Analysis on Semantic level Information Retrieval and Query Processing

EasyChair Preprint 4424

Abstract

Query processing and Information Retrieval plays important ap-
plication of Natural Language Processing (NLP) and Data Mining. It aims
to retrieve relevant documents for natural language queries. Nowadays large
amounts of unstructured data are scattered across the web. So Information

Retrieval from these large volumes of unstructured data using natural languages
become a more crucial and challenging task. The relevant Information Retrieval
from such a large amount of unstructured data needs knowledge about the
semantic information or contextual information. The semantic information re-
retrieval from unstructured data uses the methods from Data Analytics, Natural
Language Processing and Machine Learning etc. Here we propose a survey on
different models for Information Retrieval, Information Retrieval using Natural

Languages and emphasis on semantic level Information Retrieval. And also
perform the comparison and analysis of various models.

Keyphrases: Keywords Natural Language Processing · Information Retrieval · Query, Processing · Machine Learning · Deep Learning · Neural Networks · Ontology ·, Word Embedding · Document Embedding

@booklet{EasyChair:4424,
  author    = {S K Liji and Muhamed P Ilyas},
  title     = {Analysis on Semantic level Information Retrieval and Query Processing},
  howpublished = {EasyChair Preprint 4424},
  year      = {EasyChair, 2020}}