Gabriele Pergola is an Assistant Professor at the Department of Computer Science.

Research Interests

His current research investigates the use of statistical models within machine learning for natural language processing and text understanding. He is particularly interested in topics related to sentiment analysis, question answering, topic and event extraction, and clinical text mining. More broadly, he is curious about the relationship between language and information.

He was previously appointed as Research Fellow working with Prof. Yulan HeLink opens in a new window as part of the Event-Centric Framework for Natural Language Understanding, a Turing AI Acceleration Fellowship held by Prof. Yulan HeLink opens in a new window, and funded by the UKRI.

Reading groups

• Organiser of the NLP Discussion MeetingsLink opens in a new window of the NLP Group at the University of Warwick, gathering students and researchers interested in Natural Language Processing. Email me if interested in joining the meetings.

Teaching

• CS918 Natural Language Processing - Module Leader (Term II, 2022/2023)
• CS918 Natural Language Processing - Module Organizer (Term I, 2021/2022)
• CS918 Natural Language Processing - Lab Demonstrations and Seminars (Term I in 2018/2019, 2019/2020, 2020/2021).
• CS909 Data Mining - Lab Demonstrations and Seminars (Term II in 2018/2019 and 2019/2020).
• CS331 Neural Computing - Seminars (2019/2020)

Education

He received his PhD degree in natural language understanding from the University of WarwickLink opens in a new window (UK) on "Probabilistic Neural Topic Models for Text UnderstandingLink opens in a new window".
He holds a BSc and an MEng (cum laude) degree in Computer Engineering from the University of Palermo (Italy). Prior to joining Warwick, he received a Postgraduate Fellowship from the University of Rome "La Sapienza" (Italy) for designing and implementing machine learning systems to support access to cultural heritage as part of the research project "Design and development of innovative technologies for the enjoyment of cultural heritage".

Awards and Recognitions

• 2022 - Turing Post-Doctoral Enrichment Scheme - Awarded by the Alan Turing Institute, London, UK.

• 2022 - PhD Thesis Prize - Faculty of Science, Engineering, and Medicine (SEM) Thesis Prize in Computer Science. Awarded by the University of Warwick, Coventry, UK.

• 2019 - Best Presentation - Prize in the Machine Learning and AI track, WPCCS 2019, University of Warwick.

Research Projects

• 2024 - Policy Support Fund - Department for Levelling Up, Housing and Communities (DLUHC); Human Machine Intelligence Group, Cranfield University; Blavatnik School of Government, University of Oxford.
  Co-PI for the project: "A case study of the application of Natural Language Processing for Information Extraction and Analysis in Policy Review"
• 2023/24 - STAR funding - NPCC Forensic Capability Network
  PI for the project: "Development of an NLP model to detect threatening and abusive language used in communication with victims".

• 2022/23 - RDF Science Development Award
  PI for the project: "An Event-Centric Dialogue System for Second Language Learners".

• 2022 - National AI Strategy Fund
  PI for the project: "METU: An Inclusive AI-Powered Framework Making Text Easier to Understand".

Recent Invited Talks

• 2023 - "Large Language Models and The Key Ingredients Powering the Rise of Chatbots" - Keele University
• 2023 - "The not-so-silent AI revolution: Chatbots and their Impact on Engaging Education" - Department of Psychology - University of Warwick

Publications

• Z. Sun, J. Li, G. Pergola, B.C. Wallace, and Y. He. Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical StudyLink opens in a new window. The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2024

• J Lu, S An, M Lin, G Pergola, Y He, D Yin, X Sun, Y Wu. MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation, arXiv:2308.08239.

• J. Lu, G. Pergola, L. Gui and Y. He. Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question AnsweringLink opens in a new window, arXiv:2305.04522.

• L. Zhu, R. Zhao, G. Pergola and Y. He. Disentangling Aspect and Stance via a Siamese Autoencoder for Aspect Clustering of Vaccination Opinions. Findings of ACL, 2023.

• X. Tan, G. Pergola and Y. He. Event Temporal Relation Extraction with Bayesian Translational ModelLink opens in a new window. The 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL), May 2023.

• J. Lu, J. Li, B.C. Wallace, Y. He and G. Pergola. NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching SummarizationLink opens in a new window. Findings of EACL, 2023.

• Z. Sun, J. Li, G. Pergola, B.C. Wallace, B. John, N. Greene, J. Kim and Y. He. PHEE: A Dataset for Pharmacovigilance Event Extraction from TextLink opens in a new window. The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), Dec. 2022.

• J. Lu, X. Tan, G. Pergola, L. Gui and Y. He. Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationLink opens in a new window. Findings of EMNLP, 2022.

