Neural ranking models for document retrieval

Trabelsi, Mohamed; Chen, Zhiyu; Davison, Brian D.; Heflin, Jeff

doi:10.1007/s10791-021-09398-0

Cited by 21 publications

(6 citation statements)

References 142 publications

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“…Also, it uses a large amount of web knowledge to generate expanded queries, which may lead to scalability issues when applied to largescale collections or real-time scenarios. 2021 Trabelsi et al [21] "Neural ranking models for document retrieval"…”

Section: Appendixmentioning

confidence: 99%

Efficient information retrieval model: overcoming challenges in search engines-an overview

Sharma,

Panda

2023

IJEECS

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<span>Search engines play a vital role in information retrieval (IR) indexing and processing vast and diverse data, which now encompasses the ever-expanding wealth of multimedia content. However, search engine performance relies on the efficiency and effectiveness of their information retrieval systems (IRS). To enhance search engine performance, there is a need to develop more efficient and accurate IRS that retrieves relevant information quickly and accurately. To address this challenge, various approaches, including inverted indexing, query expansion, and relevance feedback, have been proposed for IR. Although these approaches have shown promising results, but their effectiveness and limitations require a comprehensive examination This research aims to investigate the challenges and opportunities in designing an efficient IRS for search engines and identify key areas for improvement and future research. The study involves a comprehensive literature review on information retrieval impacting academia, industry, healthcare, e-commerce, and other domains. Researchers rely on search engines to access relevant scientific papers, professionals use them to gather market intelligence, and consumers utilize them for product research and decision-making. The findings of this study will contribute to the development of more efficient and effective information retrieval systems, leading to improved search engine performance and user satisfaction.</span>

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Section: Appendixmentioning

confidence: 99%

Efficient information retrieval model: overcoming challenges in search engines-an overview

Sharma,

Panda

2023

IJEECS

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“…Deep contextualized language models, like BERT [16] and Ro-BERTa [25], have been recently proposed to solve multiple tasks [13,23,29,30,35,38,39,42,45,48,50]. Building on BERT, Chen et al [8] proposed a BERT-based ranking model to capture the matching signals between the query and the table fields using the sentence pair setting.…”

Section: Related Workmentioning

confidence: 99%

StruBERT: Structure-aware BERT for Table Search and Matching

Trabelsi,

Chen,

Zhang

et al. 2022

Preprint

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A large amount of information is stored in data tables. Users can search for data tables using a keyword-based query. A table is composed primarily of data values that are organized in rows and columns providing implicit structural information. A table is usually accompanied by secondary information such as the caption, page title, etc., that form the textual information. Understanding the connection between the textual and structural information is an important yet neglected aspect in table retrieval as previous methods treat each source of information independently. In addition, users can search for data tables that are similar to an existing table, and this setting can be seen as a content-based table retrieval. In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table. StruBERT features are integrated in a new end-to-end neural ranking model to solve three table-related downstream tasks: keyword-and content-based table retrieval, and table similarity. We evaluate our approach using three datasets, and we demonstrate substantial improvements in terms of retrieval and classification metrics over state-of-the-art methods. CCS CONCEPTS• Information systems → Retrieval models and ranking; Structured text search.

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“…This task naturally invites neural information retrieval. 1 Large Language Models (LLM) have been demonstrated to understand language and can extract core concepts in text irrespective of the writing style. Furthermore, Graph Neural Networks (GNN) can encode relationships between data, making it a beneficial backend for recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

Intelligent knowledge base search tool using large language model and graph neural network

Payumo,

Subramanian,

et al. 2024

Pattern Recognition and Tracking XXXV

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Within many organizations, a vast number of communications, memos, reports and documents have been accumulated in internal servers. Efficiently discovering relevant entries can reduce time spent addressing organizational needs such as personnel skills matching or anomaly resolution. However, per organization, information retrieval on these disparate data types can be challenging, as systems must be designed for their domain while accounting for unstructured and inconsistent datasets. Traditional querying via search terms often requires relevancy tuning by subject matter experts which makes it difficult to build retrieval systems. We argue that development of retrieval systems can be simplified and enhanced by embedding data with Large Language Models (LLMs), organizing information in a Knowledge Graph (KG) structure, and further encoding their relational features through a Graph Neural Network (GNN). One of the major challenges of using GNNs for information retrieval is optimizing negative edge selection. Training GNNs requires a balanced ratio between positive and negative edges however the space of negative edges is exponentially larger than positive edges. In this work, we extend the LLM-GNN hybrid architecture by applying ensemble voting on a set of trained LLM-GNNs. Preliminary results have shown modest improvement on our personnel-document matching tasks. This work contributes to a developmental effort that aims to help engineers and scientists find new research opportunities, learn from past mistakes, and quickly address future needs.

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Neural ranking models for document retrieval

Cited by 21 publications

References 142 publications

Efficient information retrieval model: overcoming challenges in search engines-an overview

Efficient information retrieval model: overcoming challenges in search engines-an overview

StruBERT: Structure-aware BERT for Table Search and Matching

Intelligent knowledge base search tool using large language model and graph neural network

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