ] ውጥ- -ኤُ ውጥ-ኤُ [ውጤُ] ፍֳግ- -ኣ ፍֳግ-ኣ [ፍֳጋ] ናፍቅ- -ኦُ ናፍቅ-ኦُ [ናፍ‫]ُבּ‬ ድ‫ץ‬ግ- -ኢُ ድ‫ץ‬ግ-ኢُ [ድ‫ץ‬ጊُ] ‫וֹר‬ክ- -ኢ ‫וֹר‬ክ-ኢ [‫וֹר‬ኪ] ዝ‫ץ‬ፍ- -ኢያ ዝ‫ץ‬ፍ-ኢያ [ዝ‫ץ‬ፊያ] ጠ‫ושׂ‬- -ኤٍ ጠ‫ושׂ‬-ኤٍ [ጠ‫]ٍהשׂ‬ -ְድ ‫א‬- ‫א‬-ְድ [‫ְא‬ድ] -ٍ Stem-like Verb (Examples) Morpheme Derived Noun ዝ‫ו‬- -ٍ ዝ‫ו‬-ٍ [ዝ‫]ٍו‬ ደስ- -ٍ ደስ-ٍ [ደስٍ]
  • 15. Noun (Examples) Morpheme Derived Noun ָጅ -ነُ ָጅ-ነُ [ָጅነُ] እግ‫ץ‬ -ኧኛ እግ‫ץ‬-ኧኛ [እግ‫נ‬ኛ ] ክብ‫ץ‬ -ኧُ ክብ‫ץ‬-ኧُ [ክብ‫]ُנ‬ ከ‫דـ‬ -ኤ ከ‫דـ‬ -ኤ [ከ‫]הـ‬ ጢ‫ו‬ -ኦ ጢ‫ו‬-ኦ [ጢ‫]ז‬ ኢُዮጵያ -ኣዊ ኢُዮጵያ-ኣዊ [ኢُዮጵያዊ] እንግֵዝ -ኛ እንግֵዝ-ኛ [እንግֵዝኛ] ኧ and ኦ Classes of Compound Words Example Derived Noun Noun + Noun ብ‫ُנ‬ + ‫ו‬ጣድ ብ‫ُנ‬ ‫ו‬ጣድ Noun + [ኧ] + Noun ቤُ + [ኧ] + ‫א‬ንግስُ ቤ‫ـ‬ ‫א‬ንግስُ Noun + Verbal Stems ָብ + ወֳድ- ָብ ወֳድ Verbal Stem + [ኦ] + Verbal Stem ‫ُץר‬- + [ኦ] + አደ‫ץ‬- ‫ِץר‬ አደ‫ץ‬ Verbal Stem + [ኦ] + Noun ‫ُץר‬- + [ኦ] + አዳ‫ע‬ ‫ِץר‬ አዳ‫ע‬
  • 16. Amharic nouns can be marked for: i. Number by affixation of morphemes (and vowel changes) or repetition of words Noun in Singular Form (Examples) Description of the Noun Morpheme Plural Form Ending with consonant - - [ ] Ending with vowel - Personal Pronoun - - [ ] Proper Noun - Plural formation by repetition - - [ ] Loanwords from Geez (do not have similar patterns for plural formation) ii. Definiteness by affixation of morphemes or vowels based on number, gender, and/or ending of the noun. Indefinite Noun (Examples) Ending of the Noun Number Gender Definite Noun Feminine - [ ] / - [ ] Singular Masculine - [ ] Consonant Plural - [ ] Feminine - [ ] / - [ ] Singular Masculine - [ ] Vowel Plural - [ ]
  • 17. iii. Gender by affixation of the morpheme - , e.g. --> - [ ] iv. Case (a) Objective case by affixation of the morpheme - , e.g. (subjective case) --> - [ ] (b) Possessive case by affixation of morphemes or vowels based on person, number, gender, and/or ending of the noun (personal pronouns by prefixing -, e.g. --> - [ / ]) Subjective Case (Examples) Ending of the Noun Person Number Gender Possessive Case Singular - [ ] First Plural - [ ] Masculine - [ ] Singular Feminine - [ ] Second Plural - [ ] Masculine - [ ] Singular Feminine - [ ] īlj Ending with consonant Third Plural - [ ] Singular - [ ] First Plural - [ ] Masculine - [ ] Singular Feminine - [ ] Second Plural - [ ] Masculine - [ ] Singular Feminine - [ ] Ending with vowel Third Plural - [ ]
  • 18. Amharic adjectives can be derived from: i. Verbal Roots by infixing vowels between consonants (C) as shown below ii. Nouns by suffixing bound morphemes iii. Stems by suffixing bound morphemes iv. Compound Words of nouns and adjectives by affixing the vowel -ኧ e.g. ሆድ ሰፊ --> ሆድ-ኧ ሰፊ [ሆደ ሰፊ] Verbal Root (Examples) Pattern of Derivation Derived Adjective ድ-ር-ቅ CኧCኧC ድኧርኧቅ [ደረቅ] ጥ-ቅ-ር CECUC ጥEቅUር [ጥቁር] ጥ-ብ-ብ CኧC1C1IC ጥኧብIብ [ጠቢብ] ፍ-ጥ-ን CኧC1C1ኣC ፍኧጥኣን [ፈጣን] Noun (Examples) Morpheme Derived Adjective ነገር -ኧኛ ነገር-ኧኛ [ነገረኛ] ተራራ -ኣማ ተራራ-ኣማ [ተራራማ] ፈርስ -ኣም ፈርስ-ኣም [ፈርሳም] ህዝብ -ኣዊ ህዝብ-ኣዊ [ህዝባዊ] Stems (Examples) Morpheme Derived Adjective ደካም- -ኣ ደካም-ኣ [ደካማ] ንቅ- -U ንቅ-U [ንቁ] በል- -Iታ በል-Iታ [በሊታ]
