Arabic Part of Speech Tagger

Synopsis

Part of speech tagging is the process of selecting the most likely sequence of syntactic categories for the words in a sentence. It determines grammatical characteristics of the words, such as part of speech, grammatical number, gender, person, etc. In the case of Arabic language, this task is not trivial since most of the words are ambiguous as a result of the absence of vowels.

For each word, we want at a minimum to identify its main lexical category (noun, verb etc.) and inflectional features (plural, past tense etc.) if any. We might also identify some quasi-semantic features (proper noun) or even specify a word sense relative to some lexicon.

Information

Technical Specifications Web application Download MAPS Main Page

This article is best reviewed with its accompanied paper.

View paper

Reference: MSLTAG

Last updated: 15/1/2023

Oveview

Kalmasoft PoS Tagger is the answer to most of the problems related to Arabic corpus tagging, a context-sensitive rule-based solution hand-crafted set of comprehensive syntactic rules to deal with Arabic datasets, the output is a structured XML or JSON format but SQL database and CSV are among the other alternatives.

Kalmasoft PoS Tagger is designed to prepare Arabic annotated corpus since tagged corpus is more useful than an untagged corpus because there is more information there than in the raw text alone. Once a corpus is tagged, it can be used to extract information. This can then be used for creating dictionaries and grammars of a language using real language data. Tagged corpora are also useful for detailed quantitative analysis of text.

The system's output -processed corpus- is therefore suited for machines rather than human although there exists a view interface for testing purposes which works well for short text; output can also be saved as HTML or TXT file.

Related Datasets & Solutions

The sample shown below contains multilingual text. Without proper rendering support, you may see question marks, boxes, or other symbols instead of the correct intended characters, you may either get the suitable fonts from our lingual support page.

Sample output

V: verb
N: noun
A: adjective
a: adverb
C: conjunction
Pr: preposition
d: demonstrative
O: ordinal number
R: pronoun
r: relative
F: foreign word
E: verbal noun
T: typographic error
X: No Solution
P: perfective
I: imperfective
M: imperative
E: emphatic
S: singular
D: dual
P: plural
F: feminine
M: masculine
1: first person
2: second person
3: third person

Check full documentation of Kalmasoft tagset here.

تعددت وتنوعت الأزمات التي خلفتها الحرب في اليمن وأزمة الانقطاع الكامل لخدمة الكهرباء ضاعفت من معاناة سكان هذه البلاد ودفعتهم نحو مصادر الطاقة البديلة للتخفيف من آثار تلك الأزمة

toddt wtnwot Al!zmAt Alty KlfthA AlHrb fy Alymn w!zm: AlAnqTAo AlkAml lKdm: AlkhrbA' DAoft mn moAnA: skAn hch AlblAd wdfothm nHw mSAdr AlTAq: Albdyl: lltKfyf mn |xAr tlk Al!zm:

ID	Token	KATS	Syntax	Arguments	Prefix
1	تعددت	toddt	VPIA	3PF•••
2	وتنوعت	wtnwot	VPIA	053PF•••	PC
3	الأزمات	Al!zmAt	NNG	••••PF	PD
4	التي	Alty	PL
5	خلفتها	KlfthA	VPIA	023SF3SF
6	الحرب	AlHrb	NNN	••••S•	PD
7	في	fy	PP
8	اليمن	Alymn	NN•G
9	وأزمة	w!zm:	NF•G	••••SF	PC
10	الانقطاع	AlAnqTAo	NF•G	07•SM	PD
11	الكامل	AlkAml	NA•G	••••SM	PD
12	لخدمة	lKdm:	NF•G	••••SF
13	الكهرباء	AlkhrbA'	N••G		PD
14	ضاعفت	DAoft	VPIA	033SF•••
15	من	mn	PP
16	معاناة	moAnA:	NF•G	03•SF
17	سكان	skAn	NQ•G	••••BM
18	هذه	hch	PD
19	البلاد	AlblAd	N••G		PD
20	ودفعتهم	wdfothm	VPIA	013SF3PM	PC
21	نحو	nHw	NV
22	مصادر	mSAdr	NF•A	••••PM
23	الطاقة	AlTAq:	N••G	••••SF	PD
24	البديلة	Albdyl:	NA•G	••••SF	PD
25	للتخفيف	lltKfyf	NF•G		PP
26	من	mn	PP
27	آثار	\|xAr	NF•G	••••B•
28	تلك	tlk	PD
29	الأزمة	Al!zm:	NF•G	••••SF

(*) These are for reference only, the real module generates simple version gloss (stems only).