M Tech Dissertations

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

Browse

Subject: search.filters.subject.Natural language processing

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    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.
  • Thumbnail Image
    ItemOpen Access
    SMS query processing for information retrieval
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Shinghal, Khushboo; Majumder, Prasenjit
    SMS text messaging is one of the fast and popular communication mode on mobile phones these days. This study presents a query processing system for information retrieval system when queries are Short-message-Service (SMS). SMS contains various user improvisation and typographical errors. Proposed approach uses approximate string matching techniques and context extraction to normalize SMS queries with minimum linguistic resources. We have tested the system on FIRE 2011 SMS based FAQ retrieval corpus. Results seems encouraging
  • Thumbnail Image
    ItemOpen Access
    Learning to rank: using Bayesian networks
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Gupta, Parth; Mjumder, Prasenjit; Mitra, Suman K.
    Ranking is one of the key components of an Information Retrieval system. Recently supervised learning is involved for learning the ranking function and is called 'Learning to Rank' collectively. In this study we present one approach to solve this problem. We intend to test this problem in di erent stochastic environment and hence we choose to use Bayesian Networks for machine learning. This work also involves experimentation results on standard learning to rank dataset `Letor4.0'[6]. We call our approach as BayesNetRank. We compare the performance of BayesNetRank with another Support Vector Machine(SVM) based approach called RankSVM [5]. Performance analysis is also involved in the study to identify for which kind of queries, proposed system gives results on either extremes. Evaluation results are shown using two rank based evaluation metrics, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG).