A Computational Classification of Dynamic Urdu Copula Verb

Abbas, Qaiser; Raza, Ghulam

doi:10.5120/14874-3252

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…The root form of another copula verb ban 'become' (Abbas and Raza, 2014;Raza, 2011) is presented in 19(c). The subjunctive form of copula verb rahEN 'remain' (Abbas and Raza, 2014;Raza, 2011) is annotated in 19(d). The copula verb with present and the past tense hEN/tHE 'are/were' is annotated in 19(e).…”

Section: Copula Verbsmentioning

confidence: 99%

Semi-Semantic Annotation: A guideline for the URDU.KON-TB treebank POS annotation

Abbas

2016

ALA

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This work elaborates the semi-semantic part of speech annotation guidelines for the URDU.KON-TB treebank: an annotated corpus. A hierarchical annotation scheme was designed to label the part of speech and then applied on the corpus. This raw corpus was collected from the Urdu Wikipedia and the Jang newspaper and then annotated with the proposed semi-semantic part of speech labels. The corpus contains text of local & international news, social stories, sports, culture, finance, religion, traveling, etc. This exercise finally contributed a part of speech annotation to the URDU.KON-TB treebank. Twenty-two main part of speech categories are divided into subcategories, which conclude the morphological, and semantical information encoded in it. This article reports the annotation guidelines in major; however, it also briefs the development of the URDU.KON-TB treebank, which includes the raw corpus collection, designing & employment of annotation scheme and finally, its statistical evaluation and results. The guidelines presented will be useful for linguistic community to annotate sentences not only for the national language Urdu but for the other indigenous languages like Punjab, Sindhi, Pashto, etc. as well.Keywords: semi-semantic part of speech; rich information; deep learning; parsing aid; linguistically motivated annotation; humanistic annotation PovzetekRezultat tega dela so navodila za označevanje polsemantičnih besednih vrst v drevesnici URDU.KON-TB. Hierarhična označevalna shema je bila oblikovana z namenom, da razvrsti besedne vrste in jih kot take uporabi na korpusih. Tokratni koprus, ki je sestavljen iz strani Urdu Wikipedie in časopisa Jang, je bil označen s predlaganimi polsemantičnimi besednimi vrstami. Korpus vsebuje tekste lokalnih in mednarodnih novic, zgodbe s socialno temo, šport, kulturo, finance, vero, potovanja in druge teme. Uspešen poskusa označevanja je nadgradil drevesnico URDU.KON-TB. Dvaindvajset osnovnih besednih vrst je razdeljenih v podkategorije z morfološkimi in semantičnimi informacijami. Članek podaja jasne osnovne smernice označevanja. Hkrati ponuja kratek pregled razvoja drevesnice URDU.KON-TB, ki vsebuje zbirke surovih korpusov, oblikovanje in uporabo shem za označevanje ter nenazadnje tudi statistično oceno in rezultate. Predlagana navodila za označevanje so namenjena jezikovnim skupnostim, ki označujejo stavke tako v državnem jeziku Urdu kot tudi v drugih jezikih, kot so Punjab, Sindhi in drugih.Ključne besede: polsemantična besedna vrsta; številne informacije; globoko učenje; pomoč pri razvrščanju; jezikoslovno utemeljeno označevanje; humanistično označevanje 98 Qaiser ABBAS

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Section: Copula Verbsmentioning

confidence: 99%

Semi-Semantic Annotation: A guideline for the URDU.KON-TB treebank POS annotation

Abbas

2016

ALA

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“…The second level contains eleven subcategories of V to represent different morphological or functional forms e.g. V.COP (V as a copula verb (Abbas and Raza 2014)), V.IMPERF (V has an imperfective form (Butt and Ramchand 2001; Butt and Rizvi 2010)), V.INF (V has an infinitive form Butt (1993); Abbas and Nabi Khan (2009); Abbas et al (2015)), etc. The third level contains further twenty-five subcategories to represent the morphological information in depth e.g.…”

Section: Preliminary Workmentioning

confidence: 99%

Morphologically rich Urdu grammar parsing using Earley algorithm

Abbas¹

2015

Nat. Lang. Eng.

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This work presents the development and evaluation of an extended Urdu parser. It further focuses on issues related to this parser and describes the changes made in the Earley algorithm to get accurate and relevant results from the Urdu parser. The parser makes use of a morphologically rich context free grammar extracted from a linguistically-rich Urdu treebank. This grammar with sufficient encoded information is comparable with the state-of-the-art parsing requirements for the morphologically rich Urdu language. The extended parsing model and the linguistically rich extracted-grammar both provide us better evaluation results in Urdu/Hindi parsing domain. The parser gives 87% of f-score, which outperforms the existing parsing work of Urdu/Hindi based on the tree-banking approach.

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“…At the end, as mentioned in Section I that the linguistic information like the Urdu POS [25], morphological information [8,9,10], Urdu Ezafe [7], verb morphological forms [2,24], etc., can improve the prediction results. The enrichment of linguistic information is possible in the extended PST version of the QASKU model because the resources needed are in pipeline.…”

Section: Future Workmentioning

confidence: 99%

A Stochastic Prediction Interface for Urdu

Abbas

2014

IJISA

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Abstract-This work lays down a foundation for text prediction of an inflected and under-resourced language Urdu. The interface developed is not limited to a T9 (Text on 9 keys) application used in embedded devices, which can only predict a word after typing initial characters. It is capable of predicting a word like T9 and also a sequence of word after a word in a continuous manner for fast document typing. It is based on Ngram language model. This stochastic interface deals with three N-gram levels from unary to ternary independently. The unigram mode is being in use for applications like T9, while the bigram and tri-gram modes are being in use for sentence prediction. The measures include a percentage of keystrokes saved, keystrokes until completion and a percentage of time saved during the typing. Two different corpora are merged to build a sufficient amount of data. The test data is divided into a test and a held out data equally for an experimental purpose. This whole exercise enables the QASKU system outperforms the FastType with almost 15% more saved keystrokes.

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A Computational Classification of Dynamic Urdu Copula Verb

Cited by 5 publications

References 8 publications

Semi-Semantic Annotation: A guideline for the URDU.KON-TB treebank POS annotation

Semi-Semantic Annotation: A guideline for the URDU.KON-TB treebank POS annotation

Morphologically rich Urdu grammar parsing using Earley algorithm

A Stochastic Prediction Interface for Urdu

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