Complex Network of Urdu Language

Khan, Nuzhat; Bakht, Muhammad Paend; Khan, Muhammad Junaid; Samad, Abdul

doi:10.1109/macs48846.2019.9024791

Cited by 1 publication

(3 citation statements)

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“…This misinterpretation was because of not performing scientific research for exploration of word order and sentence structure in natural Urdu language [40]. In our previous work [22], it was observed that there are some hidden patterns in complex topological structure of Urdu network expressed in the form of word order. Specific stable order was detected in most of long sentences except for some short sentences.…”

Section: Figure 3 Corpus Collectionmentioning

confidence: 98%

“…Although most of world languages follow universal features, still their differences are worth detecting. Our previous works explored complex network properties of Urdu [22] while two empirical linguistic laws are verified to look into statistical attributes of the language [23], [24]. The resulting features motivated to broaden investigation of word order in Urdu structure.…”

Section: Figure 2 Requirement Of Structure Discovery In Machine Learningmentioning

confidence: 99%

“…Instead of annotation, each word was considered an independent entity regardless of grammer and meaning. Based on bag of bigrams model a word co-occurrence network is designed through sliding window technique [22]. Resultant network is converted to a graph G having more than 5K words as nodes.…”

Section: Figure 3 Corpus Collectionmentioning

confidence: 99%

See 2 more Smart Citations

Two-fold complex network approach to discover the impact of word-order in Urdu language

Khan

Kamaruddin

Sheikh

et al. 2021

IJEECS

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<div>This work examines standard Urdu text to confirm impact of word order in the language structure. The complex network approach is used to obtain universal properties of two different word co-occurrence networks. Macro and micro scale two-fold examinations of networks are performed for structure discovery. While preserving the vocabulary size, two networks are generated from same text with and without standard word order. In addition, text networks are benchmarked with a random network to extract global features. Achieved outcomes indicate certain word order in Urdu structure for most of the sentences. The normal and shuffled text networks demonstrated similar large-scale characteristics. The results show that average path length and network diameter is reduced after shuffling. On the other hand, clustering coefficient is increased in shuffled text as compared to normal text. Our results validated that few short sentences in range of three words are fully free order. The observations revealed that long sentences are ambiguous without standard order. Both networks are topologically similar but shuffling caused massive discrepancy in network composition and sentence structure. Inside graph view, grammatical association-based words connectivity exists in normal text network. With this universal approach, impact of word order in Urdu language is confirmed. Meanwhile, this breakthrough directs to uncover language composition by extracting small sentences as motifs.</div>

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Section: Figure 3 Corpus Collectionmentioning

confidence: 98%

Section: Figure 2 Requirement Of Structure Discovery In Machine Learningmentioning

confidence: 99%