DeepAI AI Chat
Log In Sign Up

Exploring Combinations of Ontological Features and Keywords for Text Retrieval

07/20/2018
by   Tru H. Cao, et al.
0

Named entities have been considered and combined with keywords to enhance information retrieval performance. However, there is not yet a formal and complete model that takes into account entity names, classes, and identifiers together. Our work explores various adaptations of the traditional Vector Space Model that combine different ontological features with keywords, and in different ways. It shows better performance of the proposed models as compared to the keyword-based Lucene, and their advantages for both text retrieval and representation of documents and queries.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/15/2018

Semantic Search by Latent Ontological Features

Both named entities and keywords are important in defining the content o...
07/13/2020

A Feature Analysis for Multimodal News Retrieval

Content-based information retrieval is based on the information containe...
07/20/2018

A Generalized Vector Space Model for Ontology-Based Information Retrieval

Named entities (NE) are objects that are referred to by names such as pe...
09/14/2017

T^2K^2: The Twitter Top-K Keywords Benchmark

Information retrieval from textual data focuses on the construction of v...
04/22/2023

(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis

As ecommerce continues growing, huge investments in ML and NLP for Infor...
03/06/2022

Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents

Text semantic matching is a fundamental task that has been widely used i...
08/12/2021

TextBenDS: a generic Textual data Benchmark for Distributed Systems

Extracting top-k keywords and documents using weighting schemes are popu...