Swapnil Hingmire


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Extracting Events from Industrial Incident Reports
Nitin Ramrakhiyani | Swapnil Hingmire | Sangameshwar Patil | Alok Kumar | Girish Palshikar
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

Incidents in industries have huge social and political impact and minimizing the consequent damage has been a high priority. However, automated analysis of repositories of incident reports has remained a challenge. In this paper, we focus on automatically extracting events from incident reports. Due to absence of event annotated datasets for industrial incidents we employ a transfer learning based approach which is shown to outperform several baselines. We further provide detailed analysis regarding effect of increase in pre-training data and provide explainability of why pre-training improves the performance.


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R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
Irene Li | Alexander Fabbri | Swapnil Hingmire | Dragomir Radev
Proceedings of the 28th International Conference on Computational Linguistics

The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs. In this paper, we frame learning prerequisite relationships among concepts as an unsupervised task with no access to labeled concept pairs during training. We propose a model called the Relational-Variational Graph AutoEncoder (R-VGAE) to predict concept relations within a graph consisting of concept and resource nodes. Results show that our unsupervised approach outperforms graph-based semi-supervised methods and other baseline methods by up to 9.77% and 10.47% in terms of prerequisite relation prediction accuracy and F1 score. Our method is notably the first graph-based model that attempts to make use of deep learning representations for the task of unsupervised prerequisite learning. We also expand an existing corpus which totals 1,717 English Natural Language Processing (NLP)-related lecture slide files and manual concept pair annotations over 322 topics.

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Extracting Message Sequence Charts from Hindi Narrative Text
Swapnil Hingmire | Nitin Ramrakhiyani | Avinash Kumar Singh | Sangameshwar Patil | Girish Palshikar | Pushpak Bhattacharyya | Vasudeva Varma
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events

In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi. An MSC is a formal representation allowing the depiction of actors and interactions among these actors in a scenario, apart from supporting a rich framework for formal inference. We propose an approach to extract MSC actors and interactions from a Hindi narrative. As a part of the approach, we enrich an existing event annotation scheme where we provide guidelines for annotation of the mood of events (realis vs irrealis) and guidelines for annotation of event arguments. We report performance on multiple evaluation criteria by experimenting with Hindi narratives from Indian History. Though Hindi is the fourth most-spoken first language in the world, from the NLP perspective it has comparatively lesser resources than English. Moreover, there is relatively less work in the context of event processing in Hindi. Hence, we believe that this work is among the initial works for Hindi event processing.


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Extraction of Message Sequence Charts from Narrative History Text
Girish Palshikar | Sachin Pawar | Sangameshwar Patil | Swapnil Hingmire | Nitin Ramrakhiyani | Harsimran Bedi | Pushpak Bhattacharyya | Vasudeva Varma
Proceedings of the First Workshop on Narrative Understanding

In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering. We propose algorithms to automatically extract an MSC from a history narrative. For a given narrative, we first identify verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. As a final step in MSC extraction, we employ a state-of-the art algorithm to temporally re-order these interactions. Our evaluation on multiple publicly available narratives shows improvements over four baselines.

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Extraction of Message Sequence Charts from Software Use-Case Descriptions
Girish Palshikar | Nitin Ramrakhiyani | Sangameshwar Patil | Sachin Pawar | Swapnil Hingmire | Vasudeva Varma | Pushpak Bhattacharyya
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

Software Requirement Specification documents provide natural language descriptions of the core functional requirements as a set of use-cases. Essentially, each use-case contains a set of actors and sequences of steps describing the interactions among them. Goals of use-case reviews and analyses include their correctness, completeness, detection of ambiguities, prototyping, verification, test case generation and traceability. Message Sequence Chart (MSC) have been proposed as a expressive, rigorous yet intuitive visual representation of use-cases. In this paper, we describe a linguistic knowledge-based approach to extract MSCs from use-cases. Compared to existing techniques, we extract richer constructs of the MSC notation such as timers, conditions and alt-boxes. We apply this tool to extract MSCs from several real-life software use-case descriptions and show that it performs better than the existing techniques. We also discuss the benefits and limitations of the extracted MSCs to meet the above goals.


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Identification of Alias Links among Participants in Narratives
Sangameshwar Patil | Sachin Pawar | Swapnil Hingmire | Girish Palshikar | Vasudeva Varma | Pushpak Bhattacharyya
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications. This task becomes challenging because these participants are often referred to using multiple aliases. In this paper, we propose an approach based on linguistic knowledge for identification of aliases mentioned using proper nouns, pronouns or noun phrases with common noun headword. We use Markov Logic Network (MLN) to encode the linguistic knowledge for identification of aliases. We evaluate on four diverse history narratives of varying complexity. Our approach performs better than the state-of-the-art approach as well as a combination of standard named entity recognition and coreference resolution techniques.


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Measuring Topic Coherence through Optimal Word Buckets
Nitin Ramrakhiyani | Sachin Pawar | Swapnil Hingmire | Girish Palshikar
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Measuring topic quality is essential for scoring the learned topics and their subsequent use in Information Retrieval and Text classification. To measure quality of Latent Dirichlet Allocation (LDA) based topics learned from text, we propose a novel approach based on grouping of topic words into buckets (TBuckets). A single large bucket signifies a single coherent theme, in turn indicating high topic coherence. TBuckets uses word embeddings of topic words and employs singular value decomposition (SVD) and Integer Linear Programming based optimization to create coherent word buckets. TBuckets outperforms the state-of-the-art techniques when evaluated using 3 publicly available datasets and on another one proposed in this paper.

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Event Timeline Generation from History Textbooks
Harsimran Bedi | Sangameshwar Patil | Swapnil Hingmire | Girish Palshikar
Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)

Event timeline serves as the basic structure of history, and it is used as a disposition of key phenomena in studying history as a subject in secondary school. In order to enable a student to understand a historical phenomenon as a series of connected events, we present a system for automatic event timeline generation from history textbooks. Additionally, we propose Message Sequence Chart (MSC) and time-map based visualization techniques to visualize an event timeline. We also identify key computational challenges in developing natural language processing based applications for history textbooks.

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Experiments with Domain Dependent Dialogue Act Classification using Open-Domain Dialogue Corpora
Swapnil Hingmire | Apoorv Shrivastava | Girish Palshikar | Saurabh Srivastava
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)


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Noun Phrase Chunking for Marathi using Distant Supervision
Sachin Pawar | Nitin Ramrakhiyani | Girish K. Palshikar | Pushpak Bhattacharyya | Swapnil Hingmire
Proceedings of the 12th International Conference on Natural Language Processing


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LMSim : Computing Domain-specific Semantic Word Similarities Using a Language Modeling Approach
Sachin Pawar | Swapnil Hingmire | Girish K. Palshikar
Proceedings of the 11th International Conference on Natural Language Processing

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Sprinkling Topics for Weakly Supervised Text Classification
Swapnil Hingmire | Sutanu Chakraborti
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)