Fsmnlp presentation 02

Handling Unknown Words in Arabic FST

Morphology

Khaled Shaalan and Mohammed Attia

Faculty of Engineering and IT,

The British University in Dubai

Presented by

Younes Samih

Heinrich-Heine-Universität, Germany

Bird’s Eye view

Problem

• Out of Vocabulary words (OOV) cause a problem to

morphological analysers, parsers, MT, etc.

• The manual extension of lexical databases is costly an time

consuming.

• With the large amount of data, manual extension of lexicons

becomes practically impossible.

Solution

• Creating an automatic method for updating a lexical database

• Integrating a Machine Learning method with a finite state

guesser to lemmatize unknown words

• Weighting new words by relevance and importance

Outline

• Introduction

• Morphological Guesser

• Methodology

• Testing and Evaluation

• Conclusion

Introduction

• Why deal with unknown words?

• Complexity of lemmatization in Arabic

• Data used

Introduction

Why deal with unknown words?

• Language is always changing

• New words appear

• Old words disappear

• Unknown words make up 29% of the Gigaword

corpus

• Unknown words (OOV) always cause a problem to:

• Morphological analysers

• Parsers

• Machine Translation & other applications

Introduction

Complexity of lemmatization in Arabic

• Lemmatization means reducing words to their base

(canonical) forms

• played -> play studies - study

• went -> go wives -> wife

• New words in English appear in their base form 86% of

the time (Lindén, 2008)

• New words in Arabic appear in their base form 45% of

the time

• Arabic morphology is complex and semi-algorithmic:

root, patterns, inflections, clitics, etc.

Proclitics Prefix Lemma Suffix Enclitic

Conjunction/

question article

Comp Tense/mood –

number/gend

Verb Tense/mood –

number/gend

Object

pronoun

Conjunctions و

wa ‘and’ or فfa ‘then’

li ‘to’ Imperfectiveل

tense (5)

lemma

Imperfective

tense (10)

First person

(2)

Question word أ

᾽a ‘is it true that’

sa ‘will’ Perfective tenseس

(1)

Perfective

tense (12)

Second

person (5)

la ‘then’ Imperative (2) Imperative (5) Third personل

(5)

Introduction

Complexity of lemmatization in Arabic

Possible Concatenations in Arabic Verbs

lemma

šakara ‘to thank’, generate شكر

2,552 valid forms

Proclitics lemma Suffix Enclitic

Conjunction/

question article

Preposition Definite

article

Noun Gender/Number Genitive

pronoun

Conjunctions و

wa ‘and’ or ف

fa ‘then’

,’bi ‘withب

’ka ‘asك

or لli ‘to’

’al ‘theال

Stem

Masculine Dual

(4)

First person

(2)

Feminine Dual

(4)

Question word أ

᾽a ‘is it true

that’

Masculine

regular plural

(4)

Second person

(5)

Feminine

regular plural

(1)

Third person

(5)

Feminine Mark

(1)

Introduction

Complexity of lemmatization in Arabic

Possible Concatenations in Arabic Nouns

lemma

mu῾allim ‘teacher’, generate 519 معلم

valid forms

Introduction

Data used

• A large-scale corpus of 1,089,111,204

words

• 85% from the Arabic Gigaword Fourth Edition

• 15% from news articles crawled from the Al-Jazeera

web site

Morphological Guesser

We develop a morphological guesser for

Arabic unknown words that handles all

possible

• Clitics

• Prefixes

• Suffixes

• And all relevant alteration operations that include

insertion, assimilation, and deletion

Guesser LEXC

======

LEXICON Conjunctions

+وـ conj:وـ Prepositions;

+فـ conj:فـ Prepositions;

Prepositions;

LEXICON Prepositions

+لـ prep:لـ Article;

+كـ prep:كـ Article;

+بـ prep:بـ Article;

Article;

LEXICON Article

+الـ defArt Nouns;

+الـ defArt Adjectives;

Nouns;

Adjectives;

LEXICON Nouns

+noun GuessWords;

^ss^ ^خادم se^ FemMascduMascpl;

....

LEXICON Adjectives +adj+fem GuessWords;

+adj+masc GuessWords;

^ss^ ^سعيد se^+adj+masc

FemMascduFemduMascplFempl;

....

LEXICON GuessWords ^ss^^GUESSNOUNSTEM^^se^

FemMascduFemduMascplFempl;

^ss^^GUESSNOUNSTEM^^se^

FemMascduFemduFempl;

^ss^^GUESSNOUNSTEM^^se^

FemMascduFemdu;

….

