INFORMATION RETRIEVAL USING NEURAL NETWORKS VINEETH REDDY ANUGU - PowerPoint PPT Presentation

Oct 27, 2023 •308 likes •459 views

INFORMATION RETRIEVAL USING NEURAL NETWORKS VINEETH REDDY ANUGU CMSC 676 INFORMATION RETRIEVAL INTRODUCTION How can we use neural networks for Information Retrieval? Neural Networks is a field of machine learning How can we train a

INFORMATION RETRIEVAL USING NEURAL NETWORKS VINEETH REDDY ANUGU CMSC 676 INFORMATION RETRIEVAL
INTRODUCTION Ø How can we use neural networks for Information Retrieval? Ø Neural Networks is a field of machine learning Ø How can we train a machine learning model and use that for specific application of information retrieval? Ø This process is highly data demanding
WHAT ARE NEURAL NETWORKS (CNN)? Ø Neural networks learn from raw data Ø Supervised and Unsupervised learning Ø CNNs are set of filters that extract patterns in data Ø Used for image recognition, can be used on textual data Ø CNNs are feed-forward networks
RELATED WORK
THE DEEP RELEVANCE MATCHING MODEL(DRMM) Ø Used for ad-hoc retrieval by relevance matching Ø Convolutional Neural Network is used in this model Ø DRMM is an interaction-based model Ø Interaction-based models looks for interaction between queries and terms Ø Requires Matching Histogram Mapping as input Ø Involves a term gating network
DRMM ARCHITECTURE
WORD2VEC MODEL Ø Used to learn embeddings of given data Ø Embeddings are vector representations of data Ø Useful in recommendation systems, embeddings are drawn from user data Ø Two models Ø Skip-gram Model Ø Continuous bag-of-words Model(CBOW)
COMPARE AND CONTRAST
COMPARING DRMM MODEL Ø Two datasets, Robust04 and ClueWeb-09-Cat-B, are used. Ø Comparing DRMM with well established IR Models
COMPARISON ON ROBUST-04
COMPARISON ON CLUEWEB-09-CAT-B
COMPARING WORD2VEC MODEL Ø Comparing the Word2Vec model with the FastText model Ø FastText obtains n-grams instead of embeddings Ø Useful for rare and out-of-vocabulary word embeddings Ø Uses differ based on the task
THANK YOU

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