Vani Kanjirangat

I am currently working as a Researcher in the Natural Language Processing (NLP) lab of IDSIA, Switzerland. I completed my PhD in NLP, which was primarily centered on integrating machine learning and NLP techniques for text plagiarism detection. Ongoing research work includes Biomedical Text Mining, Semantic Shift Detection and Visual Summary Generations using NLP techniques, Temporal Embeddings, Transformers and other Deep Learning models. Alongside, I am working on projects aligned with application of deep learning models in financial and question answering domains.

• ResearchGate
• Google Scholar

• Kanjirangat, V., Samardzic, T., Dolamic, L. and Rinaldi, F., 2024, August. NLP_DI at NADI 2024 shared task: Multi-label Arabic Dialect Classifications with an Unsupervised Cross-Encoder. In Proceedings of The Second Arabic Natural Language Processing Conference (pp. 742-747).
• Kanjirangat, V., Antonucci, A. and Zaffalon, M., 2024. On the Limitations of Zero-Shot Classification of Causal Relations by LLMs (Work in Progress). Proceedings http://ceur-ws. org ISSN, 1613, p.0073.
• Vani Kanjirangat, Tanja Samardžić, Ljiljana Dolamic, Fabio Rinaldi (2023). Optimizing the Size of Subword Vocabularies in Dialect Classification. In Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023) (pp. 14-30). doi: 10.18653/v1/2023.vardial-1.2
• Kanjirangat, V. and Antonucci, A., 2023. Edge Labelling in Narrative Knowledge Graphs. The 6th International Workshop on Narrative Extraction from Texts: Text2Story 2023, co-located with 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023.
• Veena, G., Kanjirangat, V. and Gupta, D., 2023. AGRONER: An unsupervised agriculture named entity recognition using weighted distributional semantic model. Expert Systems with Applications, p.120440.
• Veena, G., Gupta, D. and Kanjirangat, V., 2023. Semi-supervised Bootstrapped Syntax-Semantics based Approach for Agriculture Relation Extraction for Knowledge Graph Creation and Reasoning. IEEE Access.
• Kanjirangat, V. and Rinaldi, F., 2021. Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information. Journal of Biomedical Informatics, 122, p.103893.
• Kanjirangat, V., Samardzic, T., Rinaldi, F. and Dolamic, L., 2022, December. Early Guessing for Dialect Identification. In Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 6417-6426).
• Kanjirangat, V., Samardzic, T., Dolamic, L. and Rinaldi, F., 2022, December. NLP DI at NADI Shared Task Subtask-1: Sub-word Level Convolutional Neural Models and Pre-trained Binary Classifiers for Dialect Identification. In Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP) (pp. 468-473).
• Vani, K., Mellace, S., & Antonucci, A. (2020). Temporal Embeddings and Transformer Models for Narrative Text Understanding. In Proceedings of the Text2StoryIR'20 Workshop @ ECIR. arXiv:2003.08811 [BEST PAPER AWARD!]
• Vani, K., Mitrović, S., Antonucci, A., and Rinaldi, F (2020). SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces. SemEval-2020, Task 1: Unsupervised Lexical Semantic Change Detection. In Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain. Association for Computational Linguistics.
• Mellace,S., Vani, K., and Antonucci, A (2020). Relation Clustering in Narrative Knowledge Graphs. In Proceedings of AI4Narratives at Conjunction with 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence.
• Oita, M., Vani, K., & Oezdemir-Zaech, F. (2020). Semantically Corroborating Neural Attention for Biomedical Question Answering. In Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II (pp. 670-685). Springer International Publishing. doi: 10.1007/978-3-030-43887-6₆₀
• Vani, K., and Antonucci, A (2019). NOVEL2GRAPH: Visual Summaries of Narrative Text Enhanced by Machine Learning. Text2Story@ ECIR.
• Volpetti, C., Vani, K, and Antonucci, A (2020). Temporal Word Embeddings for Narrative Understanding. ICMLC.
• Vani, K., & Gupta, D. (2018). Unmasking text plagiarism using syntactic-semantic based natural language processing techniques: Comparisons, analysis and challenges. Information Processing & Management, 54(3), 408-432. doi: 10.1016/j.ipm.2018.01.008
• Vani, K., & Gupta, D. (2018). Integrating syntax‐semantic‐based text analysis with structural and citation information for scientific plagiarism detection. Journal of the Association for Information Science and Technology, 69(11), 1330-1345. DOI: 10.1002/asi.24027
• Vani, K., & Gupta, D. (2017). Detection of idea plagiarism using syntax–semantic concept extractions with genetic algorithm. Expert Systems with Applications, 73,11-26. doi: 10.1016/j.eswa.2016.12.022
• Vani, K., and Gupta, D (2017). Text plagiarism classification using syntax based linguistic features. Expert Systems with Applications 88: 448-464.
• Vani, K., & Gupta, D. (2016). ASE@ DPIL-FIRE2016: Hindi Paraphrase Detection using Natural Language Processing Techniques & Semantic Similarity Computations. In FIRE (Working Notes) (pp. 244-249)
• Vani, K., & Gupta, D. (2016). Study on Extrinsic Text Plagiarism Detection Techniques and Tools. Journal of Engineering Science & Technology Review, 9(5).
• Vani, K., and Gupta, D (2014). Using K-means cluster based techniques in external plagiarism detection. International Conference on Contemporary Computing and Informatics (IC3I). IEEE.

E-Mail: vanik AT idsia.ch

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