Awais Athar


2020

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SimplifyUR: Unsupervised Lexical Text Simplification for Urdu
Namoos Hayat Qasmi | Haris Bin Zia | Awais Athar | Agha Ali Raza
Proceedings of the 12th Language Resources and Evaluation Conference

This paper presents the first attempt at Automatic Text Simplification (ATS) for Urdu, the language of 170 million people worldwide. Being a low-resource language in terms of standard linguistic resources, recent text simplification approaches that rely on manually crafted simplified corpora or lexicons such as WordNet are not applicable to Urdu. Urdu is a morphologically rich language that requires unique considerations such as proper handling of inflectional case and honorifics. We present an unsupervised method for lexical simplification of complex Urdu text. Our method only requires plain Urdu text and makes use of word embeddings together with a set of morphological features to generate simplifications. Our system achieves a BLEU score of 80.15 and SARI score of 42.02 upon automatic evaluation on manually crafted simplified corpora. We also report results for human evaluations for correctness, grammaticality, meaning-preservation and simplicity of the output. Our code and corpus are publicly available to make our results reproducible.

2018

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PronouncUR: An Urdu Pronunciation Lexicon Generator
Haris Bin Zia | Agha Ali Raza | Awais Athar
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Urdu Word Segmentation using Conditional Random Fields (CRFs)
Haris Bin Zia | Agha Ali Raza | Awais Athar
Proceedings of the 27th International Conference on Computational Linguistics

State-of-the-art Natural Language Processing algorithms rely heavily on efficient word segmentation. Urdu is amongst languages for which word segmentation is a complex task as it exhibits space omission as well as space insertion issues. This is partly due to the Arabic script which although cursive in nature, consists of characters that have inherent joining and non-joining attributes regardless of word boundary. This paper presents a word segmentation system for Urdu which uses a Conditional Random Field sequence modeler with orthographic, linguistic and morphological features. Our proposed model automatically learns to predict white space as word boundary as well as Zero Width Non-Joiner (ZWNJ) as sub-word boundary. Using a manually annotated corpus, our model achieves F1 score of 0.97 for word boundary identification and 0.85 for sub-word boundary identification tasks. We have made our code and corpus publicly available to make our results reproducible.

2012

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Context-Enhanced Citation Sentiment Detection
Awais Athar | Simone Teufel
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Detection of Implicit Citations for Sentiment Detection
Awais Athar | Simone Teufel
Proceedings of the Workshop on Detecting Structure in Scholarly Discourse

2011

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Sentiment Analysis of Citations using Sentence Structure-Based Features
Awais Athar
Proceedings of the ACL 2011 Student Session