Andrea Pierleoni

opentargets/json_schema: Schema for Platform release 26.03

Irene López Santiago, Dániel Süveges, Gautier Koscielny, Adam S. Faulconbridge et autres

What's Changed [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in https://github.com/opentargets/json_schema/pull/235 Update regex pattern for diseaseFromSourceMappedId by @DSuveges in https://github.com/opentargets/json_schema/pull/237 chore(eva): add evidence only to clinical significances enum by @ireneisdoomed in https://github.com/opentargets/json_schema/pull/238 [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in https://github.com/opentargets/json_schema/pull/236 chore: update regex for phenotypeFromSourceId …

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HALLUCANA: Fixing LLM Hallucination with A Canary Lookahead

Tianyi Li, Erenay Dayanık, Shubhi Tyagi, Andrea Pierleoni

In this paper, we present HALLUCANA, a canary lookahead to detect and correct factuality hallucinations of Large Language Models (LLMs) in long-form generation.HALLUCANA detects and intervenes as soon as traces of hallucination emerge, during and even before generation.To support timely detection, we …

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HalluCana: Fixing LLM Hallucination with A Canary Lookahead

Tianyi Li, Erenay Dayanık, Shubhi Tyagi, Andrea Pierleoni

In this paper, we present HalluCana, a canary lookahead to detect and correct factuality hallucinations of Large Language Models (LLMs) in long-form generation. HalluCana detects and intervenes as soon as traces of hallucination emerge, during and even before generation. To support timely …

REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking

Nacime Bouziani, Shubhi Tyagi, Joseph Arnold Fisher, Jens Lehmann et autres

Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error …

REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking

Nacime Bouziani, Shubhi Tyagi, Joseph Arnold Fisher, Jens Lehmann et autres

Nacime Bouziani, Shubhi Tyagi, Joseph Fisher, Jens Lehmann, Andrea Pierleoni. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track). 2024.

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WebIE: Faithful and Robust Information Extraction on the Web

Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos et autres

Extracting structured and grounded fact triples from raw text is a fundamental task in Information Extraction (IE). Existing IE datasets are typically collected from Wikipedia articles, using hyperlinks to link entities to the Wikidata knowledge base. However, models trained only on Wikipedia …

WebIE: Faithful and Robust Information Extraction on the Web

Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos et autres

Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos, Andrea Pierleoni. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

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Improving Entity Disambiguation by Reasoning over a Knowledge Base

Tom Ayoola, Joseph Arnold Fisher, Andrea Pierleoni

Recent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types. This limits the range of contexts in which entities can be disambiguated. …

ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking

Tom Ayoola, Shubhi Tyagi, Joseph Arnold Fisher, Christos Christodoulopoulos et autres

We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass, …

Improving Entity Disambiguation by Reasoning over a Knowledge Base

Tom Ayoola, Joseph Arnold Fisher, Andrea Pierleoni

Recent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types.This limits the range of contexts in which entities can be disambiguated.To allow …

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ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking

Tom Ayoola, Shubhi Tyagi, Joseph Arnold Fisher, Christos Christodoulopoulos et autres

Tom Ayoola, Shubhi Tyagi, Joseph Fisher, Christos Christodoulopoulos, Andrea Pierleoni. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track. 2022.

Open Targets Platform: supporting systematic drug–target identification and prioritisation

David Ochoa, Andrew Hercules, Miguel Carmona, Dániel Süveges et autres

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last …

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