Term weighting is a process of scoring and ranking a term's relevance to a user's information
need or the importance of a term to a document. This thesis aims to investigate novel term …

The information retrieval community has observed significant performance improvements
over various tasks due to the introduction of neural architectures. However, such …

This paper addresses a ranking model which uses the content of the documents along with
their link structures to obtain an efficient ranking scheme. The proposed model combines the …

This paper is concerned with supervised rank aggregation, which aims to improve the
ranking performance by combining the outputs from multiple rankers. However, there are …

Most of information retrieval effectiveness evaluation metrics assume that systems
appending irrelevant documents at the bottom of the ranking are as effective as (or not …

Ranking plays an important role in information retrieval, aiming to sort the documents
retrieved for a given query in the descending order of relevance. Recently, many …

Most of the Information Retrieval techniques are based on representing the documents
using the traditional vector space model ie bag-of-words model. In this paper, associations …

In this thesis, we present a query expansion approach based on contextualized word
embeddings for improving document ranking performance. We employ Bidirectional …

Term weighting schemes are central to the study of information retrieval systems. This article
proposes a novel TF-IDF term weighting scheme that employs two different within document …

One of the most important research topics in Information Retrieval is term weighting for
document ranking and retrieval, such as TFIDF, BM25, etc. We propose a term weighting …