Naren Ramakrishnan


Improving Zero-Shot Event Extraction via Sentence Simplification
Sneha Mehta | Huzefa Rangwala | Naren Ramakrishnan
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)

The success of sites such as ACLED and Our World in Data have demonstrated the massive utility of extracting events in structured formats from large volumes of textual data in the formof news, social media, blogs and discussion forums.Event extraction can provide a window into ongoing geopolitical crises and yield actionable intelligence. In this work, we cast socio-political event extraction as a machine reading comprehension (MRC) task. % With the proliferation of large pretrained language models Machine Reading Comprehension (MRC) has emerged as a new paradigm for event extraction in recent times. In this approach, extraction of social-political actors and targets from a sentence is framed as an extractive question-answering problem conditioned on an event type. There are several advantages of using MRC for this task including the ability to leverage large pretrained multilingual language models and their ability to perform zero-shot extraction. Moreover, we find that the problem of long-range dependencies, i.e., large lexical distance between trigger and argument words and the difficulty of processing syntactically complex sentences plague MRC-based approaches.To address this, we present a general approach to improve the performance of MRC-based event extraction by performing unsupervised sentence simplification guided by the MRC model itself. We evaluate our approach on the ICEWS geopolitical event extraction dataset, with specific attention to ‘Actor’ and ‘Target’ argument roles. We show how such context simplification can improve the performance of MRC-based event extraction by more than 5{% for actor extraction and more than 10{% for target extraction.


Mitigating Uncertainty in Document Classification
Xuchao Zhang | Fanglan Chen | Chang-Tien Lu | Naren Ramakrishnan
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

The uncertainty measurement of classifiers’ predictions is especially important in applications such as medical diagnoses that need to ensure limited human resources can focus on the most uncertain predictions returned by machine learning models. However, few existing uncertainty models attempt to improve overall prediction accuracy where human resources are involved in the text classification task. In this paper, we propose a novel neural-network-based model that applies a new dropout-entropy method for uncertainty measurement. We also design a metric learning method on feature representations, which can boost the performance of dropout-based uncertainty methods with smaller prediction variance in accurate prediction trials. Extensive experiments on real-world data sets demonstrate that our method can achieve a considerable improvement in overall prediction accuracy compared to existing approaches. In particular, our model improved the accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data.


Finding the Storyteller: Automatic Spoiler Tagging using Linguistic Cues
Sheng Guo | Naren Ramakrishnan
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)


Seeded Discovery of Base Relations in Large Corpora
Nicholas Andrews | Naren Ramakrishnan
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing