Conference Papers

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    Word Sense Disambiguation using Bidirectional LSTM
    (Institute of Electrical and Electronics Engineers Inc., 2019) Rakshith, J.; Savasere, S.; Ramachandran, A.; Akhila, P.; Koolagudi, S.G.
    Word Sense Disambiguation is considered one of the challenging problems in natural language processing(NLP). LSTM-based Word Sense Disambiguation techniques have been shown effective through experiments. Models have been proposed before that employed LSTM to achieve state-of-the-art results. This paper presents an implementation and analysis of a Bidirectional LSTM model using openly available datasets (Semcor, MASC, SensEval-2 and SensEval-3) and knowledge base (WordNet). Our experiments showed that a similar state of the art results could be obtained with much less data or without external resources like knowledge graphs and parts of speech tagging. © 2019 IEEE.
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    Effect of Batch Normalization and Stacked LSTMs on Video Captioning
    (Institute of Electrical and Electronics Engineers Inc., 2021) Sarathi, V.; Mujumdar, A.; Naik, D.
    Integration of visual content with natural language for generating images or video description has been a challenging task for many years. Recent research in image captioning using Long Short term memory (LSTM) recently has motivated its possible application in video captioning where a video is converted into an array of frames, or images, and this array along with the captions for the video are used to train the LSTM network to associate the video with sentences. However very little is known about using fine tuning techniques such as batch normalization or Stacked LSTMs models in video captioning and how it affects the performance of the model.For this project, we want to compare the performance of the base model described in [1] with batch normalization and stacked LSTMs with base model as our reference. © 2021 IEEE.
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    A Comparative Study of Deep Learning Models for Word-Sense Disambiguation
    (Springer Science and Business Media Deutschland GmbH, 2022) Jadiya, A.; Dondemadahalli Manjunath, T.; Mohan, B.R.
    Word-sense disambiguation (WSD) has been a persistent issue since its introduction to the community of natural language processing (NLP). It has a wide range of applications in different areas like information retrieval (IR), sentiment analysis, knowledge graph construction, machine translation, lexicography, text mining, information extraction, and so on. Analysis of the performance of deep learning algorithms with different word embeddings is required to be done since various deep learning models are deployed for the task of disambiguation of word sense. In this paper, comparison of several deep learning models like CNN, LSTM, bidirectional LSTM, and CNN + LSTM is done with trainable as well as pretrained GloVe embeddings with common preprocessing methods. Performance evaluation of temporal convolutional network (TCN) model is done along with the comparison of the same with the formerly mentioned models. This paper shows that using GloVe embeddings may not result in better accuracy in the case of word-sense disambiguation, i.e., trainable embeddings perform better. It also includes a framework for evaluating deep learning models for WSD and analysis of the usage of embeddings for the same. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.