• L. Zhu, Z. Fang, G. Pergola, R. Procter and Y. He. Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media. 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Jul. 2022.
• X. Tan, G. Pergola and Y. He. Extracting Event Temporal Relations via Hyperbolic Geometry, Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov. 2021.
• H. Yan, L. Gui, G. Pergola and Y. He. Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction, The 59th Annual Meeting of the Association for Computational Linguistics (ACL), Aug. 2021.
• L. Zhu, G. Pergola, L. Gui, D. Zhou and Y. He. Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection, The 59th Annual Meeting of the Association for Computational Linguistics (ACL), Aug. 2021.
• G. Pergola, L. Gui and Y. He. A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Jun. 2021.
• G. Pergola, E. Kochkina, L. Gui, M. Liakata and Y. He. Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies, The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Apr. 2021.
• R. Zhao, L. Gui, G. Pergola and Y. He. Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews, The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Apr. 2021
• Gabriele Pergola - Probabilistic Neural Topic Models for Text UnderstandingLink opens in a new window - PhD Thesis, Apr. 2021 - [slidesLink opens in a new window]
• J. Lu, G. Pergola, L. Gui, B. Li and Y. He. CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering, The 28th International Conference on Computational Linguistics (COLING), Dec. 2020. [code]
• L. Gui, J. Leng, G. Pergola, Y. Zhou, R. Xu and Y. He. Neural Topic Model with Reinforcement Learning. Conference on Empirical Methods in Natural Language Processing (EMNLP), Hong Kong, China, Nov. 2019
  

  @inproceedings{gui_rl2019,
      title = "Neural Topic Model with Reinforcement Learning",
      author = "Gui, Lin  and
        Leng, Jia  and
        Pergola, Gabriele  and
        Zhou, Yu  and
        Xu, Ruifeng  and
        He, Yulan",
      booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://www.aclweb.org/anthology/D19-1350",
      pages = "3478--3483"
  }
  

  In recent years, advances in neural variational inference have achieved many successes in text processing. Examples include neural topic models which are typically built upon variational autoencoder (VAE) with an objective of minimising the error of reconstructing original documents based on the learned latent topic vectors. However, minimising reconstruction errors does not necessarily lead to high quality topics. In this paper, we borrow the idea of reinforcement learning and incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. Furthermore, our proposed model is able to automatically separating background words dynamically from topic words, thus eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. Experimental results on the 20 Newsgroups and the NIPS datasets show superior performance both on perplexity and topic coherence measure compared to state-of-the-art neural topic models.
  

• G. Pergola, L. Gui and Y. He. TDAM: a Topic-Dependent Attention Model for Sentiment AnalysisLink opens in a new window. Information Processing and Management, 56(6):102084, 2019.

  @article{pergola19tdam,
          title = {TDAM: A topic-dependent attention model for sentiment analysis},
          author = {Gabriele Pergola and Lin Gui and Yulan He},
          journal = {Information Processing \& Management},
          year = {2019},
          publisher = {Elsevier},
          volume ={56},
          number = {6},
          pages = {102084},
          year = {2019},
          issn = {0306-4573},
          url = {http://www.sciencedirect.com/science/article/pii/S0306457319305461}
  }

  We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and employs an attention mechanism to derive local topic embedding for words and sentences. These are subsequently incorporated in a modified Gated Recurrent Unit (GRU) for sentiment classification and extraction of topics bearing different sentiment polarities. Those topics emerge from the words’ local topic embeddings learned by the internal attention of the GRU cells in the context of a multi-task learning framework. In this paper, we present the hierarchical architecture, the new GRU unit and the experiments conducted on users’ reviews which demonstrate classification performance on a par with the state-of-the-art methodologies for sentiment classification and topic coherence outperforming the current approaches for supervised topic extraction. In addition, our model is able to extract coherent aspect-sentiment clusters despite using no aspect-level annotations for training.
  

• G. Pergola, Y. He and D. Lowe. Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global ContextLink opens in a new window. The 2nd AAAI Workshop on Health Intelligence (AAAI18), New Orleans, Louisiana, USA, Feb. 2018.

  @inproceedings{pergola18,
  title = "Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context",
  author = "Gabriele Pergola and Yulan He and David Lowe",
  booktitle = "The 2nd AAAI Workshop on Health Intelligence (AAAI)",
  year = "2018",
  month = jun,
  day = "20",
  language = "English",
  pages = "499--506"
  }
  

  Making sense of words often requires to simultaneouslyexamine the surrounding context of a term as well as theglobal themes characterizing the overall corpus. Severaltopic models have already exploited word embeddingsto recognize local context, however, it has been weaklycombined with the global context during the topic inference.This paper proposes to extract topical phrasescorroborating the word embedding information with theglobal context detected by Latent Semantic Analysis,and then combine them by means of the Polya urn ´model. To highlight the effectiveness of this combinedapproach the model was assessed analyzing clinical reports,a challenging scenario characterized by technicaljargon and a limited word statistics available. Resultsshow it outperforms the state-of-the-art approaches interms of both topic coherence and computational cost.
  

• P. Cottone, S. Gaglio, G. Lo Re, M. Ortolani, G. Pergola. Structural Knowledge Extraction from Mobility DataLink opens in a new window. (AI*IA), 294-307, 2016.
• P. Cottone, M. Ortolani, G. Pergola. Gl-learning: an optimized framework for grammatical inferenceLink opens in a new window. 339-346, (CompSysTech), 2016.
• P. Cottone, M. Ortolani, G. Pergola. Detecting Similarities in Mobility PatternsLink opens in a new window. (STAIRS), 167-178, 2016.
• T. Catarci, F. Leotta, M. Mecella, D. Sora, P. Cottone, G. Lo Re, M. Ortolani, V. Agate, G. Pecoraro, G. Pergola. Your Friends Mention It. What About Visiting It? A Mobile Social-Based Sightseeing ApplicationLink opens in a new window. (AVI),2016.

Contact

Room CS2.34,
Computer Science Department,
University of Warwick,
Coventry,
CV4 7AL

gabriele dot pergola dot 1 at warwick dot ac dot uk