Tommaso Teofili

About Tommaso Teofili

Tommaso Teofili, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at Università degli Studi Roma Tre, specializes in the field of Natural Language Processing, Information Retrieval, Explainability.

His recent articles reflect a diverse array of research interests and contributions to the field:

Vector search with OpenAI embeddings: Lucene is all you need

Searching Dense Representations with Inverted Indexes

Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes

Generating optimized model explanations

Kelpie: an explainability framework for embedding-based link prediction models

CERTEM: explaining and debugging black-box entity resolution systems with CERTA

Controlled publication of sensitive data within an established timeframe

Explaining link prediction systems based on knowledge graph embeddings

Tommaso Teofili Information

University

Position

___

Citations(all)

100

Citations(since 2020)

98

Cited By

12

hIndex(all)

5

hIndex(since 2020)

5

i10Index(all)

3

i10Index(since 2020)

3

Email

University Profile Page

Google Scholar

Tommaso Teofili Skills & Research Interests

Natural Language Processing

Information Retrieval

Explainability

Top articles of Tommaso Teofili

Vector search with OpenAI embeddings: Lucene is all you need

2024/3/4

Searching Dense Representations with Inverted Indexes

arXiv preprint arXiv:2312.01556

2023/12/4

Jimmy Lin
Jimmy Lin

H-Index: 52

Tommaso Teofili
Tommaso Teofili

H-Index: 2

Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes

2023/10/21

Tommaso Teofili
Tommaso Teofili

H-Index: 2

Jimmy Lin
Jimmy Lin

H-Index: 52

Generating optimized model explanations

2023/6/22

Kelpie: an explainability framework for embedding-based link prediction models

Proceedings of the VLDB Endowment

2022/8/1

CERTEM: explaining and debugging black-box entity resolution systems with CERTA

Proceedings of the VLDB Endowment

2022/8/1

Controlled publication of sensitive data within an established timeframe

2022/6/14

Explaining link prediction systems based on knowledge graph embeddings

2022/6/10

Effective explanations for entity resolution models

2022/5/9

Explaining Link Prediction with Kelpie.

2022

Document replication based on distributional semantics

2022/11/1

Application of local interpretable model-agnostic explanations on decision services

2022/10/27

Organizing hierarchical data for improved data locality

2021/11/2

TrustyAI explainability toolkit

arXiv preprint arXiv:2104.12717

2021/4/26

Methods and systems for ranking search results via implicit query driven active learning

2021/4/6

See List of Professors in Tommaso Teofili University(Università degli Studi Roma Tre)

Co-Authors

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