{"type":"ScholarlyArticle","title":"On Building an Interpretable Topic Modeling Approach for the Urdu Language","authors":[{"name":"Zarmeen Nasim","slug":"zarmeen-nasim","given":"Zarmeen","family":"Nasim","affiliation":""}],"abstract":"This research is an endeavor to combine deep-learning-based language modeling with classical topic modeling techniques to produce interpretable topics for a given set of documents in Urdu, a low resource language. The existing topic modeling techniques produce a collection of words, often un-interpretable, as suggested topics without integrat-ing them into a semantically correct phrase/sentence. The proposed approach would first build an accurate Part of Speech (POS) tagger for the Urdu Language using a publicly available corpus of many million sentences. Using semanti-cally rich feature extraction approaches including Word2Vec and BERT, the proposed approach, in the next step, would experiment with different clus-tering and topic modeling techniques to produce a list of potential topics for a given set of documents. Finally, this list of topics would be sent to a labeler module to produce syntactically correct phrases that will represent interpretable topics.","doi":"10.24963/ijcai.2020/740","url":"https://ijcai.org/proceedings/2020/740","pdf":"https://ijcai.org/proceedings/2020/0740.pdf","markdown":"https://ijcai.org/proceedings/2020/740.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/740","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":740,"pages":{"first":5200,"last":5201},"keywords":["Natural Language Processing: NLP Applications and Tools","Natural Language Processing: Natural Language Processing","Natural Language Processing: Embeddings","Natural Language Processing: Natural Language Summarization"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24099/file_24099.m3u8","length_sec":300,"provider":"hls"}],"references":[{"position":1,"raw":"[Daud et al., 2017] Ali Daud, Wahan Khan, and Dunren Che. Urdu language processing: a survey. Artificial Intelli- gence Review, 47(3): 279-311, 2017.","title":"Urdu language processing: a survey","authors":"Ali Daud, Wahan Khan, Dunren Che","year":"2017","venue":"Artificial Intelligence Review","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Devlin et al., 2018] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805, 2018.","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. Distributed repre- sentations of words and phrases and their compositional- ity. In Advances in Neural Information Processing Sys- tems, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Rehman et al., 2018] Anwar U. Rehman, Zobia Rehman, Junaid Akram, Waqar Ali, Munam Ali Shah, and Muham- mad Salman. Statistical Topic Modeling for Urdu Text Articles. In Proceedings of 24th International Conference on Automation and Computing (ICAC), pages 1-6, Sep- tember 2018. IEEE.","title":"Statistical Topic Modeling for Urdu Text Articles","authors":"U Anwar, Zobia Rehman, Junaid Rehman, Waqar Akram, Munam Ali, Ali Shah, Muhammad Salman","year":"2018","venue":"Proceedings of 24th International Conference on Automation and Computing (ICAC)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Shakeel et al., 2018] Khadija Shakeel, Ghulam Rasool Ta- hir, Irsha Tehseen, and Mubashir Ali. A framework of Urdu topic modeling using latent dirichlet allocation (LDA). In Proceedings of 8th Annual Computing and Communication Workshop and Conference (CCWC), pages 117-123, January 2018. IEEE. Figure 1: Proposed Methodology","title":"A framework of Urdu topic modeling using latent dirichlet allocation (LDA)","authors":"Khadija Shakeel, Rasool Ghulam, Irsha Tahir, Mubashir Tehseen, Ali","year":"2018","venue":"Proceedings of 8th Annual Computing and Communication Workshop and Conference (CCWC)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}