  • 19. Amharic adjectives can be marked for: i. Number by affixation of morphemes or repetition of consonants (and affixing the vowel - ) Adjective in Singular Form (Examples) Description of the Adjective Morpheme Plural Form Ending with consonant - - [ ] Ending with vowel - - [ăȀ ň] Plural formation by repetition of consonant - - [ ] ii. Definiteness by affixation of morphemes or vowels based on number, gender, and/or ending of the adjective. Indefinite Adjective (Examples) Ending of the Adjective Number Gender Definite Adjective Feminine - [ ] / - [ ] Singular Masculine - [ ] Consonant Plural - [ ] Feminine - [ ] / - [ ] Singular Masculine - [ ] Vowel Plural - [ ] iii. Gender by affixation of the morpheme - , e.g. --> - [ ] iV. Case (Objective Case) by affixation of the morpheme - , e.g. --> - [ ]
  • 20. Amharic verbal stems (from which various forms of verbs are formed) can be derived from: i. Verbal Roots by (a) affixing the vowel - - to produce C C1C1 C-, e.g. - - --> - [ -] (b) repeating penultimate consonants and affixing the vowels - - and - - to produce C C1 C1C1 C-, e.g. - - --> - [ -] ii. Verbal Stems by affixing morphemes Verbal Stem (Examples) Morpheme Derived Verbal Stem - - - - [ -] - - - - [ -] - - - - [ -] iii. Compound Words of (a) stems and verbs, e.g. - + --> (b) sub-words and verbs, e.g. + -->
  • 21. Amharic verbs are marked for: i. Person, gender, number, case, and tense/ / / /aspect Singular Plural Person (Subjective Case) Gender Past Tense Non-Past Tense Past Tense Non-Past Tense First - /- - - - Masculine - /- - - - - Second Feminine - - - - - - Masculine - - - - - Third Feminine - - - - - Objective Case Tense Subjective Case Person Gender Singular Plural First - - - - Masculine - - /- Second Feminine - - - - Masculine - - Third Person, Singular, Masculine Third Feminine - - - - First - - - - Masculine - - Second Feminine - - - - Masculine - - Past Tense Third Person, Singular, Feminine Third Feminine - - -- - . . . etc . . . etc . . . etc . . . etc . . . etc . . . etc
  • 22. ii. Mood Mood Number Person Gender Completed Action Command Request Negative First - /- - - - - Masculine - /- - - - Second Feminine - - - - - - - Masculine - - - - - Singular Third Feminine - - - - - First - - - - - Second - - - - - - - Plural Third - - - - - - - - Amharic verbs in general show high degree of inflection since person, case, gender, number, tense, aspect, mood and others are marked on the verb. For example, indicates: ™ the subject (third person, masculine, singular) ™ the object (first person, plural) ™ negation … ™ past tense
  • 23. • State machines are widely used in NLP for modeling phonology, morphology and syntax. • State machines are formal models that consist of states, transitions among states, and an input representation. ♦ States – represent the set of properties of an abstract machine ♦ Transitions – represent jumps from one state to another ♦ Inputs – sequences of symbols or letters that can be read by the machine • A machine with finite number of states is called finite state machine (FSM). • FSM has two special states: start state and final state. • There are two types of FSMs: finite state automata and finite state transducers. S0 S2 0 0 1 1 Input symbol Final state Transition Start state S1 1
