M Tech Dissertations

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3

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Start date: 2020Subject: search.filters.subject.Natural language processing

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Now showing 1 - 5 of 5
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    User Stories to Concept Map: An approach to Visualise Dependencies
    (2021) Shah, Dishant; Tiwari, Saurabh
    "Writing the user stories which captures the user’s perspective in the agile framework is the starting step of gathering requirements. As user stories are written informally with fewer restrictions but may get affected by the inherent NL issues such as ambiguity, incompleteness and inter-dependencies. In this thesis work, we have proposed an approach to automatically generate the conceptual model (i.e., concept maps) from the user stories. The approach also identifies the inter-dependencies between the user stories, and subsequently analyses the incompleteness among them. The approach makes use of natural language processing (NLP) techniques for the identification of linguistics patterns. Next, the linguistics patterns are mapped into the concepts and attributes, resulting in the generation of concept maps by applying the proposed heuristic rules. After generating concept maps from the user stories of a software system, we have recognized the dependency between the concepts for a single user story, and are able to identify inter-dependencies between the set of user stories. We have evaluated the applicability of the proposed approach by experimenting on 22 different projects available publicly. On average, we found that the generated concept maps are able to capture inter-dependency with 94.7% accuracy. We have developed tool support for realising the proposed approach."
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    Commonsense validation and explanation
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2020) Makwana, Vivek H.; Lalchandani, Jayprakash
    Common-sense reasoning[1] is a field of artificial intelligence and machine learning that focuses on helping computers understand and interact with people more naturally by finding ways to collect these assumptions and teach them to computers. Common-sense reasoning has been most successful in the field of natural language processing (NLP).Without common-sense, it won’t be easy to build versatile and unsupervised NLP systems in an increasingly digital and mobile world. When we talk to each other and talk online, we try to be as interesting and take advantage of new ways to express things. There’s more to it than one would think. If we say, “can you put an elephant into the fridge?” you could answer the question quite easily despite the fact, in all probability, you had never pictured an elephant in the fridge. This is an example of we as humans, not just knowing about the world, but knowing how to apply our knowledge to things we haven’t thought about before. It remains a challenging question on how to evaluate whether a system has a sense-making capability. Existing benchmarks measure common-sense knowledge indirectly and without explanation. In this thesis, we directly test whether a system can differentiate natural language statements that make sense from those that do not make sense. A system is also asked to identify the most relevant reason why a given statement is against common-sense. We have used models trained over large-scale language modeling tasks and human performance, showing that there are different challenges for system sense-making.
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    Apparel attributes classification using deep learning
    (2020) Desai, Harsh Sanjaykumar; Jat, P.M
    Apparel attributes classification finds a practical applications in E-Commerce. The project is for www.Blibli.com website which is an E-commerce Platform in Indonesia and a partner of Coviam Technologies. This report describes an approach to classify attributes such as material, neck/collar, sleeves type etc. specific to various apparels using Natural Language Processing and Deep Learning techniques. The classified products based on attributes will be used as filters on search results page to enhance and improve search mechanism of website. We have classified 95% apparel products based on material attribute and achieved 87% test accuracy on neck/collar attribute classification. The report is divided into four main parts which covers: Introduction, DataSet Preparation, Methodology and the Experimentation. Lastly, other similar work performed during internship along with the future work is discussed.
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    Clickbait detection using deep learning Techniques
    (2020) Parikh, Apurva Ketanbhai; Majumder, Prasenjit
    With the growing shift towards news consumption primarily through social media sites like Twitter, Facebook etc., most of the news agencies are prompting their stories on social media platform. These news agencies are publishing fake news on social media to generate revenue by enticing users to click on their articles. To increase the number of readers agencies use eye-catchy headlines accompanied with article link, which attract the reader to read the article. These attractive headlines are called Clickbaits. Usually, clickbait article does not meet the expectation of the user. In this work we try to develop an end-to-end clickbait detection system using Transformer based model Bidirectional Encoder Representations from Transformers (BERT). We also found few clickbait specific features which we hypothesised can be utilised along with BERT model to develop a better classifier. Our proposed approach using BERT significantly outperformed baseline paper which utilised BiLSTM.
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    Augmenting dialogue generation using dialogue act embeddings: a transfer learning approach
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2020) Bisht, Abhimanyu Singh; Majumder, Prasenjit
    The following work looks at contemporary end-to-end dialogue systems with the aim of improving dialogue generation in an open-domain setting. It provides an overview of popular literature in the domain of dialogue generation, followed by a brief look at how human dialogue is understood from the perspective of Linguistics and Cognitive Science. We try to extract useful ideas from these domains of research and implement them in a transfer learning approach where a pretrained language model is supplemented with dialogue act information using special embeddings. The hypothesis behind the proposed approach is that the dialogue act information will aid the generation process. The proposed approach is then compared with a baseline approach on their performance on the DailyDialog[12] dataset using perplexity as the evaluation metric. Though the proposed approach is a significant improvement over the baseline, the contribution of the Dialogue Act Embeddings in the development is shown to be marginal via ablation analysis.