ALTERATION RULES

=================

a -> b || L _ R

XFST

=====

read regex < arb-Alphabet.txt

define Alphabet

define PossNounStem [[Alphabet]^{2,24}] "+Guess":0;

substitute defined PossNounStem for

"^GUESSNOUNSTEM^“

1

2

3

Methodology

We use a pipelined approach

• First: a machine learning (SVM), context-sensitive tool

(MADA) is used to predict:

• POS

• Morpho-syntactic features of number, gender, person, tense, etc.

• Second: The finite-state morphological guesser is used

to produce all the possible interpretations of words and

suggested lemmas.

• Third: The two output are matched together and the

agreed analysis is selected.

Methodology

Example قوَن والُمَسوِّ

wa-Al-musaw~iquwna “and-the-marketers”

MADA output:

form:wAlmswqwn num:p gen:m per:na case:n asp:na mod:na vox:na

pos:noun prc0:Al_detprc1:0 prc2:wa_conj prc3:0 enc0:0 stt:d

Finite-state guesser output:

@Guess+masc+pl+nom+والمسوقadj+ والمسوقون

@Guess+sg+والمسوقونadj+ والمسوقون

@Guess+masc+pl+nom+والمسوقnoun+ والمسوقون

@Guess+sg+والمسوقونnoun+ والمسوقون

@Guess+masc+pl+nom+مسوقdefArt@+adj+ال@conj+و والمسوقون

@Guess+sg+مسوقونdefArt@+adj+ال@conj+و والمسوقون

Guess+masc+pl+nom@ Correct Analysis+مسوقdefArt@+noun+ال@conj+و والمسوقون

@Guess+sg+مسوقونdefArt@+noun+ال@conj+و والمسوقون

@Guess+masc+pl+nom+المسوقconj@+adj+و والمسوقون

@Guess+sg+المسوقونconj@+adj+و والمسوقون

@Guess+masc+pl+nom+المسوقconj@+noun+و والمسوقون

@Guess+sg+المسوقونconj@+noun+و والمسوقون

Methodology

Results • Corpus size is 1,089,111,204 tokens, 7,348,173

types

• Unknown Types in the corpus: 2,116,180 (29%)

• After spell checking, correctly spelt types are

208,188

• Types with frequency of 10 or more: 40,277

• After lemmatization:18,399 types

Testing and Evaluation

We create a gold standard of 1,310 words

manually-annotated for:

• Gold lemma

• Gold POS

• Lexical relevance (include in a dictionary): yes or

no

Among unknown words,

- Proper nouns are the most common

- Verbs are the least common

Gold POS Type Count Ratio

noun_prop 584 45%

noun 264 20%

adj 255 19%

verb 52 4%

Testing and Evaluation

Evaluating POS (accuracy)

• Baseline: The most frequent tag (proper name)

for all unknown words: 45%

• Mada: 60%

• Voted POS Tagging: 69%. When a lemma gets a

different POS tag with a higher frequency we

take the higher Accuracy

POS tagging

1 POS Tagging baseline 45%

2 MADA POS tagging 60%

3 Voted POS Tagging 69%

Testing and Evaluation

Evaluating Lemmatization (accuracy)

• Baseline: new words appear in their base form:

45%

• Pipelined strict definite article ‘al’: 54%

• Pipelined ignoring definite article ‘al’: 63%

Lemmatization

1 Lemma first-order baseline 45%

2 Pipelined lemmatization (first-

order decision) with strict

definite article matching

54%

3 Pipelined lemmatization (first-

order decision) ignoring definite

article matching

63%

Testing and Evaluation

Evaluating Lemma Weighting

• The weighting criteria aims to push lexicographically

relevant words up the list and less interesting words down.

• We aim to make the number of important words high in the

top 100 and low in the bottom 100

Word Weight = ((number of

sister forms * 800) +

frequencies of sister forms) / 2 +

POS factor

Good words In top

100

In bottom

100

relying on Frequency

alone (baseline)

63 50

relying on number of

sister forms * 800

87 28

relying on POS factor 58 30

using combined criteria 78 15

Conclusion

• We develop a methodology for automatically extracting

and lemmatizing unknown words in Arabic

• We pipeline a finite-state guesser with a machine

learning tool for lemmatization

• We develop a weighting mechanism for predicting the

relevance and importance of lemmas

• Out of 2,116,180 unknown words, we create a lexicon of

18,399 lemmatized, POS-tagged and weighted entries.