  • 24. • Finite state automaton (FSA) is finite state machine that only accepts a set of given strings (a language). • FSA can be deterministic or non-deterministic. • In deterministic FSA, every state has one transition for each possible input. ♦ Example: A deterministic FSA that determines if a binary string contains an even number of 0's. ♦ Strings accepted by this deterministic FSA are: ε, 1, 11, 111, 00, 010, 1010, 10110, etc. S1 0 0 1 S2 1 S0 ε
  • 25. • In non-deterministic FSA, an input can lead to one, more than one or no transition for a given state. ♦ Example: A non-deterministic FSA that determines if a binary string contains an even number of 0’s or an even number of 1’s. ♦ Strings accepted by this non-deterministic FSA are: ε, 1, 11, 111, 00, 010, 1010, 10110, 011, 11011, 1010101, etc. S1 0 0 1 S2 1 S0 ε S3 1 1 0 S4 0 ε
  • 26. • FSAs can be used to recognize words in a language. • Examples: ♦ Single word recognition S0 S1 ሰ S2 S3 በ ረ S0 S1 w S2 S3 a l S4 k S0 S1 ሰበረ S0 S1 walk
  • 27. ♦ Recognition of multiple words S0 ሰበ S1 ብ ረ S2 ቀ e tern in al S4 eth c i anol S5 opia S3 S2 S0 S1 ሰበረ, ሰበቀ, ሰበብ internal, eternal, ethical, ethiopia, ethanol
  • 28. ♦ Recognition of multiple words (for instance, Amharic pronouns: Eኔ, Eኛ, Aንተ, Aንቺ, Eናንተ, Eስዎ, Eርስዎ, Eሱ, Eርሱ, Eሷ, Eርሷ, Eሳቸው, Eርሳቸው, Eነሱ, Eነርሱ) E Aን ሷ ቺ ር ሱ S0 S3 S6 S4 S2 S5 ተ E ሳቸው ስዎ ኔ ኛ ናነተ ነ ር ነ ሱ S1
  • 29. • One word and multiple inflections S0 walk S1 S2 s ed ing S2 ሰበር S1 ኧን ኧህ ኣት ኧው ኣቸው ኧኝ ኧሽ ኣችሁ ኣችሁት . . . . . . S0
  • 30. • Multiple words and multiple inflections S0 walk S1 S2 s ed ing jump help . . . . . . S2 ሰበር S1 ኧን ኧህ ኣት ኧው ኣቸው ኧኝ ኧሽ ኣችሁ ኣችሁት . . . . . . S0 ገደል ማረክ . . . . . .
  • 31. • One word and multiple inflections with affixes S3 ሰብር S0 S2 Eንዲ Eንዳይ ከሚ ሊ የሚ ኧን ህ ኣት ኧው ኣቸው ብን በት ለት ባቸው . . . . . . . . . . . . S1
  • 32. • Multiple words and multiple inflections with affixes S3 ሰብር S0 S2 Eንዲ Eንዳይ ከሚ ሊ የሚ ኧን ህ ኣት ኧው ኣቸው ብን በት ለት ባቸው . . . . . . . . . . . . S1 ማርክ ገድል . . . . . .
  • 33. • Marking part-of-speech S0 [word] y S3 cate S2 S1 S5 ion ism ist er y S4
  • 34. • Marking part-of-speech S0 [word] y Adj cate N N V ion ism ist er y N
  • 35. • Collect words in a large corpus and compile into a trie data structure: ...walk walked walking walks wall walls want wanted wanting wants warn warned warning warns ... w a l l k e s d i n g s e d s i n g d n g i s e n r n t
  • 37. • Identify frequent suffix trees Discovered Morphology • Stems - with common suffix tree: ♦ walk ♦ want ♦ warn • Morphemes - frequent suffix tree: ♦ ε ♦ – ed ♦ – s ♦ – ing w a l l k e s d i n g s e d s i n g d n g i s e n r n t
  • 38. Discovered Morphology • Stems - with common suffix tree: ♦ ሰብር ♦ ገድል • Morphemes - frequent suffix tree: ♦ ε ♦ – ኧው ♦ – Uበት ♦ – Uባቸው ♦ – Uት • Other affixes: ♦ – Eንደ ♦ – ሚ – ♦ – ማይ – Eንደ ሚ ማይ ሰብር ሰብር ገድል ገድል ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት ኧው Uበት Uባቸው Uት