1. Part-of-Speech
Tagging
Alkhalaf.H , Alotaibi.S , Alruhaili.Sh

2. Outline:
• Introduction
• Methods
• Constructing An Automatic Lexicon for Arabic Language.
• APT: Arabic Part-of-speech Tagger.
• The HMM-Based POS Tagger.
• The Stemmer
• The POS Tagger
• Results
• Constructing An Automatic Lexicon for Arabic Language.
• APT: Arabic Part-of-speech Tagger.
• The HMM-Based POS Tagger.
• Conclusion

3. Introduction:
* Arabic language
• Arabic is the language of millions of people all
over the world For that Interest in the Arabic
language is growing fast.
• Language processing tools for Arabic are yet to
achieve the quality and robustness.
• So far not been covered enough and still fertile
field.

4. In the study of languages
• Corpus Linguistics refers to a methodology
which governs a natural language by developing
it through a set of theoretical and abstract rules
• Corpus Linguistics, originally done by hand, are
now performed by an automated process using
algorithms in software applications

5. Part-of-Speech Tagging (POS tagging or
POST)
• Part of the Annotation method in the Corpus
Linguistics is the process of assigning a part-of-
speech to each word in a sentence as well as its
context in relationship with adjacent and related words
in a phrase, sentence, or paragraph
• A simplified form of this is commonly associated with
the identification of words as
nouns, verbs, adjectives, adverbs, etc.

6. The Arabic verbal structures are composed
of three classes
• Noun: It is either a name or a word that
describes a person, thing or idea.
• Verb: It is a word that denotes an action and
could be combined with some particles.
• Particle: This class includes everything that is
neither a verb nor a noun, prepositions of
coordination, conjunction.

7. APT: Arabic Part-of-speech Tagger
Previously
Word
Search in lexicon
Found ?
yes no
Assign all tag
possible
Not assign any
tag
Methodology:

8. NOW
APT: Arabic Part-of-speech Tagger (cont.)
Word
Search root in lexicon
There is more of
a tag or did not
find any tag ?
Stemming
yes no
Assign tag by affixes Tagging

9. APT: Arabic Part-of-speech Tagger (cont.)
Results:

10. APT: Arabic Part-of-speech Tagger (cont.)
• The statistical tagger achieved an accuracy of
around 90% when disambiguating ambiguous
words with this tagset.

11. Constructing An Automatic Lexicon for Arabic Language
Methodology:

12. Constructing An Automatic Lexicon for
Arabic Language (cont.)
•When calculating the efficiency errors were
ignored of stemming process.
• The algorithm extracts the only triple roots.
%
Total
%
correct
words
incorrect
words
#
correct
words
#
Incorrect
words
# word
96.50%96.50%3.50%30211313
Results:

13. The HMM-Based POS Tagger

14. The Tokenizer
• Since punctuation marks need to be tagged; it tags
them as PUNC by pass them to the POS tagger.
• The purpose of the tokenization phase is to go
through some pre-processing steps in order to
prepare the input text for the remaining modules.
• HMM POS Tagger architecture developed a tokenizer
to separate the punctuation marks from the words.
Then the tokenizer converts the input text into a list
of words using the space as a delimiter. The
resulting list is passed to the stemme.

15. The Stemmer
• Stemming is the process of segmenting and
separating affixes from a stem to produce prefix,
stem, and suffix parts.

16. The Stemmer (cont.)

17. The POS Tagger
• HMM model ( The POS tagger) has been built by
constructing the trigram language models.

18. The POS Tagger (cont.)

19. The HMM-Based POS Tagger
• F-measure :
[2 x Precision x Recall] / [Precision + Recall]
where Precision = Ncorrect / Nresponse
and Recall = Ncorrect / Nkey

20. The HMM-Based POS Tagger (cont.)
• The performance of the POS tagger decreased
to55 % when it was used to tag a non-stemmed
text.
• Using F-measure ;The HMM tagger achieved
97 %.

21. Conclusion
• Part of speech (PoS) tagging are very important and
basic applications of Natural Language Processing
• In this paper we highlighted the importance of part
of speech tagging in wide range of NLP applications
.
• We have display the most important technologies
interested in POS used so far for part of speech
taggers for Arabic text from several papers.

22. Thanks..